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2025

  • 3 min read

Understanding MCP (Model Context Protocol) - What It Is, How It Works, and Why It Matters

In the rapidly evolving world of AI and large language models (LLMs), context management is becoming a cornerstone for building smarter, more efficient, and reliable applications. Enter MCP (Model Context Protocol) — a protocol designed to help developers and systems define, organize, and control the context passed into AI models. In this post, we’ll explore what MCP is, how it works, and real-world use cases that demonstrate its value.

🔍 What is MCP (Model Context Protocol)?

MCP, or Model Context Protocol, is an open protocol for specifying and managing the context that gets sent to language models. Context, in this sense, refers to the structured information provided to an LLM during inference—everything from user inputs and chat history to documents, tools, and system instructions.

MCP is part of a broader movement to standardize how AI applications interact with LLMs, making it easier to build reproducible, debuggable, and modular AI workflows.

Think of MCP as the equivalent of an API contract, but for LLM context. It tells the model what to expect, how to behave, and what tools it has access to—all in a consistent, declarative format.

⚙️ How MCP Works

MCP operates through a YAML-based declarative configuration, where the developer defines a context schema that the model runtime interprets. This configuration can include:

  • System instructions: Base directives that set the model’s behavior (e.g., “You are a helpful assistant.”).
  • Memory objects: Previous messages or facts the model should remember.
  • Tools and functions: Descriptions of callable tools available to the model (like a calculator, API, or database).
  • User inputs: Current prompts or queries from users.
  • Artifacts: Structured data, like documents or JSON blobs, that the model should reference.

Each item in the MCP context is versioned and traceable, enabling better observability and debugging during LLM usage.

An example MCP file might look like:

version: "1.0"
system: "You are a coding assistant."
inputs:
  user_message: "Write a Python function that merges two dictionaries."
memory:
  - type: chat_history
    content:
      - role: user
        content: "Hi, can you help me with Python?"
      - role: assistant
        content: "Sure! What do you need?"
tools:
  - name: code_linter
    description: "Lint and format Python code."

🧠 Why Use MCP?

As applications become more complex, prompt engineering becomes less scalable. Developers need:

  • Modularity: Reuse context components like tools or user profiles across sessions.
  • Auditability: Track what was sent to the model and why it responded the way it did.
  • Interoperability: Use a shared context format across multiple model vendors or frameworks.
  • Observability: Inspect and debug the actual context used during inference.

MCP helps solve these problems by separating context building from business logic, and promoting reproducibility.

✅ Example Use Cases

1. Agent-Based Applications

MCP enables advanced agents (like LangChain or Autogen) to clearly define toolkits, memory, and goals. You can declaratively list the tools available to the agent and pass them as structured context to the model runtime.

2. Enterprise Chatbots

In customer service, MCP can define rules, FAQs, and access control logic for LLMs. By declaring memory, user roles, and pre-approved documents, you ensure compliance and consistency.

3. Coding Assistants

By declaring available functions (e.g., a code executor or documentation fetcher), coding assistants can use tools intelligently without hardcoding them into the prompt.

4. Observability and Debugging

When models hallucinate or fail, MCP context logs provide full transparency into what information was available to the model—helping teams debug issues faster.

🚀 Final Thoughts

MCP is still evolving, but it’s already proving essential for production-grade LLM applications. By abstracting and standardizing model context, it empowers teams to build smarter, safer, and more maintainable AI systems.

Whether you're building a chatbot, an agentic system, or a retrieval-augmented generation (RAG) pipeline, embracing MCP can offer a cleaner, more scalable path forward.

  • in zh
  • 2 min read

認識 MCP（模型上下文協議）- 什麼是 MCP、如何運作，以及它的重要性

在快速發展的人工智慧（AI）與大型語言模型（LLM）領域中，上下文管理成為打造更聰明、更高效、更可靠應用程式的關鍵。這時候，**MCP（Model Context Protocol，模型上下文協議）**登場，它是一項協助開發人員與系統定義、組織並控制傳遞至 AI 模型之上下文的協議。本文將探討 MCP 是什麼、如何運作，並透過實際案例來說明其價值。

🔍 MCP（模型上下文協議）是什麼？

MCP 是一種 開放協議（open protocol），用來定義與管理提供給語言模型的「上下文」（context）。所謂上下文，指的是推論時傳遞給 LLM 的結構化資訊，涵蓋使用者輸入、聊天紀錄、文件、工具以及系統指令等。

你可以將 MCP 想像成 API 契約的語境版本：它讓模型知道該期待什麼、有何工具可用、應如何回應，而且這一切都是以一致、宣告式的格式來進行。

⚙️ MCP 如何運作？

MCP 透過 YAML 格式的宣告式設定檔運作，開發人員可在此定義模型運行時所需的上下文結構，內容包括：

  • 系統指令（System Instructions）：定義模型的基本行為（例如：「你是一位有幫助的助理」）。
  • 記憶物件（Memory Objects）：模型應該記住的對話或事實。
  • 工具與函數（Tools and Functions）：模型可調用的功能描述（例如計算機、API 或資料庫）。
  • 使用者輸入（User Inputs）：目前使用者輸入的提示或問題。
  • 人工製品（Artifacts）：模型需要參考的結構化資料（如文件或 JSON 檔）。

MCP 的每一個部分都具備版本控制與可追蹤性，有助於提升 **可觀測性（observability）**與除錯能力。

範例 MCP 設定如下：

version: "1.0"
system: "You are a coding assistant."
inputs:
  user_message: "Write a Python function that merges two dictionaries."
memory:
  - type: chat_history
    content:
      - role: user
        content: "Hi, can you help me with Python?"
      - role: assistant
        content: "Sure! What do you need?"
tools:
  - name: code_linter
    description: "Lint and format Python code."

🧠 為何要使用 MCP？

隨著 AI 應用的複雜性增加，純粹依賴提示工程（prompt engineering）已不具可擴展性。開發者面臨以下挑戰：

  • 模組化（Modularity）：可在不同會話中重複使用使用者資料、工具設定等上下文元件。
  • 可稽核性（Auditability）：追蹤模型接收到的上下文，以及模型如何生成回應。
  • 互通性（Interoperability）：跨多個模型供應商或框架使用共通的上下文格式。
  • 可觀測性（Observability）：方便檢查與除錯模型實際運行時使用的上下文。

MCP 的出現正好解決這些問題，將上下文建構與業務邏輯分離，提升 AI 系統的可維護性與擴展性。

✅ 使用範例

1. 代理型應用（Agent-Based Applications）

MCP 可清楚定義智能代理可用的工具、記憶、目標等資源，支援如 LangChain 或 AutoGen 這類框架的先進代理架構。

2. 企業級聊天機器人

客服應用中，可透過 MCP 宣告規則、FAQ、使用者角色與授權資訊，確保一致性與合規。

3. 程式碼助理

開發工具助理可利用 MCP 宣告可用的函數（如程式執行器、說明文件查詢器），使模型能智慧地選擇並調用工具。

4. 觀測與除錯

當模型產生錯誤回應或幻覺時，MCP 的上下文紀錄能提供完整資訊，有助於快速找出問題根源。

🚀 結語

雖然 MCP 尚在發展中，但它已成為建構生產級 LLM 應用的核心組件之一。透過標準化與模組化上下文管理，MCP 協助開發者打造更聰明、更安全、更易維護的 AI 系統。

無論你是在開發聊天機器人、智能代理，或是 RAG（檢索增強生成）應用，採用 MCP 都將成為未來的主流方式。

  • 76 min read

China’s core banking system market is undergoing rapid modernization, driven by the need for internet-scale performance (to support platforms like WeChat Pay and Alipay) and digital transformation across banks. Domestic Chinese vendors have risen to prominence with next-generation core platforms that emphasize high performance, modern architectures, flexibility, and integration capabilities. Below, we detail key Chinese core banking vendors serving large commercial banks and fintech platforms, including their technology, architecture, integration flexibility, configurability, business capabilities, competitive advantages, and company profiles. We also identify system integrators (SIs) active in this market and discuss how vendors and SIs collaborate. Finally, we assess overall trends, especially the shift from legacy mainframe cores to cloud-native platforms in China, in the context of AI integration and digital transformation.

Sunline (Shenzhen Sunline Tech)

Overview: Sunline is a leading Chinese fintech software provider (founded 2002) known for its core banking system innovations (A Decade of Success: Sunline Expands Partnership with a Major Chinese Joint-Stock Bank by Winning a Core Banking System Project). It was the first in China to develop a Java-based core banking system, breaking from the COBOL/mainframe tradition around 2010 (Sunline's Observation: 10-year History of Core Changes in China's Banking Industry). Sunline’s solutions today are cloud-native, AI-driven, and widely adopted by banks embarking on digital transformation (A Decade of Success: Sunline Expands Partnership with a Major Chinese Joint-Stock Bank by Winning a Core Banking System Project). Notable clients include WeBank (China’s first digital-only bank), Ping An Bank, Bank of Nanjing, Bank of Dongguan, and many regional banks, where Sunline’s core has often been a centerpiece of modernization efforts (Sunline's Observation: 10-year History of Core Changes in China's Banking Industry) (Sunline's Observation: 10-year History of Core Changes in China's Banking Industry).

Yusys Technologies (Beijing Yusys / Yucheng Tech)

Overview: Yusys Technologies (est. 1999) is a top domestic banking IT provider, often regarded as an industry leader in China’s banking software market (共推金融业数字化进程：宇信科技与华为的合作与蜕变-华为) (共推金融业数字化进程：宇信科技与华为的合作与蜕变-华为). Yusys offers a comprehensive suite of banking solutions, and its core banking system is a flagship product used by many Chinese banks. The company has a vast customer base that spans the central bank, all three policy banks, all six state-owned commercial banks, 12 joint-stock banks, 180+ city commercial banks, 200+ rural banks/credit unions, and 50+ foreign banks in China (across its product lines) (背靠百度，宇信科技难言增长 - 妙投). While not every one of those uses Yusys for core systems, this reach illustrates Yusys’s strong market penetration and trust. Yusys’s core banking solution is a new-generation, fully distributed system built on its unified development platform, emphasizing advanced design, flexibility, and broad functionality (Core Banking). The company is known for its deep domain experience (25+ years) and has been listed on IDC’s FinTech rankings globally (共推金融业数字化进程：宇信科技与华为的合作与蜕变-华为).

  • High Performance & Scalability: Yusys’s core banking platform is engineered to handle high transaction volumes and rapid growth. It “fully supports distributed & micro-service architecture”, meaning it can run on clusters of commodity servers and scale out horizontally as load increases (Core Banking). Built in Java, it leverages modern middleware and in-memory caching to ensure throughput under heavy loads. Yusys has demonstrated performance in projects like replacing legacy cores in regional banks where high concurrency for online channels was required. Additionally, Yusys collaborates with technology partners to enhance performance – for example, it has a joint solution with PingCAP’s TiDB (a distributed NewSQL database) to provide strong consistency and HTAP (hybrid transaction/analytical) capabilities for core banking data (神州信息新一代分布式银行核心系统 | PingCAP 平凯星辰) (神州信息新一代分布式银行核心系统 | PingCAP 平凯星辰). By using distributed databases and middleware, Yusys cores can meet massive data and concurrency demands. The system is also proven on domestic cloud infrastructure; Yusys and Huawei jointly showcased core banking on Huawei’s Kunpeng servers and GaussDB database, indicating the core can scale on purely Chinese tech stacks with high performance (共推金融业数字化进程：宇信科技与华为的合作与蜕变-华为) (共推金融业数字化进程：宇信科技与华为的合作与蜕变-华为). This positions Yusys well for large banks that must support millions of users (including heavy mobile payments traffic).

  • Modern Architecture: Yusys’s core is a cloud-ready, microservice-based system. It is developed in Java and adheres to a modular SOA design, with independent business components for key functions. According to Yusys, the solution is built on a Unified Development Platform, following leading technical architecture principles (Core Banking). It supports distributed deployment, microservices, and domestic databases natively (Core Banking). In practice, the architecture includes a Business Middle Platform concept: Yusys’s Internet Finance Core is constructed on top of the distributed core, with functional centers (Customer Center, Product Center, Marketing Center, Limit/Quota Center, Account Center, Contract Center, Transaction Center, Payment Center, Accounting Center, Error handling Center, Internal Control Center) (Core Banking). These correspond to microservices or modules that encapsulate specific domains, which can be reused and extended. The core integrates with a Data Middle Platform to leverage data for AI and risk control in real time (Core Banking). This two-layer architecture (business middle platform + data platform) is very modern and enables open banking and analytics. Yusys fully supports deployment on private cloud or hybrid cloud, and it’s not tied to any proprietary hardware – it can run on mainstream Linux servers and various databases (Oracle, MySQL, as well as China’s OceanBase or TiDB, etc.). The system’s openness and adherence to “leading technical architecture” ensure longevity and easier integration of emerging tech (like containers and service mesh for microservices, though not explicitly cited).

  • Integration with External Systems: Yusys core banking is designed to be API-driven and easily integrated. It provides hundreds of services accessible via standard interfaces (e.g., RESTful APIs, message queues) that allow external channels and fintech apps to connect. The middle-platform approach means that for each domain (customer, account, etc.), there are well-defined services – facilitating integration with mobile banking, WeChat mini-apps, Alipay, etc. In fact, Yusys has a strong background in online banking and channels – it first made its name developing internet banking for China Construction Bank (背靠百度，宇信科技难言增长 - 妙投). This heritage means its core banking solution was likely built with omnichannel integration in mind from the start. Yusys also provides an Enterprise Service Bus and unified communication platform as part of its technology stack (Core Banking) (Core Banking), which can mediate between the core and external systems. Additionally, Yusys often co-develops solutions with partners; for example, it worked with Ant Group (Ant Financial) and Huawei to ensure interoperability with Ant’s distributed database (OceanBase) and Huawei’s tech for joint clients (共推金融业数字化进程：宇信科技与华为的合作与蜕变-华为) (共推金融业数字化进程：宇信科技与华为的合作与蜕变-华为). Therefore, a bank using Yusys core can expect smooth integration with payment platforms, credit bureaus, regulatory systems, and AI services. The core supports open APIs and real-time data sharing, which is crucial for embedding banking services in ecosystems (WeChat/Alipay require banks’ cores to respond in sub-seconds to payment requests, and Yusys’s architecture supports that level of responsiveness).

  • Product Configurability & Customization: Configurability is a highlight of Yusys’s core. It features an “intelligent parameter management” capability and a financial product factory, similar to other modern cores, allowing banks to create or modify products through configuration rather than code (神州信息新一代分布式银行核心系统 | PingCAP 平凯星辰) (this is mentioned in context of DCITS, but Yusys likely has analogous tools). Every business module’s parameters are centrally managed, enabling rapid adjustments to things like interest rates, fee rules, and product definitions. Yusys’s platform emphasizes quick launch of new products – historically, one pain point with legacy systems was 3-6 month product rollouts, which Yusys aims to shorten dramatically. In addition to parameters, Yusys supports component reusability: its core shares common services (e.g., customer info, accounts) across retail, corporate, and other lines, so new offerings can be assembled from existing components. Customization is supported through extension points and an extensive rule engine (Yusys provides a rule engine tool as part of its platform (Core Banking)). This lets banks insert custom business logic or compliance rules without altering core code. Overall, banks using Yusys can expect a highly flexible product configuration process, from designing new loan products to tailoring workflows, all within the system’s parameterization and rule frameworks. This flexibility is one reason Yusys has remained a preferred IT partner as banks innovate in consumer finance, supply chain finance, etc., areas where fast customization is needed (背靠百度，宇信科技难言增长 - 妙投) (背靠百度，宇信科技难言增长 - 妙投).

  • Business Functionalities: Yusys delivers comprehensive banking functionality covering retail, corporate, and digital banking needs. Its core system supports standard core banking modules: deposits (current, savings, time deposits), loans (consumer, SME, corporate loans), payments/transfer, general ledger, and ancillary services like collateral management, limits management, and risk controls. Yusys often implements an entire suite – front to back. For example, it offers solutions for credit (loan origination, credit approval) that tie into the core, data analytics platforms, omnichannel front-ends (online banking, mobile banking, WeChat banking) (核心系统-中国银行业IT解决方案市场中领军企业之一) (核心系统-中国银行业IT解决方案市场中领军企业之一), and regtech (regulatory reporting). In the core itself, Yusys’s Internet Finance Core (targeted for fintech scenarios) includes customer center, product center, account center, contract center, transaction center, payment center, accounting center, etc., essentially covering all core banking processes (Core Banking). It also has a Marketing center for campaigns and a Quota/Limit center for credit limits (Core Banking) – indicating built-in CRM and risk features. This breadth means banks can run a wide range of financial products on a single integrated platform. Yusys’s core is also capable of supporting innovative businesses: community finance, online lending platforms, rural finance portals, etc., are mentioned as use cases (核心系统-中国银行业IT解决方案市场中领军企业之一). Importantly, the core is tightly integrated with Yusys’s data platform to support AI-driven functions like intelligent customer identification, risk scoring, and personalized marketing in real time (核心系统-中国银行业IT解决方案市场中领军企业之一). Thus, beyond traditional banking ledger tasks, Yusys core can be seen as an enabler for smart banking – combining transactional and analytical capabilities.

  • Competitive Advantages: Yusys’s competitive strengths include its all-round solution portfolio, extensive experience, and strong ecosystem ties. It has been in banking IT for over two decades, accumulating domain knowledge and a stable of mission-critical systems at big banks. Yusys is often called a “leader in multiple banking IT segments”, with top market share in areas like credit management and online banking (背靠百度，宇信科技难言增长 - 妙投). This cross-domain presence allows Yusys to offer banks an integrated approach (core + lending + channels + regtech all from one provider), which can reduce integration cost and risk. Another advantage is Yusys’s alignment with China’s tech ecosystem: it deeply collaborates with domestic tech giants – for instance, working with Huawei (Diamond partner) on database, server, OS optimization, with Ant Group on financial cloud and database, and with Baidu (which invested in Yusys in 2020) to incorporate AI like large language models (背靠百度，宇信科技难言增长 - 妙投) (背靠百度，宇信科技难言增长 - 妙投). This means Yusys is at the forefront of bringing technologies like AI, big data, blockchain, and Xinchuang (trusted domestic IT) into banking. Few competitors can claim such broad partnerships. Additionally, Yusys’s sheer client base acts as a testament and a network effect – new customers find comfort that Yusys solutions are already proven in banks of all sizes, including tier-1 banks and foreign banks in China. Yusys also has an expanding international footprint (subsidiaries in Hong Kong, Singapore, Indonesia) (共推金融业数字化进程：宇信科技与华为的合作与蜕变-华为), and it recently won a core banking project for a multinational bank’s Hong Kong and international branches (背靠百度，宇信科技难言增长 - 妙投), showcasing competitiveness against global vendors. Finally, the company’s scale (publicly listed, thousands of employees) and financial stability make it a low-risk choice for large banks compared to smaller startups. Its ability to continuously innovate (e.g., integrating AI anti-fraud “firewalls” with BlackEye Tech (背靠百度，宇信科技难言增长 - 妙投)) while maintaining legacy expertise gives it a balanced advantage.

  • Technology Stack & Architecture: Yusys’s core uses a Java EE technology stack, leveraging microservice frameworks (likely Spring Cloud or similar) and containerization for deployment. It runs on standard operating systems (Linux-based) and supports both SQL and NewSQL databases. Yusys explicitly supports domestic databases (to comply with localization): the system has been run with Huawei GaussDB, Ant’s OceanBase, and PingCAP’s TiDB as evidenced by joint solutions (共推金融业数字化进程：宇信科技与华为的合作与蜕变-华为) (神州信息新一代银行核心系统联合解决方案 - OceanBase). The architecture is cloud-native to a large extent – supporting dockerized deployment, elastic scaling of computing resources, and possibly Kubernetes orchestration (Yusys’s site references “Unified Microservice Platform” and cloud resource management tools, implying cloud-native design) (Core Banking) (Core Banking). For integration, a service management and control platform (likely an API gateway/ESB) is part of the stack (Core Banking). Yusys also provides development tools like a workflow engine and rule engine (Core Banking) to facilitate custom logic. On the frontend, Yusys has frameworks for unified mobile and web banking which can plug into the core easily. In summary, the stack is that of a modern enterprise application: microservices + middle platform + distributed data + DevOps toolchain. Yusys’s long history means it also knows how to interface with legacy systems; it likely has connectors for mainframe or older Unix systems to aid migration.

  • Migration Approach: Yusys typically approaches core banking replacement by gradual module implementation or parallel run. Given the complexity (200+ subsystems in a big bank), Yusys often advocates picking a domain (say, retail lending) to go first on the new core, then phasing others. It provides migration tools to map and convert data from legacy core databases to the new schema. Yusys’s core being parameter-driven helps mirror existing products on the new system to ensure functional equivalence. In some cases, Yusys might run a shadow core in parallel with the old one, reconcile outputs, and then switch over when stable. The company’s broad experience (1000+ financial institutions served (共推金融业数字化进程：宇信科技与华为的合作与蜕变-华为)) means it has encountered many legacy environments. For instance, Yusys has been involved in legacy mainframe-to-open migrations at state banks. It likely uses a combination of automated data migration, rigorous testing (unit, integration, parallel comparison), and training. Yusys also established joint labs with partners for tricky migrations, such as a “TD (Teradata) to GaussDB” migration at a joint-stock bank with Huawei (共推金融业数字化进程：宇信科技与华为的合作与蜕变-华为) – these joint efforts smooth out technical conversion issues in advance. Its migration philosophy emphasizes risk control: run old and new in tandem until the new core is proven stable, then decommission the old. With so many reference projects, Yusys has templates for migration plans which reduces execution risk.

  • Risk Management & Support: Yusys provides strong support and risk management for core banking projects. Being a large firm, it usually embeds teams on-site for project duration, covering project management, technical support, and even business consulting. Yusys has a “Financial Innovation and Operation” service division that helps banks with operational support post-go-live (共推金融业数字化进程：宇信科技与华为的合作与蜕变-华为). The core system itself has built-in risk controls: e.g., an internal control center, and integration with Yusys’s risk management systems (for credit risk, operational risk) (核心系统-中国银行业IT解决方案市场中领军企业之一). This means compliance and risk checks are part of processes (loans won’t disburse without passing internal risk rules, etc.). Yusys’s partnership with Baidu’s AI (Wenxin model) could enhance risk management via AI-driven anomaly detection or intelligent customer service, though that is emerging. From a delivery standpoint, Yusys has a long track record with relatively few public failures, indicating effective project risk mitigation. It is certified in CMMI5 (common for major vendors) and likely ISO standards for service management. To manage technical risk, Yusys not only tests in labs but often first deploys new tech in smaller banks (acting as pilots) before scaling up to bigger banks – this incremental approach has been observed in how distributed databases were first tried in a rural bank scenario together with Yusys (共推金融业数字化进程：宇信科技与华为的合作与蜕变-华为) (共推金融业数字化进程：宇信科技与华为的合作与蜕变-华为). In terms of ongoing support, Yusys offers 24/7 technical support and periodic health checks. Because it serves so many banks, it can also rapidly apply patches or regulatory updates across clients, reducing risk of non-compliance for its users.

  • Notable References & Case Studies: Yusys’s client list is exhaustive. Notably, it has been a key IT solutions provider to China Construction Bank (CCB) (one of the big four) since early on, initially in online banking and later in other systems (背靠百度，宇信科技难言增长 - 妙投). Yusys has delivered core banking or critical modules to policy banks (like Agricultural Development Bank) and many joint-stock banks. In 2022-2023, Yusys won a contract to implement a new-generation core for a multinational bank’s Hong Kong and overseas branches, beating international competitors (背靠百度，宇信科技难言增长 - 妙投) – a strong endorsement of its product quality. Domestically, Yusys has helped urban commercial banks (e.g., Bank of Beijing, Bank of Shanghai) and rural commercial banks modernize cores (often in partnership with Huawei’s infrastructure) (共推金融业数字化进程：宇信科技与华为的合作与蜕变-华为). A recent example is its joint solution on Huawei’s Kunpeng architecture for Hangzhou Bank, aimed at building a new distributed core system (共推金融业数字化进程：宇信科技与华为的合作与蜕变-华为). Yusys is also active in smart rural finance projects, enabling rural banks to offer digital banking via its core plus online banking packages. Another case: Yusys worked with Ant Financial’s tech to deliver a core system on OceanBase for a regional bank, one of the first of its kind, demonstrating its adaptability to different platforms (神州信息新一代银行核心系统联合解决方案 - OceanBase). Furthermore, Yusys’s dominance in credit systems (12.41% market share in loan management platforms in 2022) (背靠百度，宇信科技难言增长 - 妙投) means many banks’ lending operations (post-loan accounting etc.) run through Yusys software, often integrated into the core. This gives it a foot in the door to eventually replace full core systems. While specific bank names for core deployments are not always public, Yusys’s breadth of partial and full deployments and its involvement in essentially every major Chinese bank in some capacity speaks to its credibility as a core vendor.

DCITS (Digital China Information Service Co., a.k.a. “Shenzhou Info”)

Overview: DCITS is a veteran player in China’s core banking scene – having provided core banking technology for over 30 years. It is often dubbed the “core banking system leader” in the domestic market, reportedly holding the #1 market share in core banking solutions in China for 11 consecutive years up to 2022 (分布式核心业务系统 - 金融科技重磅产品 - 神州信息官网-成为领先的金融数字化转型合作伙伴). DCITS’s flagship core product is called Sm@rtEnsemble, a new-generation distributed core banking system built on the company’s self-developed platform (Sm@rtGalaxy) (分布式核心业务系统 - 金融科技重磅产品 - 神州信息官网-成为领先的金融数字化转型合作伙伴). DCITS has participated in over 100 bank core system projects, including major state-owned banks, joint-stock banks, city and rural banks (神州信息核心系统建设再获利好，中标全国性股份制银行). It was an early pioneer, involved in core system construction for a policy bank as far back as 2003, and has since been the “first implementer” for many banks’ core modernization (神州信息核心系统建设再获利好，中标全国性股份制银行). DCITS’s solutions emphasize robust engineering, parameterization, and independence from foreign technology. The company also actively aligns with China’s FinTech innovation and localization requirements (it’s deeply involved in the “Xinchuang” ecosystem). In sum, DCITS is a powerhouse known for reliable (if not flashy) core systems that power a large swath of Chinese banking.

  • High Performance & Scalability: The Sm@rtEnsemble core is designed to handle China-scale banking workloads. It adopts a fully distributed processing mechanism across all layers – from the service runtime platform, to data caching, to data storage (神州信息新一代分布式银行核心系统 | PingCAP 平凯星辰). This ensures that no single bottleneck (like a centralized database or mainframe CPU) limits throughput. By leveraging distributed transaction processing and data sharding, the system can process high volumes of transactions concurrently and store massive amounts of data with linear scalability. DCITS highlights that Sm@rtEnsemble achieves high scalability, availability, and flexibility, meeting banks’ needs for “high concurrency, large data volumes, sudden workload spikes, and agile response” (分布式核心业务系统 - 金融科技重磅产品 - 神州信息官网-成为领先的金融数字化转型合作伙伴). In practice, DCITS cores have been proven in some of the largest institutions. For example, China’s big banks that handle millions of transactions per day have used DCITS components (if not full cores). DCITS also supports various distributed database technologies to scale the data tier; it has demonstrated its core on PingCAP TiDB (for horizontal scaling and HTAP) (神州信息新一代分布式银行核心系统 | PingCAP 平凯星辰) and on Ant’s OceanBase (another high-performance distributed DB) (神州信息新一代银行核心系统联合解决方案 - OceanBase). The ability to use in-memory data grids and caching (part of Sm@rtGalaxy) further boosts performance for read-heavy workloads. Additionally, DCITS cores can utilize parallel processing for end-of-day batch jobs, shrinking batch window on large data sets. Importantly, by not relying on legacy mainframes, DCITS cores allow banks to scale out on cost-effective hardware, reducing the traditional performance-cost tradeoff. Numerous successful high-volume deployments attest to DCITS’s performance – e.g., Agricultural Bank of China’s overseas core, Postal Savings Bank’s new core modules, etc., were known to involve DCITS technology, coping with nationwide volume.

  • Modern (vs Legacy) Architecture: DCITS has evolved from legacy integrated architectures to a modern componentized architecture. Sm@rtEnsemble is built on open platform technology and SOA principles, with loosely coupled, independent business services (神州信息新一代银行核心系统联合解决方案 - OceanBase). It fully embraces microservices and cloud-native architecture in its latest incarnation (2023年神州信息研究报告：银行核心系统龙头厂商，全面抓住信创机遇). The system essentially features a “dual-core architecture”: many Chinese references note DCITS building a “双核” (dual core) mode, which typically means separating the ledger (accounting core) from the transaction processing core, each as independent modules that sync in real-time. This was done to improve performance and resilience (ledger updates can be decoupled from transactional flow). DCITS explicitly mentions “Lego-like freely combinable system modules”, indicating a highly modular design where services can be composed or reassembled as needed (神州信息“新一代分布式核心系统”上市，重塑银行竞争力). The Sm@rtGalaxy platform underpins this, providing common services and a unified infrastructure for all microservices. The architecture supports cloud deployment and containerization – DCITS has worked with Docker/Kubernetes environments and even released its own LightOS (Linux-based OS tuned for financial core systems) to optimize performance on open hardware (恒生电子发布操作系统LightOS，聚焦金融核心系统信创需求). A key architectural feature is no vendor lock-in at any layer: Sm@rtEnsemble is not tied to any third-party software or hardware and supports all major domestic servers, databases, and middleware (分布式核心业务系统 - 金融科技重磅产品 - 神州信息官网-成为领先的金融数字化转型合作伙伴). This means the architecture is abstracted enough to run on various UNIX/Linux flavors, middleware like WebSphere or local equivalents, and databases from Oracle to open-source or Chinese-made. Such neutrality is a deliberate design to meet banks’ “tech independence” goals. In contrast to legacy cores that were monolithic and often tied to specific high-end UNIX or mainframe systems, DCITS’s modern architecture is open, distributed, and flexible – yet engineered specifically for banking (with strong consistency and ACID transaction support over the microservices). It’s also cloud-native in the sense of enabling elastic scaling and resilient deployment (the company notes Chinese banks have successfully applied cloud-native tech to core systems for flexible resource allocation and fast response ([PDF] contents - DCITS)).

  • Integration & External Connectivity: DCITS core systems are known for their integration-friendly design, supporting extensive interoperability with external channels and subsystems. The Sm@rtEnsemble core exposes Financial Services Standard Interfaces for all core functions (神州信息新一代分布式银行核心系统 | PingCAP 平凯星辰). Through these interfaces (likely web services or APIs conforming to ISO20022 or other standards), the core’s capabilities (account opening, payments, loan processing, etc.) can be consumed by peripheral systems. DCITS, being a long-time systems house, often supplies not just the core but also integration middleware to banks. Its core can work in conjunction with DCITS’s enterprise service bus and messaging systems. The system is built to handle “hundreds of peripheral systems” smoothly – as seen in one deployment, after going live, the new core effectively supported docking requirements of hundreds of peripheral subsystems with smooth operation (Sunline's Observation: 10-year History of Core Changes in China's Banking Industry). DCITS emphasizes loose coupling, which makes integration easier: changes in one module (say, adding a new delivery channel) don’t require reworking the core. The core also supports open banking and API management features. DCITS was an early partner with API standards in China and can enable banks to expose services externally (with appropriate security). Moreover, DCITS’s core readily integrates with Chinese payment networks (UnionPay, NetsUnion) and fintech platforms. Many banks using DCITS core have connected it to WeChat Pay, Alipay, and other FinTech apps via open APIs or through an API gateway layer – given DCITS’s clients include large retail banks, such integration is mandatory and well-tested. Additionally, DCITS actively works with SIs and partners, so its core often comes with a library of adapters for common external systems (ATM switch, credit card system, etc.). In summary, integration is a forte: the core provides standard interfaces, and DCITS ensures that whatever mix of external systems a bank has (old or new), the core can interface with them – either natively or via an integration project.

  • Configurability & Customization: One of DCITS’s hallmark features is extensive parameterization. Sm@rtEnsemble is built to be heavily driven by parameters and a product factory configuration approach (神州信息新一代分布式银行核心系统 | PingCAP 平凯星辰) (分布式核心业务系统 - 金融科技重磅产品 - 神州信息官网-成为领先的金融数字化转型合作伙伴). All business modules (deposits, loans, etc.) have their rules and product definitions managed through a unified Parameter Management Platform】, and the system allows the bank to configure various financial products (with different characteristics) by assembling parameters in the Financial Product Factory (神州信息新一代分布式银行核心系统 | PingCAP 平凯星辰). This means a bank can launch new product variants (a new deposit type, a new loan offering) by cloning and tweaking parameters rather than coding. The parameterization covers interest calculation methods, fee schedules, transaction limits, GL posting rules, etc. The core’s Lego-like modularity also extends to customizing processes: banks can turn modules on/off or reconfigure process flows. DCITS also supports multi-institution and multi-ledger setup via config (useful for banks that have multiple legal entities or branches on one instance). Customization beyond parameters is achieved via DCITS’s toolkit (ModelB@nk development framework) which allows development of custom business logic as separate components that integrate with the core’s service interfaces (神州信息新一代银行核心系统联合解决方案 - OceanBase). Essentially, DCITS provides a blueprint of a bank’s core with best-practice processes, which is then tailored to the specific bank through parameter settings and selective enhancements. This approach speeds up implementation while still accommodating unique requirements. It also ensures that future upgrades are easier, as the core product remains standard and customizations are layered via parameters or add-ons. Banks have leveraged this to respond quickly to market changes – for example, during sudden interest rate liberalization or new regulatory rules, DCITS core banks could update parameters and comply rapidly, a task that would be arduous in hard-coded legacy systems. Overall, DCITS offers high configurability**, reducing reliance on vendor intervention for every change.

  • Business Functionalities: DCITS’s Sm@rtEnsemble covers full retail and corporate banking functionality. It includes modules such as Customer Information File, Core banking services (deposits of all types, loan servicing for all loan products, payments and remittances, funds transfers), General Ledger and accounting, Operations management (user management, branch operations, etc.), and Risk management modules (神州信息新一代分布式银行核心系统 | PingCAP 平凯星辰). Essentially, it’s an end-to-end core processing suite. The core supports multi-currency, multi-branch, multi-timezone operations (important for banks with overseas branches). DCITS also provides solutions for regulatory reporting, anti-money laundering, and other compliance areas that tie into the core. One distinctive capability of DCITS’s core is its dual-core (twin-engine) structure: it can maintain parallel transaction processing and accounting books, which enhances reliability for financial reporting and allows advanced features like real-time profit and loss calculation. Furthermore, DCITS core can operate in a “dual active” data center mode, supporting geo-redundancy for critical banking functions. For business innovation, DCITS has introduced features like componentized business services that can be reused across channels – for example, a “loan disbursement service” used by branch tellers, mobile app, and internet banking uniformly. This improves consistency and agility. Additionally, DCITS has expanded its product to cater to newer business lines: e.g., it supports internet finance scenarios, inclusive finance (micro-loans), and it can integrate with fintech services (like alternative credit scoring) through its open APIs. Its core is often deployed along with a DCITS Enterprise Service Bus and mid-platform that provides analytical and marketing capabilities. In summary, DCITS core is functionally rich and proven in handling all daily banking operations, with particular strength in retail banking (many city banks use it as a turnkey core) and increasing capabilities in corporate banking and wealth management via extended modules.

  • Competitive Advantages: DCITS’s primary advantage is its proven track record and market leadership in China. Being the incumbent in many core system projects, it has unparalleled experience in local requirements (regulations, payment systems, language, etc.) and a reputation for delivery success. IDC reports have placed DCITS as the market share leader in China’s banking IT solution market, especially in core banking, for many years (分布式核心业务系统 - 金融科技重磅产品 - 神州信息官网-成为领先的金融数字化转型合作伙伴). This longevity builds trust; banks know DCITS is a stable, big player (it is part of the Digital China group, with roots from Lenovo) that will be around to support them. Another advantage is technology independence and compliance with the national push for secure, controllable IT. Sm@rtEnsemble does not rely on any foreign proprietary platforms and fully supports Chinese alternatives (servers, OS, databases) (分布式核心业务系统 - 金融科技重磅产品 - 神州信息官网-成为领先的金融数字化转型合作伙伴). In an era where regulators encourage reducing foreign tech in banks, this is a major selling point. DCITS is also often the first mover in new trends for domestic banks: e.g., first to implement a core on a domestic distributed database (one case involved a rural bank using a Chinese DB, proving the concept) (Sunline's Observation: 10-year History of Core Changes in China's Banking Industry). Its willingness to innovate within China’s ecosystem – and successful case studies doing so – give it an edge for banks aiming to modernize while staying compliant. Additionally, DCITS provides a one-stop solution environment: it has subsidiaries and partners covering hardware, cloud services, and integration (for instance, its affiliate “CloudCore” (Yunhe) Network focuses on cloud and core banking services (分布式核心业务系统 - 金融科技重磅产品 - 神州信息官网-成为领先的金融数字化转型合作伙伴) (分布式核心业务系统 - 金融科技重磅产品 - 神州信息官网-成为领先的金融数字化转型合作伙伴)). This enables it to deliver comprehensive projects (from infrastructure to application) which some smaller vendors cannot. Moreover, DCITS’s methodology of heavy parameterization means faster implementation and updates – banks can adapt more quickly, which is a competitive advantage in a fast-changing market. Lastly, DCITS benefits from scale: it has a large support network and can mobilize big teams for core projects, which large banks find reassuring when undertaking multi-year core replacements.

  • Technology Stack & Architecture: DCITS’s Sm@rtEnsemble runs on a Java-based, open stack. Earlier versions (branded ModelB@nk) ran on J2EE app servers (WebLogic, etc.) with Oracle/DB2 databases on UNIX servers. The new distributed version can run on Linux clusters, uses microservices (possibly Spring Cloud or ServiceComb framework), and supports container orchestration. It uses DCITS’s own middleware (Sm@rtGalaxy) for distributed transaction coordination and data caching. Databases: the system is DB-agnostic but optimized for distributed databases in China’s “HTAP” category (TiDB, OceanBase) or traditional RDBMS if needed. It ensures strong ACID consistency across distributed transactions – likely through a combination of two-phase commit and high-performance NoSQL for certain data. The stack also likely includes Kafka or similar for event streaming between services and Redis or a homegrown cache for hot data. On top of the technical stack, DCITS provides a developer workbench for parameter configuration and minor customization (scripts, formula, etc.). For cloud, DCITS cores have been deployed on Huawei Cloud Stack and other private clouds; DCITS even built a financial PaaS with Forms Syntron and others (Fincube, see below) indicating it can operate in a cloud environment with containerized microservices (Huawei and Forms Syntron Release Distributed Open Banking Service Solution for Banks - Huawei). DCITS’s use of LightOS (its custom OS based on openEuler Linux) and involvement with Kunpeng CPUs show it fine-tunes the software to get maximum performance on Chinese hardware (恒生电子发布操作系统LightOS，聚焦金融核心系统信创需求). Security-wise, the stack adheres to Chinese financial security standards and encryption algorithms. The architecture ensures that even though components are distributed, from the bank’s perspective it’s one integrated system – DCITS likely provides an integrated monitoring and O&M console to manage the core’s microservices, making it easier for bank IT to operate the system post-implementation.

  • Migration Approach: DCITS typically replaces legacy systems via a gradual, measured approach. Given many of its projects involve replacing very old COBOL/mainframe or first-gen cores, DCITS often runs a parallel test phase where the new core is run with actual data in parallel to the old core for some cycles to compare results (a strategy proven to ensure accuracy). It leverages its parameterization to configure the new core to mimic existing product behavior exactly, which reduces gaps. DCITS also sometimes uses a “double core, double ledger” approach during migration – running both cores and slowly migrating products one by one. For example, a bank might move its savings accounts to the new core first while loans remain on the old, then move loans, etc., until the old core can be retired. DCITS provides data migration tools that can extract data from legacy systems and load into the new core’s databases, with data validation steps. The vendor has experience handling tricky areas like migrating transaction history, which often is enormous for big banks. They might segment history (only move a few years’ data, archive the rest) depending on the bank’s preference. DCITS emphasizes ensuring consistency and balance checks during migration – its core has robust reconciliation features to cross-verify balances between old and new systems for a period of time. Additionally, DCITS coordinates with SIs (if any on the project) for peripheral system cut-over – making sure that channels are rerouted to the new core at the right time. Because DCITS core can run on new hardware in parallel, banks often set up the new core environment separately while the old runs, minimizing disruption until final switch. DCITS’s long list of 100+ core clients means it has developed standard migration frameworks and best practices for different scenarios (be it a small rural bank or a giant bank). As a result, the risk of migration is mitigated by this maturity.

  • Risk Management & Support: DCITS takes a very methodical, risk-averse stance in core projects. It usually insists on comprehensive testing (functional, performance, security) in controlled environments (often establishing joint labs with the bank for this). For example, DCITS and PingCAP set up a joint lab to fine-tune the core on TiDB, ensuring stability before deploying (神州信息新一代分布式银行核心系统 | PingCAP 平凯星辰). DCITS’s core has strong fault-tolerance: thanks to distribution, if one node fails, others take over. Features like multi-active deployment improve disaster recovery (no single data center failure will take down the core) (Sunline's Observation: 10-year History of Core Changes in China's Banking Industry). For operational risk, DCITS core provides tools for monitoring and alerting so issues can be caught early. The company’s support model includes on-site support during critical cutover periods and dedicated support teams for each major client afterwards. Being a large company, DCITS has the capacity to respond quickly; it also often assigns a resident support team for a period after go-live. In terms of security, DCITS adheres to all PBoC security standards and has built-in controls (user access management, transaction limits, audit logs, etc.) as part of the core. For risk of project failure, DCITS leans on its iterative approach – they might do multiple “mock go-live” rehearsals with the bank to iron out issues. Also, DCITS’s independence from third-party tech reduces supply chain risk – the bank isn’t reliant on a foreign vendor’s support for a database or OS, as DCITS can support the full stack. This holistic control can be seen as risk mitigation in itself. Lastly, DCITS holds various certifications (likely CMMI5, ISO9001, etc.) ensuring quality processes in development and support.

  • Notable References & Case Studies: DCITS has a rich portfolio. Some highlights: In the mid-2000s, DCITS was involved in Agricultural Development Bank of China’s core banking project (policy bank) – one of the earliest modern core implementations in China (神州信息核心系统建设再获利好，中标全国性股份制银行). It subsequently implemented core solutions or key modules in several state-owned banks. For instance, DCITS had a role in Bank of China’s overseas core system and China Construction Bank’s domestic core upgrade (as an integrator/provider of certain components). More recently, China Everbright Bank and China Guangfa Bank (CGB), both joint-stock banks, have engaged DCITS for next-gen core projects. In 2019, DCITS partnered with a regional bank to launch a core banking system on a fully domestic tech stack (hardware + OS + database), which was a breakthrough demonstration of a secure and controllable core system (Sunline's Observation: 10-year History of Core Changes in China's Banking Industry). In 2023, it was reported that a “top-tier city commercial bank” chose DCITS to build its new core, making DCITS the only fintech firm with live core cases across all categories of commercial banks (神州信息中标一Top级城商行项目启动新一代核心系统建设 - 证券时报). DCITS’s Sm@rtEnsemble has been successfully deployed in banks like Bank of Jiangsu, Bank of Ningbo (leading city banks), and many rural commercial banks looking to leapfrog into digital banking. DCITS is also the core system provider for some foreign banks’ China operations, where localization is key. An example of innovation: DCITS collaborated with Huawei to implement Hangzhou Bank’s new distributed core on Kunpeng processors – this project is often cited as a model for commercial banks adopting distributed architecture with domestic tech (共推金融业数字化进程：宇信科技与华为的合作与蜕变-华为). Additionally, DCITS often references that it holds the largest market share among city and rural bank core systems, meaning dozens upon dozens of smaller banks run on its cores. Each of these is a testament to DCITS’s adaptability (these banks have varied needs). The company’s long-term relationships (some clients have stuck with DCITS through multiple generations of core upgrades) illustrate strong client satisfaction.

Forms Syntron (Shenzhen Forms Syntron)

Overview: Forms Syntron is a fintech solution provider originally known for digital banking channels, which has emerged as a notable core banking platform vendor especially in partnership with Huawei. It has roots in Hong Kong and Shenzhen, and a workforce of ~3,000. Forms Syntron’s flagship offering in core banking is the “Fincube” Digital Banking Core – an open banking platform built on distributed architecture and co-developed with Huawei’s infrastructure support (Huawei and Forms Syntron Release Distributed Open Banking Service Solution for Banks - Huawei) (Huawei and Forms Syntron Release Distributed Open Banking Service Solution for Banks - Huawei). Forms Syntron specializes in Bank 4.0-era solutions: highly agile, customer-experience-focused banking systems. Its systems are used by some innovative banks in China and Asia (including Hong Kong virtual banks). While smaller than Sunline or DCITS, Forms Syntron leverages cutting-edge tech (like cloud, AI, Web3) and strong partnerships (Huawei, Microsoft) to punch above its weight. It often co-creates solutions with banks, acting as an incubator for new digital banking models.

  • High Performance & Scalability: The Fincube platform is designed for distributed, high-concurrency operation. Built atop Huawei’s FusionCube hyper-converged infrastructure, it inherits high-density computing and storage capabilities for scaling transaction workloads (Huawei and Forms Syntron Release Distributed Open Banking Service Solution for Banks - Huawei). The solution eliminates traditional performance bottlenecks by distributing both application and data layers. For example, it uses Huawei’s FusionStorage to ensure reliable and fast distributed storage, crucial for core banking data integrity and speed (Huawei and Forms Syntron Release Distributed Open Banking Service Solution for Banks - Huawei). In terms of throughput, Fincube’s microservices architecture can scale horizontally; banks can add more FusionCube nodes to increase TPS linearly. Huawei’s testing indicated this platform can flexibly expand capacity to meet surges in mobile and internet banking demand (Huawei and Forms Syntron Release Distributed Open Banking Service Solution for Banks - Huawei). The architecture supports active-active deployments, which means load can be balanced across data centers, further boosting throughput and resilience. A key performance enabler is the “Universe Analytics Platform” Forms Syntron built – it uses FusionCube as the basic unit of distributed architecture and coordinates across them (Huawei and Forms Syntron Release Distributed Open Banking Service Solution for Banks - Huawei). This effectively is a PaaS that handles microservice management, ensuring each service can scale or failover independently without dragging down others. In summary, by marrying software and powerful hardware, the Forms Syntron core platform achieves the performance needed for real-time digital banking, as evidenced by its use in banks that operate fully online (which require very fast response even under heavy user traffic).

  • Modern Architecture: Forms Syntron’s core banking solution is fully modern and cloud-native. The Fincube platform is built with a containerized microservices framework called SolApp on the Universe PaaS (Huawei and Forms Syntron Release Distributed Open Banking Service Solution for Banks - Huawei). It consists of a large library of microservices (Forms Syntron boasts a BaaS library of thousands of microservices) that provide granular banking functions (Huawei and Forms Syntron Release Distributed Open Banking Service Solution for Banks - Huawei). This microservice approach means each business capability (e.g., account creation, loan calculation, payment processing) is a separate service that can be updated or scaled independently. The architecture is explicitly aligned with Open Banking principles: Fincube is described as a distributed open platform solution (Huawei and Forms Syntron Release Distributed Open Banking Service Solution for Banks - Huawei). It emphasizes experience-driven design, meaning the architecture was built to easily plug banking services into external customer experiences. One unique architectural concept is that it enables agile innovation – banks can quickly assemble new workflows by picking and choosing from the microservice “Lego” pieces (hence the name Fincube, implying a cube of modular components) (Huawei and Forms Syntron Release Distributed Open Banking Service Solution for Banks - Huawei). The platform leverages Huawei’s IaaS and PaaS – for instance, using Huawei’s API (CubeCenter API) to integrate hardware management into the software’s O&M, achieving more efficient automated operations (Huawei and Forms Syntron Release Distributed Open Banking Service Solution for Banks - Huawei). Essentially, it’s an integrated stack where infrastructure and application layers communicate for optimized performance and management. The architecture is also very open and secure: open in that it can incorporate third-party fintech services easily (via APIs), and secure by design (given it was likely built in compliance with stringent security frameworks and leverages Huawei’s security features). In contrast to legacy monoliths, Forms Syntron’s architecture is event-driven and stateless where possible, which improves resiliency and scalability. Moreover, it’s cloud-agnostic to an extent – while optimized for FusionCube, it can be deployed on other cloud setups (they have deployments on Azure in some international projects). Overall, the architecture is bleeding-edge, incorporating microservices, containerization, DevOps pipelines, and AI readiness (the platform is ready to integrate AI services as microservices for things like smart KYC or robo-advisors).

  • Integration & Ecosystem Connectivity: Integration is a focal point for Forms Syntron. The Fincube solution is explicitly an open banking service solution – it provides open APIs to allow banks to connect with fintech ecosystems effortlessly (Huawei and Forms Syntron Release Distributed Open Banking Service Solution for Banks - Huawei). This is critical for collaborating with platforms like WeChat and Alipay, where APIs are used to embed banking features. Forms Syntron’s core exposes all functionalities as services that can be consumed internally or externally. It also supports multichannel integration out-of-the-box: mobile banking, internet banking, branch systems, and third-party apps can all connect to the core via standardized APIs. Because it’s microservices-based, integration is simplified – external systems can call specific microservice endpoints (e.g., check account balance service) without needing to go through a complex monolithic API. Forms Syntron also built a “Lego Open Banking Solution” on top of its microservice framework (Huawei and Forms Syntron Release Distributed Open Banking Service Solution for Banks - Huawei). This likely provides pre-defined integration templates for common open banking needs. For example, if a bank wants to offer account info through a fintech app, there is a ready-made microservice and API for that. Additionally, being co-developed with Huawei, the platform likely integrates well with Huawei’s API gateway and integration middleware, which many Chinese banks use. In international projects, Forms Syntron integrated Microsoft’s AI and analytics services into its platform to create things like Banking Copilot (an AI assistant) (FORMS Syntron Presents Generative AI and Web 3.0 Financial ...), showing the ease of plugging in advanced services. The microservices design also means the platform can integrate with legacy systems via small adaptation services – e.g., a microservice that fetches data from an old system and presents it to new channels. Eliminating performance bottlenecks while integrating was a key design goal; as Huawei’s exec noted, this solution “eliminates performance bottlenecks and flexibly expands capacity to help banks quickly build digital cores and reconstruct core competitiveness in the mobile era” (Huawei and Forms Syntron Release Distributed Open Banking Service Solution for Banks - Huawei). This speaks to integration in the sense that adding new digital services (mobile era demands) won’t degrade core performance, and integration points can scale too. In summary, Forms Syntron’s core is built to connect and co-create – banks using it can easily interface with fintech partners, third-party developers (to build on the bank’s API), and even integrate non-bank services (like lifestyle apps) into their offerings, all via the robust open API framework Fincube provides.

  • Configurability & Customization: The Forms Syntron core offers extreme flexibility through microservices composition. Instead of heavy parameterization like some others, its philosophy is to allow banks to configure processes by orchestrating microservices (which is a form of configuration at a higher level). However, it likely also includes a parameter management for product settings. Since Fincube is relatively new, it was built with zero-coding configuration in mind for product design – enabling product managers to tweak attributes of a deposit or loan easily. Also, because of the microservices library (with thousands of services) (Huawei and Forms Syntron Release Distributed Open Banking Service Solution for Banks - Huawei), if a needed functionality isn’t pre-built, one can be added as a new microservice without impacting the rest of the system. This modularity is a boon for customization: banks can deploy only the services they need, customize or extend specific ones, and even develop their own microservices to plug into the platform. Forms Syntron likely provides development tools or a SDK for clients to build or modify microservices (especially since some banks co-create with them). Configurability extends to deployment and scaling – banks can configure business rules that automatically scale certain services when transaction volume increases (aided by the platform’s integration with infrastructure management via CubeCenter API (Huawei and Forms Syntron Release Distributed Open Banking Service Solution for Banks - Huawei)). In short, customizing user journeys, adding new data fields, changing a fee rule, or launching a new product can all be done by adjusting the relevant microservice or its parameters, rather than rewriting a huge codebase. This gives banks agility in creating tailored products. For instance, a bank could quickly create a new type of installment loan product by configuring the parameters in the loan service microservice and adjusting the marketing service to promote it – all within a few weeks. Forms Syntron promotes “agile innovation and operation governance” with this approach, accelerating response to market changes (Huawei and Forms Syntron Release Distributed Open Banking Service Solution for Banks - Huawei).

  • Business Functionalities: The Fincube core platform covers a broad range of banking functions, delivered as microservices. It supports retail banking services (accounts, deposits, loans, payments), small business banking, and potentially core aspects of corporate banking (though its sweet spot is retail digital banking). Each traditional module (like CASA – current and savings accounts, lending, payments, cards, etc.) is broken into microservices. For example, one might have microservices for account opening, account maintenance, interest calculation, end-of-day batch, transaction posting, funds transfer, bill payment, etc. The platform also natively includes what one might call “digital core” features: it likely has built-in support for e-KYC, digital customer onboarding, and real-time analytics embedded in transactions (since it touts an analytics platform integration). It also comes with embedded security and risk features – e.g., fraud detection microservices, anomaly alerts – crucial when integrating with open ecosystems. Forms Syntron has demonstrated capabilities in emerging tech: e.g., it developed FINNOSafe, an AI-driven risk management platform (Banking Copilot) for smart operations (FORMS Syntron Presents Generative AI and Web 3.0 Financial ...), which can complement the core by providing AI assistance in risk control and operations. We can infer that business functionalities include not just the standard banking ledger and transaction processing, but also customer experience enhancements like personalized recommendations (via AI), and multi-channel seamless experiences. For instance, if a customer starts a loan application on a mobile app, the microservice architecture allows that session to be continued in a branch or on a web portal without hiccups – all channels connect to the same set of services. Additionally, Fincube likely supports ecosystem banking functionality: enabling integration of non-bank services for customers (like if a bank wants to offer e-commerce or lifestyle products integrated with accounts, the open API framework allows it). In summary, all core transactional capabilities are present, and they are augmented by agile innovation features (like fast product launch) and AI-driven capabilities that set it apart from older cores.

  • Competitive Advantages: Forms Syntron’s competitive advantages lie in its innovation and partnerships. First, the technology itself is state-of-the-art, which appeals to banks looking to leapfrog into digital banking with a fresh start (especially internet or challenger banks). The microservices & open banking focus means it’s not burdened by legacy design – banks that choose it can more easily become platforms in the fintech ecosystem. Second, Forms Syntron has a strong strategic partnership with Huawei. Together, they bring a combined offering: Huawei provides the trusted infrastructure (servers, cloud, database) and Forms Syntron provides the banking software. This synergy is attractive, as seen when Huawei and Forms jointly released the solution to help banks build digital cores and improve competitiveness in the mobile era (Huawei and Forms Syntron Release Distributed Open Banking Service Solution for Banks - Huawei). Huawei’s endorsement lends credibility and ensures performance (since Huawei optimized FusionCube for this use). Many banks in China respect Huawei’s tech; choosing a core that’s co-developed with Huawei can ease internal buy-in, especially for IT departments mandated to use domestic tech. Third, Forms Syntron positions itself as an “incubator and enabler” for fintech innovation (FORMS HK | Your FinTech Thought Leader, Incubator and Enabler) – it co-creates with banks. This consultative, partnership approach can differentiate it from larger vendors: it’s more nimble and willing to tailor or develop new features alongside the client. Banks that want a very customized or cutting-edge solution might find this appealing. Fourth, the flexibility and speed of Fincube is a competitive edge. In an environment where product time-to-market is critical, being able to roll out updates or new services in days rather than months is a big plus. Fifth, Forms Syntron leverages AI and Web3 trends (per Microsoft collaboration and others) to offer things like generative AI assistants and blockchain integration that legacy-focused vendors might not yet provide (FORMS Syntron Presents Generative AI and Web 3.0 Financial ...). Finally, being based in Hong Kong/Shenzhen, it has cross-border experience and understands both Chinese mainland banking and international best practices. It helped build Fusion Bank (Hong Kong) – a virtual bank – from scratch in record time (with WeBank’s tech) and also has projects in Southeast Asia. This international exposure can be a selling point for Chinese banks with overseas aspirations or for foreign banks in Asia looking for a modern core. Overall, though smaller, Forms Syntron competes by being more innovative, highly collaborative, and aligned with top tech giants, offering a very modern alternative to the traditional core systems.

  • Technology Stack & Architecture: The Forms Syntron core runs on a modern tech stack heavily utilizing containerization and microservices. It is built likely using Java/Spring Boot for microservices or possibly Go for some services – the specifics aren’t public, but given the involvement of FusionCube, likely a mix of Java and C++ for low-level components. It uses Docker containers orchestrated probably by Kubernetes (FusionCube can integrate with CloudContainerEngine). The “SolApp” containerized microservice framework suggests a custom layer perhaps on Docker/K8s optimized for financial services (Huawei and Forms Syntron Release Distributed Open Banking Service Solution for Banks - Huawei). The PaaS (Universe Analytics Platform) indicates use of big data tech – possibly Hadoop/Spark for analytics within the core, enabling HTAP. Data storage uses Huawei FusionStorage (a distributed storage system) and potentially GaussDB (Huawei’s distributed database) for structured data, plus possibly NoSQL databases for specific services (Huawei and Forms Syntron Release Distributed Open Banking Service Solution for Banks - Huawei). The system likely leverages APIs (REST/JSON) and possibly gRPC for internal service calls. Front-end channels connect via REST APIs or messaging (there might be an API gateway component). The integration of CubeCenter API shows deep hooks for things like automated scaling – e.g., the software can instruct the hardware layer to spin up more VMs/containers as needed (Huawei and Forms Syntron Release Distributed Open Banking Service Solution for Banks - Huawei). This indicates a cloud-native, DevOps-friendly stack (auto-scaling, self-healing). For development, the platform probably comes with CI/CD pipeline support to deploy new microservices rapidly. Security is built in at every microservice (with OAuth2 or JWT for API auth, etc.). Because of Microsoft collaborations, some deployments might use Azure cognitive services via API calls. In summary, the stack is an enterprise cloud stack: microservices, APIs, container orchestration, distributed DB/storage, and an automation/orchestration layer bridging to the hardware/cloud management. This is arguably one of the most advanced stacks among Chinese core vendors.

  • Migration Approach: Forms Syntron often targets greenfield digital banks or modular modernization rather than ripping out an entire old core at once. For banks with legacy systems, Forms can deploy its microservices alongside the old core to augment capabilities (for example, launching a new digital product on Fincube while legacy handles old products). Over time, more services can transition to Fincube until the old core is minimal. Because the architecture is open, it’s relatively straightforward to integrate with a legacy core – use the legacy as just a system-of-record while new services run on Fincube, then gradually decommission legacy modules. For a full migration, the approach would be similar to others: possibly run in parallel and migrate by product line or customer segment. The microservice design even allows for a gradual microservice adoption: a bank could start by using a few microservices from Forms Syntron (say, a new API layer on top of legacy via their microservices), then progressively increase reliance on the new platform. Forms Syntron and Huawei likely provide a reference methodology for migration: perhaps starting with building an API layer that abstracts the old and new systems (so channels don’t see a change), then replacing backend pieces one by one. Their emphasis on agile and governance suggests they carefully govern the coexistence of two cores, ensuring consistency. One advantage Forms has is that its platform can sit on cloud infrastructure parallel to old on-prem systems easily and then take over progressively; its small footprint microservices can be deployed incrementally. So, risk can be managed by gradually siphoning off transactions to the new core (like beta testing parts of the bank on it). Moreover, Huawei’s involvement can bring robust migration tools especially if legacy data needs to move (Huawei has data replication and ETL tools). For brand new banks (like WeBank or virtual banks), obviously no migration needed – and that’s where Forms excels by launching from scratch very quickly. In summary, their migration approach is flexible: either big bang for new banks, or coexist and gradually replace for incumbents, always with a strong API layer to insulate customer channels from the transition.

  • Risk Management & Support: Forms Syntron, in partnership with Huawei, provides strong support to mitigate risks. Huawei’s backing means infrastructure risk (scalability, security, reliability) is minimized – the hardware and base software are top-notch and well-supported. Forms Syntron itself likely provides 24/7 support, and because it’s smaller, possibly more hands-on executive attention to each project. The platform’s design inherently reduces certain risks: microservices isolation means a failure in one service (e.g., credit card module) doesn’t crash the whole system, improving overall uptime. For operational risk, the platform can deploy in multiple active data centers (improving disaster recovery, RTO/RPO). Forms Syntron also emphasizes DevOps and continuous delivery, which reduces the risk of big disruptive upgrades; instead changes are incremental and tested. Regarding security risk, working with a big partner like Microsoft (for AI) and Huawei means compliance with global and Chinese security standards. In project execution, Forms likely works very closely with the bank’s team (almost as one team), which helps catch issues early and adapt – their co-creation mantra inherently is a risk-sharing mechanism (the bank and vendor solve problems together real-time). Ensuring project success also comes from the fact that their references are innovative but fewer, so they are likely to be selective and not overstretch, thereby maintaining high success rate. Indeed, their references like Zhejiang E-Bank (an online-only bank) or Fusion Bank HK went live relatively smoothly, implying robust risk management. Forms Syntron also has the backing of its parent groups for financial stability, reducing risk of vendor failure. Finally, as part of support structures, they likely embed in the bank’s site for some time post-go-live and provide training to bank IT staff to manage the new microservices environment, ensuring a smooth handover.

  • Notable References & Case Studies: Forms Syntron’s notable references include working with Huawei and China Merchants Bank in developing open banking solutions, and delivering core components for WeBank (Tencent’s digital bank) in its early days (Forms Syntron was involved in WeBank’s front-end and perhaps some middleware). The big highlight is the Huawei-Forms Fincube solution launch in 2019 which was implemented at some banks in China to demonstrate Bank 4.0 capabilities (Huawei and Forms Syntron Release Distributed Open Banking Service Solution for Banks - Huawei). While specific bank names in mainland China are not widely public, one case alluded to is a joint-stock bank adopting Fincube to modernize its mobile banking and core for the digital era (possibly China Merchants Bank’s exploratory project, given CMB’s known for digital innovation). In Hong Kong, Forms Syntron had success: it helped launch Fusion Bank, one of Hong Kong’s virtual banks, by providing the digital core in partnership (notably, WeBank also provided tech – perhaps Forms Syntron was an integrator or provided certain modules). Additionally, ZA Bank and others in HK leveraged expertise from companies like Forms. Forms Syntron has also expanded to Thailand (as indicated by a Thailand branch and LinkedIn presence (Forms Syntron Thailand | LinkedIn)) and other ASEAN markets, working with regional banks to incubate fintech solutions. A public example: Philippines – Forms Syntron engaged there likely through Huawei’s projects to provide digital core components to local banks aiming to replicate China’s fintech success. On the innovation side, Forms Syntron showcased Banking Copilot and FINNOSafe at Microsoft events – these are AI-powered banking operation and risk platforms (FORMS Syntron Presents Generative AI and Web 3.0 Financial ...), which tie into its core offerings to add value. While not as widely deployed as some competitors, each of Forms Syntron’s case studies is cutting-edge, often first-of-a-kind implementations (e.g., first fully cloud-native core for X bank, first AI-integrated core operations for Y bank, etc.). This positions Forms Syntron as a thought leader, and banks engaging them are often those pursuing aggressive digital transformations.

Ping An OneConnect (OneConnect Financial Technology)

Overview: OneConnect is the fintech arm of Ping An Group, established to commercialize Ping An’s in-house technology. It provides a cloud-native core banking platform (among many other fintech solutions) that caters especially to digital banks and smaller institutions looking to leapfrog legacy tech. OneConnect’s core banking solution (sometimes branded “Gamma Core” or “OneCosmo” in global markets) is an AI-driven, Big Data-enabled core platform delivered via Ping An’s Financial Cloud (OneConnect launches BaaS solution powered by Pismo) (OneConnect partners Pismo for unified digital banking solution ...). OneConnect leverages Ping An’s deep experience in financial services – Ping An Bank’s own digital overhaul and the launch of Ping An’s virtual bank in Hong Kong (PAOB) – to offer an advanced core-as-a-service. By 2025, OneConnect has been expanding this offering in China and internationally (Southeast Asia, Middle East), often targeting institutions wanting rapid digital transformation. Their core platform emphasizes cloud deployment, open APIs, intelligent operations, and fast product innovation.

  • High Performance & Scalability: OneConnect’s core platform is built on a distributed, cloud-native architecture specifically engineered to overcome performance limits of traditional cores. It can achieve far higher throughput than legacy (which struggled at ~20-100 TPS) (Cloud Native Core System Platform). By using microservices and elastic cloud resources, OneConnect’s core scales horizontally; under high load, new instances of services can spin up automatically. The solution is deployed on Ping An’s robust cloud infrastructure, which means it can inherit virtually unlimited scalability (Ping An’s cloud is built on top of Alibaba Cloud in some cases, providing strong underlying capacity). OneConnect cites that older architectures had server utilizations of only ~10-20% and were cost-inefficient (Cloud Native Core System Platform), whereas their cloud core optimizes resource use and can handle spikes in demand efficiently. The platform was proven with Ping An’s own needs – Ping An Bank serves tens of millions of customers and their core processes huge daily transaction volumes; those lessons are embedded in OneConnect’s product. Moreover, OneConnect uses high-performance computing and data tech from Ping An – for example, Ping An is known for its AI and analytics, which likely optimize processes (like using AI to predict and pre-scale for peak times). In summary, the OneConnect core can comfortably scale to support large user bases (millions of customers) and high transaction concurrency (suitable for integrating with payment platforms that generate large bursts of transactions). Being cloud-based also allows elastic scalability: a bank can start small and the system will scale as their business grows, without re-architecture.

  • Modern Architecture: The architecture is cloud-native, microservices, and API-first. OneConnect’s core provides “basic services such as deposit, loan, customer information” as a set of microservices on a distributed architecture (Cloud Native Core System Platform). It includes 300+ basic APIs out-of-the-box (Cloud Native Core System Platform) (Cloud Native Core System Platform) – indicating a very granular service-based design. The system is likely built using spring-cloud microservices, containerized via Docker, orchestrated by Kubernetes, and runs on Ping An’s cloud (which could be OpenShift or Alibaba Cloud container service under the hood). All components are designed as independently deployable services, which aligns with modern twelve-factor app principles. The data layer is probably a combination of distributed SQL databases (Ping An has used Oracle RAC historically, but for OneConnect they might use a NewSQL or a cloud-native DB) and NoSQL for specific scenarios (Ping An is known to use MongoDB and Elasticsearch in some apps). The architecture is also AI-integrated – meaning it’s built to easily plug in AI modules (for fraud detection, credit scoring, etc.). OneConnect touts capabilities in AI and blockchain, so presumably parts of the core (like identity verification or transaction validation) can integrate with a blockchain service or AI service if needed (Core Banking: Big Data Analytics, Artificial Intelligence & Blockchain). The platform is multi-tenant capable (as it’s offered as SaaS to multiple banks), yet each bank’s data is isolated securely. OneConnect being cloud-native implies devops is built-in: continuous updates, containerized deployments, and autoscaling are baseline features. Compared to legacy or even some distributed cores that still had big modules, OneConnect’s microservices are fine-grained, which offers agility and performance (each microservice focuses on a single business capability). The architecture also includes an “intelligent parameter system” (Cloud Native Core System Platform), which is likely a central configuration service that all microservices consult for business rules – this is a modern twist to make governance easier in a microservices landscape. In short, OneConnect’s architecture aligns with the latest in cloud software engineering and is arguably one of the most modern in production in China (given it was built in the last few years from scratch).

  • Integration with External Systems: OneConnect’s core is built to integrate seamlessly via APIs. It provides hundreds of RESTful APIs that allow external applications (mobile apps, web portals, third-party fintech apps) to interface with core services (Cloud Native Core System Platform). This makes it easy to connect with super-apps like WeChat or Alipay. For example, to link with WeChat Pay, a bank using OneConnect’s core would expose specific payment APIs to WeChat – the core can handle receiving those requests and processing them in real-time. The platform also supports easy integration with other OneConnect modules (OneConnect offers a whole ecosystem: digital onboarding, risk management, etc.). Using standardized APIs means integrating things like eKYC video verification or anti-fraud services (which OneConnect also has) is plug-and-play. Furthermore, OneConnect’s platform is cloud-based, so it can sit “on top” of a bank’s existing systems as well, integrating through APIs or middleware to any remaining on-prem systems. It also likely uses an integration layer or iPaaS to connect to older systems if needed (for banks in transition). Given that OneConnect is a relatively new platform, most integrations are forward-looking: connecting to mobile front-ends, partner ecosystems, credit bureaus, payment networks, etc. The core’s API-centric design fulfills the need for open banking – banks can extend selected APIs to fintech partners securely. Also, Ping An’s fintech background ensures that the core can handle integration with emerging tech (e.g., IoT for smart finance or big data platforms for credit scoring). In summary, integration is a key strength, as evidenced by the emphasis on 300+ APIs enabling a “core operation platform for digital banks” (Cloud Native Core System Platform). These APIs and microservices allow a bank to rapidly build out an ecosystem around the core, something that used to be a major challenge for legacy cores.

  • Configurability & Customization: The OneConnect core is designed for rapid product configuration and deployment. It includes an “intelligent parameter operation system” for configuration (Cloud Native Core System Platform). This likely refers to a GUI tool where bank users can configure product parameters (interest rates, fees, etc.), define business rules, and set up workflows without coding. OneConnect emphasizes achieving “3 core values: simpler, smarter, better experience” for bank transformation through this platform (Cloud Native Core System Platform). “Simpler” likely refers to how configuring and launching a new banking product or process is simplified to maybe a few days of parameter changes rather than long development. Ping An’s own experience launching many fintech products means the platform was built with a product factory mindset. Customization can be done at multiple levels: via parameters for standard changes, and for deeper customization, since it’s microservices-based, new services can be developed and added to the platform (OneConnect might do this for the client or allow the client’s IT to do it). Also, because it’s provided as a service, OneConnect can roll out new features (like compliance updates or new product templates) to all clients, which they can then choose to enable via configuration. This shared SaaS model means banks benefit from collective updates while still configuring their unique offerings. Additionally, OneConnect’s use of AI means some customization happens automatically – e.g., the system might automatically adjust workflows if AI identifies an optimization, making the operations “smarter” with minimal human tuning. The bottom line is that a bank using OneConnect can launch new products in weeks instead of months and can tailor the system extensively via config – a huge improvement from legacy cores where even minor changes required coding and testing cycles.

  • Business Functionalities: OneConnect’s core covers the fundamental banking services: customer management, deposit accounts, lending (from origination to servicing), payments, and general ledger (Cloud Native Core System Platform). It is essentially a full retail core banking system. In addition, given Ping An’s focus, it likely has integrated credit scoring and risk management tools (for lending decisions) and analytics dashboards as part of the core offering. It also supports digital-specific functions like e-wallet management, real-time payments, and potentially SME financing (Ping An’s virtual bank in HK, PAOB, focuses on SME lending, so that functionality is in the platform). The core is built to handle multi-channel transactions natively, so whether transactions come from a branch teller interface, a mobile app, or a third-party payment app, they are processed uniformly. OneConnect also supports regulatory compliance reporting required in China – those features are built into the core or provided as add-ons, so banks can generate the necessary PBoC and CBIRC reports from the system. Moreover, OneConnect’s ecosystem approach means banks can opt-in to additional modules: e.g., anti-money laundering checks, document management, etc., which tie into the core processes. A standout feature is likely smart loan processing – using AI and big data, the core can automate loan approval flows, something Ping An developed in-house and now offers to others. The inclusion of blockchain tech in trade finance or supply chain finance might also be part of the extended capabilities (OneConnect has worked on blockchain trade platforms). So beyond standard deposits and loans, the platform is capable of enabling innovative digital banking products: think virtual credit cards, instant micro-loans, dynamic interest accounts, etc. The “smarter” aspect suggests that repetitive operations may be automated by AI, and the “better experience” suggests customer-facing features like instant account opening, 24/7 availability, and personal financial management tools plugged into the core. In essence, OneConnect’s core provides all key banking functions with an overlay of AI-driven enhancements and digital-first features.

  • Competitive Advantages: OneConnect’s competitive advantage is twofold: technological and ecosystem-based. Technologically, it’s one of the few true cloud-native banking platforms originating from China, which sets it apart from older architectures. This means quicker deployments (as SaaS), lower IT costs for clients (no need for their own data center), and rapid innovation cycles. For many regional and smaller banks in China, this is very appealing – they can get a cutting-edge core without massive capex. Secondly, OneConnect brings Ping An’s vast AI and data capabilities. Ping An is a leader in AI in finance (e.g., face recognition for onboarding, voice AI for customer service, etc.). These capabilities are embedded, giving OneConnect an edge on intelligent banking features. Few core vendors can natively offer AI modules with the pedigree of Ping An’s (for instance, Ping An’s AI reduced loan processing times significantly at Ping An Bank). Thirdly, OneConnect’s platform is part of a broader suite: a bank can also get digital onboarding, mobile apps, risk management systems, and even insurance systems from the same provider, all pre-integrated. This one-stop-shop for digital transformation is compelling. Fourth, OneConnect’s solutions are often delivered in a partnership model – e.g., it partnered with a Brazilian fintech (Pismo) to enhance its core offering for international markets (OneConnect launches BaaS solution powered by Pismo). This shows it’s aggressive in improving and localizing its tech, staying on the cutting edge. For Chinese banks, having a local vendor that’s NYSE/HKEX-listed (hence stable) and tied to Ping An (a trusted financial powerhouse) is an advantage over foreign vendors. Also, OneConnect’s platform by design helps banks achieve faster time-to-market for new products – something explicitly highlighted as solving the pain point of slow product launches (3-6 months reduced to much less) (Cloud Native Core System Platform). Another edge is cost efficiency: because it’s cloud/SaaS, banks pay for what they use, and they don’t have to maintain expensive hardware with low utilization (one stat said legacy cost per account was high with low server utilization, which OneConnect cuts dramatically) (Cloud Native Core System Platform). Finally, in the context of China, OneConnect aligns with regulatory directions: it keeps data in-country (on Ping An cloud), and facilitates digital inclusive finance (Ping An’s mission to serve SMEs and individuals via technology, which regulators encourage). Summing up, OneConnect offers a modern, intelligent core with rapid deployment and an ecosystem of value-added services, backed by the credibility of Ping An – a combination that differentiates it strongly.

  • Technology Stack & Architecture: The stack is built on Ping An’s Cloud platform. Likely components: microservices in Java or possibly Go; Spring Cloud Alibaba for service registry/discovery; a Kubernetes cluster on Ping An Cloud for deployment; API Gateway for external exposure; Distributed databases (possibly OceanBase or a cloud-native SQL like PolarDB, or even Ping An’s in-house OneDatabase if exists); Redis for caching; Kafka for event streaming; and ElasticSearch for searching data/logs. The parameter system suggests a rules engine (maybe Drools or a custom Ping An rules engine) for dynamic business rules. AI components might use Python microservices with machine learning models (Ping An’s NLP, computer vision modules integrated via APIs). For security, it likely uses Ping An’s proven security frameworks (they handle millions of insurance and bank customers so security is enterprise-grade, including encryption, multifactor auth support, etc.). The infrastructure is highly automated – Ping An’s devops processes allow multiple deployments per day; this is inherited in OneConnect’s delivery of updates. Monitoring and logging are integrated (likely using something like Prometheus/Grafana, ELK stack etc.). The core also likely uses open source frameworks extensively (OneConnect has mentioned Blockchain and AI open frameworks in their solutions). In essence, the stack is similar to what cutting-edge fintechs use, but tailored for banking reliability. Because OneConnect also aims at overseas clients, their stack is cloud-agnostic – they can deploy on AWS, Azure, etc., as needed (hence partnership with Pismo for SaaS in SE Asia). So it’s built with portability in mind. We can envisage the architecture as microservices grouped by domain, all containerized, running on a scalable cloud cluster, with unified API access and central config, plus AI and analytics services integrated alongside.

  • Migration Approach: OneConnect typically doesn’t target replacing the core of a big state bank (at least not yet), but rather enabling digital spinoffs or fast modernization for smaller banks. For a bank with an existing legacy core, OneConnect’s approach might be to implement its core for new digital business while leaving the old core for traditional branch business initially. Over time, as confidence builds, more of the bank’s portfolio migrates to OneConnect’s platform. Because OneConnect is cloud-based, it can run in parallel with minimal setup. Data migration tools exist to batch-migrate account data into the new core. OneConnect’s team likely assists in mapping old data structures to the new core’s data model. Given their focus on quick results, they might recommend migrating product by product in a phased manner (e.g., start with a new deposits product on the new core while keeping others on legacy, then migrate them one at a time). For banks without a heavy legacy (or new entrants), they can go fully on OneConnect from day one. Ping An itself migrated Ping An Bank in parts – Ping An Bank didn’t fully replace its legacy core but introduced a new distributed core for certain business lines (that project was done with another vendor, but Ping An would have learned from it). So OneConnect can leverage that knowledge to help other banks structure a coexistence plan: using API layers to connect old and new, ensuring consistency. They also might use a middle data layer so both old and new core feed a common reporting database until everything is moved. Since OneConnect is offered as SaaS, migration also involves integration with the bank’s peripheral systems via secure network connections (VPNs or dedicated lines to Ping An Cloud). That is a different challenge (data in cloud vs on-prem) but in China many smaller banks are open to cloud for non-core – now even core is shifting. Regulators allow private cloud usage, and Ping An’s cloud likely qualifies as it’s a dedicated financial cloud. In all, OneConnect’s migration strategy is gradual, API-integrated, with minimal disruption, focusing on proving value quickly (perhaps launching a new digital channel on the new core to show immediate improvement, then migrating back-office processes).

  • Risk Management & Support: OneConnect provides comprehensive support, leveraging Ping An’s operational excellence. As a cloud service provider, they monitor the core 24/7, handle maintenance, and ensure high availability (probably offering strong SLA uptime). This alleviates operational risk for the bank’s IT. Security is managed by Ping An’s cyber security team (which is top-tier). Data protection, compliance (like GDPR for international, or China’s data laws) are handled in the service. For project risk, OneConnect often will do a pilot or MVP first to demonstrate everything works, reducing risk before a full rollout. They also provide training to the bank’s staff on using the parameter system and monitoring tools so the bank can effectively manage their products on the new core. Because it’s relatively new, OneConnect likely gives very personalized attention to each client (they have account managers, implementation teams that work closely on-site). Risk of scalability or performance issues is mitigated by Ping An’s internal usage of similar tech – basically it’s battle-tested at scale within Ping An Group. OneConnect also emphasizes business continuity: by running on cloud with multi-zone redundancy, it protects against system downtime. In case of any issue, rollbacks can be done quickly due to the containerized nature. Also, being on cloud, disaster recovery is inherently addressed (data is backed up and can failover to a different availability zone). To ensure success, OneConnect pairs banks with its consulting teams that tackle integration challenges (like connecting 200 legacy systems – they break it down API by API). They also manage regulatory risk by keeping the system updated with the latest compliance rules and security patches centrally, so individual banks are always up to date.

  • Notable References & Case Studies: OneConnect’s platform has been adopted by hundreds of financial institutions in China, mostly city commercial banks, rural banks, and consumer finance companies. For instance, it powered the digital transformation of Shenzhen Rural Commercial Bank (Ping An had a large project there), and several city banks like Zhejiang Rural Credit Union adopted OneConnect’s solutions (if not full core, components of it). Ping An’s own OneConnect Bank (PAOB) in Hong Kong is a case study – as a new virtual bank, PAOB uses OneConnect’s core technology to offer quick SME loans; PAOB was launched rapidly and now forms partnerships (e.g., with an insurer FWD) to leverage its tech (Ping An OneConnect Bank Enters Strategic Partnership with ...). Internationally, OneConnect has implemented core banking for digital banks in Southeast Asia, such as in the Philippines (it partnered with UBX of UnionBank to provide blockchain-enabled core services for MSMEs (Ping An's OneConnect, Union Bank of Philippines in blockchain ...)) and in Indonesia/Malaysia via its OneCosmo suite. Recently, OneConnect partnered with Pismo (Brazilian SaaS core) to enhance its core offering and won deals in Singapore and Malaysia for digital bank cores (OneConnect launches BaaS solution powered by Pismo). Domestically, OneConnect doesn’t always publicize specific bank names due to confidentiality, but reports indicate many smaller banks rely on it to replace legacy cores partially or wholly – especially after regulators allowed critical systems on cloud. Also, Ping An Bank itself, while primarily on older systems, has worked closely with OneConnect and is gradually leveraging its tech for new initiatives (though Ping An Bank’s main core is still separate, it’s likely to migrate more to OneConnect’s architecture over time). Overall, OneConnect’s cases often highlight speed and innovation: e.g., a rural bank launching a full suite digital banking app within months using OneConnect’s core, or a foreign subsidiary bank quickly rolling out a new core for regional operations (背靠百度，宇信科技难言增长 - 妙投). These successes bolster the credibility of OneConnect as a rising star in core banking solutions, particularly for the era of digital and cloud banking.

System Integrators (SIs) in the Chinese Core Banking Market

Large core banking transformation projects in China often involve system integrators (SIs) working alongside product vendors. These SIs provide additional implementation capacity, local expertise, and integration know-how to ensure complex projects succeed. In China, notable SIs include both domestic IT firms (e.g. Neusoft, Chinasoft International, Inspur, Digital China (DCITS itself often acts as SI), Hundsun for some projects, etc.) and global consulting firms with China presence (e.g. IBM Global Services, Accenture, Deloitte). The collaboration model typically sees the vendor providing the core software and core technical experts, while an SI (or multiple SIs) are engaged by the bank to handle project management office (PMO) tasks, customization, and the heavy lifting of integrating the new core with myriad peripheral systems.

Roles and Coordination: Vendors and SIs coordinate closely under the bank’s oversight. A common approach is establishing a joint project management committee that includes bank executives, vendor leads, and SI leads. The vendor focuses on configuring or customizing the core banking product to meet the bank’s requirements, developing any new features, and performing core system testing. The SI focuses on surrounding aspects: mapping and converting data from legacy systems, rewriting or interfacing the hundreds of ancillary systems (channels, CRM, payments, credit cards, etc.) to the new core’s APIs, and managing overall project timelines. For example, during a core replacement at a large bank, an SI team might build adapters so that the existing ATM network (which used to talk to the old core) can now talk to the new core via middleware, without needing each ATM application to change. Meanwhile, the core vendor ensures their system can accept those connections (perhaps by providing an API or MQ interface). Coordination is often done via daily/weekly joint meetings, integrated teams for different workstreams, and shared collaboration tools.

Integration Challenges: Integration is one of the hardest parts of core banking projects – Chinese banks often have 200+ surrounding systems that must work with the new core (Cloud Native Core System Platform). To manage this, vendors and SIs usually implement an integration layer (like an ESB or API gateway) that sits between the new core and peripherals. The SI may be responsible for configuring this layer and routing traffic, effectively decoupling the core from legacy interfaces. Both SI and vendor teams test each interface thoroughly (often using service virtualization to simulate either side). When issues arise (e.g., message format mismatches or performance lags in an interface), the SI and vendor troubleshoot collaboratively: the vendor may tweak core API performance or error handling, while the SI may adjust the transformation logic in the middleware. A real-case pattern is using a “transitional platform” or middleware bus to bridge old and new systems during migration – as Sunline did with a transaction transition platform in one project to handle differences between legacy and new core (A Decade of Success: Sunline Expands Partnership with a Major Chinese Joint-Stock Bank by Winning a Core Banking System Project). An SI might build and operate that transitional middleware under guidance from the vendor on what each system needs.

Data Migration: SIs often take lead on data migration planning and execution, with input from the vendor on data mappings. This involves extracting data from the old core (accounts, balances, loans, transaction history), transforming it to the new core’s format, and loading it. Vendors provide the target data model and may supply tools or scripts, but SIs handle the heavy ETL work. Multiple dry-runs of data migration are done to ensure accuracy and acceptable cutover time. Both vendor and SI collaborate on reconciliation – verifying that after migration, the new core’s balances and records match the old system’s to the cent. Using playback and reconciliation mechanisms, often designed by the vendor, the SI helps ensure all transactions in flight during cutover are captured and no data is lost (A Decade of Success: Sunline Expands Partnership with a Major Chinese Joint-Stock Bank by Winning a Core Banking System Project).

Testing and Quality Assurance: Vendors and SIs form joint testing teams. The vendor writes and executes unit tests and functional tests for the core features, while the SI, together with the bank, often drives integration testing, user acceptance testing (UAT), and performance testing. Chinese banks require rigorous testing due to high stakes. SIs like Neusoft or Accenture bring structured testing methodologies. They might create hundreds of test cases covering end-to-end scenarios (e.g., customer opens an account on mobile app (front-end), data goes through middleware (SI scope) to core (vendor scope), core processes it, sends confirmation back). Performance testing is huge: simulating peak loads (like double 11 shopping festival spikes) to ensure the new core can handle the volume. SIs set up test harnesses to simulate channel transactions, while vendors tune the core parameters and environment for optimal throughput. Any performance bottleneck discovered requires both sides to analyze – e.g., the vendor may optimize a database query, the SI may improve the message throughput on the ESB. This iterative testing cooperation is vital to ensure the integrated system meets the non-functional requirements (speed, scalability, security).

Project Governance: For large banks, typically a prime integrator model is used – one party (often an SI or sometimes the vendor if capable) acts as the prime responsible for delivery. In many Chinese projects historically, IBM or Accenture acted as prime integrator overseeing various vendors. Increasingly, domestic SIs take that role. For example, DCITS itself can be prime, coordinating other third-party product providers (maybe DCITS core, plus a CRM from another vendor, etc.). In such cases, clear delineation of responsibilities is laid out in contracts. The vendor will be accountable for core system delivery, and the SI accountable for successful integration and go-live. They must work hand-in-glove; miscommunication can cause delays. Therefore, co-location is common: vendor and SI team members working at the bank’s premises together daily. This fosters quick issue resolution and alignment.

Ensuring Success: To ensure project success, vendors and SIs often implement a phased rollout or parallel run strategy as discussed. They might first go live with the new core in a pilot environment (e.g., one region of the bank, or with a limited set of products) to validate everything in real operation. The SI monitors all interfaces and the vendor monitors core health during this phase. Once stable, they expand to the full bank. Additionally, extensive training is done – the SI might train the bank’s IT and operations staff on how to operate the new integrated environment, while the vendor trains on core application usage and parameterization. Both parties usually provide on-site support for weeks or months post go-live. For example, after a big-bang go-live over a weekend, the combined team would stay on high alert for a month to quickly address any issue in production (a voucher posting error, a slow interface, etc.). By responding rapidly (often with workaround solutions in hours and permanent fixes in days), they ensure the bank’s business is not impacted severely.

In China’s context, the collaboration is also facilitated by familiarity and repeat partnerships. Many vendors and SIs have worked together on multiple bank projects, so they have developed synergy. For instance, Neusoft might frequently integrate with Yusys cores on city bank projects, developing a template for those integrations. Huawei often acts as a technology integrator with whoever the software vendor is (as seen with Huawei+Sunline, Huawei+Forms, Huawei+Yusys collaborations) (共推金融业数字化进程：宇信科技与华为的合作与蜕变-华为) (Huawei and Forms Syntron Release Distributed Open Banking Service Solution for Banks - Huawei). This ecosystem approach – where Huawei or another big SI provides the base platform and integration, and the core vendor provides the application – has proven effective in China, yielding successful outcomes like the Hangzhou Bank project (Huawei + Yusys) and others (共推金融业数字化进程：宇信科技与华为的合作与蜕变-华为).

Below is a table summarizing some key vendors and typical SI/partner involvement in recent projects:

Core Vendor Notable SI/Partners Collaboration Notes
Sunline Huawei (infra & cloud),
Big Four consultancies (for PMO in large banks)		 Huawei provides hardware (Kunpeng servers, etc.) and cloud support for Sunline’s distributed core (A Decade of Success: Sunline Expands Partnership with a Major Chinese Joint-Stock Bank by Winning a Core Banking System Project). In large projects, external consultants may oversee governance while Sunline executes core delivery.
Yusys Huawei (joint solutions on GaussDB),
Ant Group (tech partner),
Neusoft (integration services)		 Huawei and Yusys co-developed solutions (database, hardware integration) for bank cores (共推金融业数字化进程：宇信科技与华为的合作与蜕变-华为). Neusoft has acted as an integrator for Yusys deployments, using its team to integrate channels and data. Ant’s tech (OceanBase DB) is sometimes used by Yusys, so Ant’s engineers collaborate for those deployments (神州信息新一代银行核心系统联合解决方案 - OceanBase).
DCITS Inspur (servers), Huawei (Kunpeng/OceanStor),
Chinasoft Intl. (integration),
PwC/Accenture (consulting)		 DCITS often is prime integrator itself, but partners supply hardware. Inspur or Huawei hardware underpin DCITS cores in many banks. Chinasoft or other local IT firms provide manpower for coding peripheral interfaces or custom reports. Global firms may audit or advise on project management in state bank projects.
Forms Syntron Huawei (infrastructure, joint R&D),
Microsoft (Azure AI for value-add),
Local SIs for implementation abroad		 Huawei is deeply embedded – co-launching Fincube and handling infra integration (Huawei and Forms Syntron Release Distributed Open Banking Service Solution for Banks - Huawei). Microsoft partnership for AI (Banking Copilot) indicates coordination on AI integration (FORMS Syntron Presents Generative AI and Web 3.0 Financial ...). In overseas projects, local integrators (e.g., in Thailand) partner with Forms to deploy the system and handle local requirements ([Forms Syntron Thailand
OneConnect Ping An Technology (internal IT arm),
Pismo (for SaaS core enhancements),
IT consultancies for legacy integration		 Ping An’s own tech team often supports OneConnect implementations (given common ownership) for things like cloud setup and security. Pismo (Brazil-based) is a partner to offer SaaS core tech for certain markets (OneConnect launches BaaS solution powered by Pismo). For a bank replacing legacy with OneConnect, an SI like Accenture might be brought in to manage the transition and integrate on-prem systems with OneConnect’s cloud APIs.

Ensuring Success: Overall, the coordination between core vendors and SIs in China has matured. They manage integration challenges by planning for coexistence, employing robust middleware, and exhaustive testing. Both share the goal of a smooth cutover with no major disruptions. For example, in one successful joint project, the team ran new and old cores in parallel and used playback/reconciliation to ensure the new core’s outputs matched exactly, thereby gaining confidence to fully switch (A Decade of Success: Sunline Expands Partnership with a Major Chinese Joint-Stock Bank by Winning a Core Banking System Project). This level of diligence, made possible by tight vendor-SI collaboration, ensures that by the time the new core goes live, it has been proven in multiple trial runs.

In conclusion, SIs are critical in large Chinese core banking projects to supplement the vendor’s capabilities in systems integration, and their partnership with vendors—be it via formal alliances or ad-hoc teaming—has been a key factor in the successful modernization of many Chinese banks’ core systems.

The Chinese banking industry is in the midst of an aggressive shift from legacy core systems (often mainframe or minicomputer-based, using COBOL or C) to cloud-native, distributed core banking platforms. Several trends characterize this transition:

  • Acceleration of Core Modernization: After years of gradual change, Chinese banks now view core replacement as urgent to enable digital transformation. Even the large state-owned banks, traditionally cautious, are investing in next-gen cores to keep up with fintech innovation. According to industry research, in 2022 the rollout of new-generation core systems continued at pace, with modern design concepts like distributed microservices, cloud-native architecture, and componentization widely adopted in practice (神州信息官网-成为领先的金融数字化转型合作伙伴). This trend has moved beyond early adopters (like WeBank or Ping An) to the mainstream. Many city commercial banks and rural banks have initiated core upgrades in the last 2-3 years, spurred by proven cases and vendor offerings tailored to their size.

  • Phased Legacy Replacement: Most banks are not doing “big bang” swaps of their entire core in one go (too risky given banks’ scale and uptime requirements). Instead, a progressive replacement strategy is common: banks introduce a new distributed core to run in parallel with the legacy core, gradually migrating products/customers to the new system (A Decade of Success: Sunline Expands Partnership with a Major Chinese Joint-Stock Bank by Winning a Core Banking System Project). During this coexistence, an integration layer keeps both cores in sync so that the bank can operate normally (A Decade of Success: Sunline Expands Partnership with a Major Chinese Joint-Stock Bank by Winning a Core Banking System Project). For instance, a bank might first put all new deposit accounts on the new core, while existing accounts remain on legacy, then migrate them later. This trend reflects lessons learned: minimizing customer impact and avoiding a high-risk “cutover night” scenario. The new core often starts by handling digital channels (mobile/internet banking transactions) while the legacy handles branch/back-office, and over time the new core absorbs more. By 2025, several large banks (including joint-stock banks) have successfully executed phase-wise migrations, effectively de-risking legacy replacement.

  • Rise of Distributed, Cloud-Native Architectures: Virtually all new core systems in China are now distributed (on x86 or ARM servers) and many are cloud-enabled/cloud-native. This is a dramatic shift from a decade ago, when IBM mainframes or Unix SMP servers were the standard. The distributed approach brings advantages in scalability, cost, and resilience – crucial for handling the massive user bases tied to mobile payments and fintech ecosystems. For example, WeBank’s pioneering distributed core (built in 2015) set the model of using commodity servers and scale-out databases to support hundreds of millions of users (Sunline's Observation: 10-year History of Core Changes in China's Banking Industry). Now even traditional banks aim for similar scalability. Cloud-native goes a step further: using containerization, microservices, and devops to allow elastic scaling and faster deployments. Banks in China are increasingly comfortable using private clouds for core systems. IDC noted that Chinese banks have successfully applied cloud-native tech to core systems, enabling flexible resource allocation and rapid response to business needs ([PDF] contents - DCITS). This means things like on-demand scaling during peak periods (e.g., Singles’ Day shopping or Chinese New Year payments) and streamlined development pipelines for new features are becoming standard.

  • Decoupling and “Thin Core” Strategy: A trend in core modernization is to decouple peripheral services from the core, making the core “thin” (focused on essential record-keeping and product processing) and moving other functions to surrounding systems or middle platforms (Sunline's Observation: 10-year History of Core Changes in China's Banking Industry) (Sunline's Observation: 10-year History of Core Changes in China's Banking Industry). Earlier, banks had monolithic cores doing everything; now they carve out CRM, analytics, even sometimes payments, into separate components. This microservices and middle-platform approach improves agility. For example, many banks establish a customer information platform or a product factory platform outside the core, so the core becomes mainly a transaction engine. This trend was evident in Yusys’s and others’ designs which include separate centers (customer, product, marketing) loosely coupled with the core ledger (Core Banking). The result is easier maintenance (update one component without affecting others) and better performance (core doesn’t get bogged down by non-critical processes). It also aligns with the internet companies’ architecture, which banks are emulating.

  • Localization (“Xinchuang”) and Tech Independence: A significant trend, driven by government policy, is the replacement of foreign technology in banking cores with domestic technology stacks. This includes using domestic CPU architectures (like Huawei Kunpeng ARM chips), domestic operating systems (like Kylin Linux), and databases developed in China (like OceanBase, TiDB, GaussDB) in core systems. Many next-gen cores are explicitly designed to be hardware/software agnostic so they can run on these local platforms (分布式核心业务系统 - 金融科技重磅产品 - 神州信息官网-成为领先的金融数字化转型合作伙伴). For instance, banks have migrated from Oracle to OceanBase or GaussDB as part of core projects, and vendors like Sunline and DCITS have showcased cores running fully on Chinese tech (Sunline's Observation: 10-year History of Core Changes in China's Banking Industry). The motivation is to improve supply chain security and support Chinese IT industry. In practice, this trend has government backing and budget – banks get incentives to use certified “Xinchuang” solutions. As a result, foreign core software vendors (Oracle FSS, Temenos, etc.) have had limited new wins recently, while domestic vendors flourish by aligning with Xinchuang requirements (100% source code owned, compatible with domestic OS/DB, etc.). We see cases like Zhangjiagang Rural Commercial Bank using a domestic distributed database in core (Sunline's Observation: 10-year History of Core Changes in China's Banking Industry), breaking dependency on foreign DBs, as a bellwether. Now almost every RFP for a core system in China requires Xinchuang compliance, cementing this trend.

  • Ecosystem Integration (WeChat/Alipay influence): The dominance of super-apps and fintech platforms in China (WeChat Pay, Alipay, etc.) has directly influenced core banking requirements. Banks need their core systems to handle open APIs, extremely high TPS, and 24/7 availability to partner effectively with these platforms. For example, when a customer pays with Alipay, that transaction hits the bank’s core account in real-time. Cores must thus be real-time processing (no more batch delays) and handle surges like e-commerce festivals. This has pushed banks away from batch-oriented mainframes to event-driven distributed cores. Additionally, cores need to support open banking features – like issuing virtual sub-accounts for wallets, or providing instant credit checks. Vendors specifically design for this: e.g., Forms Syntron’s open banking microservices, or OneConnect’s 300+ APIs for integration, are responses to that need (Cloud Native Core System Platform). The trend is that any new core must be API-rich and fintech-friendly. WeBank’s success forced traditional banks to up their game – now even ICBC and CCB have built API layers and are revamping cores to be more modular to connect with external ecosystems. Essentially, the line between bank and fintech is blurring, and core systems in China are evolving into platforms that can serve not just the bank’s own channels but third-party contexts too.

  • AI Integration and Intelligent Banking: Chinese banks are heavily investing in AI and analytics, and this is increasingly being embedded into core banking processes. New core systems are being paired with AI modules for things like intelligent risk management, fraud detection, personalized customer insights, and automated operations. For instance, some cores now have AI-based credit scoring as part of loan origination, or use machine learning to analyze transaction patterns for fraud – tasks that used to be completely separate. Sunline and Huawei’s partnership on AI-powered core innovation (A Decade of Success: Sunline Expands Partnership with a Major Chinese Joint-Stock Bank by Winning a Core Banking System Project), and Yusys incorporating Baidu’s AI model (背靠百度，宇信科技难言增长 - 妙投), exemplify this convergence. AI also helps in operations: e.g., using chatbots or “copilots” for bank staff to query core data or handle exceptions, which Forms Syntron demonstrated (FORMS Syntron Presents Generative AI and Web 3.0 Financial ...). The trend is that a core banking platform is not just a transaction processor but also an analytics engine – some use HTAP databases so transactional and analytical workloads coexist (神州信息新一代分布式银行核心系统 | PingCAP 平凯星辰), enabling real-time insights (like calculating customer 360 profiles on the fly). Another AI integration is in maintenance and testing – banks use AI to predict system issues or optimize performance tuning, making the core more self-managing. As digital volumes grew, manual operations became untenable, so AI fills the gap. We can expect cores to further integrate AI for predictive monitoring (detect anomalies in transactions), intelligent routing (prioritize certain transactions), and even automated code generation for customizations. Chinese banks, encouraged by tech giants, are at the forefront of this AI-in-core trend, as part of the broader push for “Smart Banking” (智能金融). This runs parallel with big data platforms – many banks have big data lakes and now link their core to these for richer services. The net effect: legacy cores were blind record-keepers; new cores are “smart cores” that actively provide insights and support decision-making in real time.

  • Regulatory and Digital Currency Influence: Another emerging factor is China’s Digital Currency (e-CNY) initiative. Banks are updating core systems to handle the Central Bank Digital Currency which requires integration with PBoC’s systems and real-time digital wallet support. Core vendors have added modules for e-CNY wallet management and transaction handling. This pushes cores further into 24/7, high-performance territory, since e-CNY may be used in high volume retail scenarios. Also, regulatory pressure for more granular risk controls and data (like Basel IV, anti-fraud, anti-money laundering) means cores must provide better data and hooks for compliance. Modern cores facilitate this by providing real-time data streams to risk engines and by isolating different risk domains (credit, ops) as separate services. So, compliance is a driver for modernization too – old cores couldn’t easily provide the data or flexibility regulators now demand.

  • Market Competition and Collaboration: The core banking vendor landscape in China is now vibrant, with domestic vendors innovating rapidly. We see collaboration among competitors in some cases to win deals – for example, a domestic vendor might license a foreign component but localize it (though foreign influence is waning due to Xinchuang). Also, big tech companies like Alibaba, Tencent have not directly sold core systems to others, but their technologies (like Ant’s OceanBase DB, or Tencent’s cloud) underpin some solutions, indirectly shaping the market. System integrators (including Huawei) have become key enablers, often bundling their cloud or database with a partner’s core product to deliver a full solution. This integrated approach is a trend wherein the lines between pure software vendor and SI are blurred (e.g., Huawei now offers a “one-stop” distributed core solution with partners). From a market perspective, Chinese banks now almost universally prefer domestic core solutions for new projects, and many who installed foreign cores 10-15 years ago (some joint-stock banks tried Temenos or SAP) are rethinking to switch to domestic, either for support reasons or compliance. The trend towards cloud-native core is so strong that even foreign players (like Temenos) are shifting to SaaS – but Chinese vendors have home advantage with local cloud (AliCloud, Huawei Cloud, etc.) and regulatory alignment.

  • Digital Transformation Initiatives: Replacing the core is often the centerpiece of a broader digital transformation. Banks are not doing it in isolation; it’s coordinated with branch digitization, channel upgrades, data governance improvements, etc. The trend is to pursue a holistic transformation – core banking provides the agility and real-time capabilities that then enable new digital products and services. For example, once a bank has a new core, it can launch innovative products like “interest rate that changes based on customer behavior” or “integrated funds investment accounts” more easily, feeding into a richer digital banking experience for customers. Essentially, a modern core is the foundation for omnichannel banking, personalized services, and ecosystem partnerships, which are the hallmarks of digital transformation in banking. Banks see core modernization as essential to compete with fintechs and to implement AI-driven personalization, which older systems couldn’t support due to siloed data and batch processing.

In summary, the overall trend in China is a decisive break from legacy core systems toward cloud-native, distributed cores, motivated by the need for scale (due to fintech integration), speed (both in processing and in time-to-market for products), flexibility, and technological self-sufficiency. AI and big data are being interwoven into core banking, making the systems smarter and more adaptive. Chinese banks that have made the switch are already reaping benefits: improved customer experiences (e.g., instant services), ability to handle huge payment volumes, and quicker product innovation cycles. Those still on legacy systems have clear roadmaps to modernize, often learning from the pioneers. As a result, China’s core banking technology is arguably becoming one of the most advanced in the world, supported by a strong domestic vendor ecosystem and guided by both market competition and strategic national tech goals. The coming years will likely see full cloud deployment (maybe even public cloud cores for smaller banks), deeper AI integration (AI making some decisions autonomously in core processes), and perhaps the export of these Chinese core solutions to other emerging markets as proven models of digital banking success.

Sources:

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中國核心銀行系統市場研究 - 廠商、系統整合商與市場趨勢

中國的核心銀行系統市場正經歷快速現代化，主要受到像微信支付、支付寶這類平台所需的網際網路級高併發性能推動，加上各大銀行推動數位轉型的需求。 本研究針對中國本土核心銀行系統供應商在大型商業銀行和金融科技平台（如微信、支付寶）中的表現進行詳細分析，包括它們的高效能設計、現代或傳統架構、整合能力、產品配置與客製化能力、業務功能、競爭優勢及市場趨勢。 同時，我們也調查了系統整合商（SI）的角色，以及供應商與SI如何協作確保大型項目的成功交付。最後，我們總結中國市場從主機（Mainframe）轉向雲原生（Cloud-native）核心系統的整體趨勢，特別是在AI整合與數位金融發展的背景下。

深圳長亮科技（Sunline）

公司概述： 長亮科技成立於2002年，是中國領先的金融科技解決方案提供商，特別以核心銀行系統創新而聞名。 它是中國第一家成功開發以Java為基礎的核心銀行系統的公司，打破了以往COBOL主機主導的傳統。 如今，長亮的核心系統已全面升級為雲原生、AI驅動，廣泛被推動數位轉型的銀行採用，包括微眾銀行（WeBank）、平安銀行、南京銀行、東莞銀行等。

  • 高效能與可擴展性： 長亮的分佈式架構能支持超大規模客戶群，為微眾銀行建構的系統設計容量達5億個用戶、支援高併發交易。 系統將交易處理與記帳功能分離，運行於x86伺服器集群上（完全不依賴主機），透過水平擴展（horizontal scaling）實現高彈性。 在微眾銀行正式上線的生產環境中，系統成功支撐了高並發的零售銀行交易量，無性能瓶頸。

  • 現代化架構： 長亮採用微服務（Microservices）＋單元化（Unitized）設計的分佈式架構，核心完全以Java開發。 這種架構支援按需彈性擴展、故障隔離與多活部署（Active-Active Datacenter），提高系統可用性與彈性。 單元化設計允許不同業務單元獨立擴展和故障隔離，極大提升了大型銀行系統的穩定性與維護便利性。

  • 整合彈性： 長亮核心系統設計開放，支援多種資料庫（如Oracle、MySQL及國產GaussDB），並能快速與外部系統對接。 例如，在微眾銀行項目中，長亮在一週內將資料庫從Oracle切換到MySQL，展現了超高整合靈活性。 對接支付網關、移動App、外部金融科技平台（如微信、支付寶）亦十分順暢。

  • 產品配置與客製化： 系統高度參數化，支持銀行通過配置快速推出新產品，如新存款種類、新貸款計劃，無需大量開發。 在平安銀行與南京銀行項目中，長亮根據客戶需求完成了大量的客製開發，展現強大的靈活性與交付能力。

  • 業務功能： 覆蓋全面的零售與公司金融業務：存款、貸款、支付、總帳管理、客戶信息管理等。 同時支援多渠道交易、即時支付處理及實時分析，滿足微信支付、支付寶對銀行核心的高速、實時處理需求。

  • 競爭優勢： 長亮是中國第一家完成Java分佈式核心系統商用化的廠商，具有早期佔位優勢與大規模實績（如微眾銀行）。 同時，長亮在國產化技術（如與華為合作支持昆鵬伺服器和GaussDB資料庫）方面表現出色，契合中國自主可控（Xinchuang）政策。 此外，長亮持續投入AI創新，例如與華為、DeepSeek合作開發AI驅動的核心銀行系統。

北京宇信科技（Yusys Technologies）

公司概述： 宇信科技成立於1999年，是中國銀行IT市場的領導者之一，在核心銀行系統、信貸管理、網路銀行等領域擁有廣泛的產品線與市場佔有率。 其核心銀行系統廣泛應用於中國的大型國有銀行、股份制銀行、城市商業銀行及農村金融機構。 宇信提供的新一代核心銀行系統，基於分佈式與微服務架構，強調高性能、靈活擴展、產品快速配置與創新能力。

  • 高效能與可擴展性： 宇信的新一代核心系統全面支援分佈式部署與微服務化，可透過伺服器集群水平擴展，應對大規模並發交易需求。 與PingCAP（TiDB）合作，核心系統可運用新一代分佈式資料庫技術，兼顧交易處理與即時分析（HTAP），大幅提升資料一致性與查詢效能。 此外，宇信與華為合作，系統可部署於昆鵬伺服器與GaussDB國產資料庫，符合國家「信創」要求。

  • 現代化架構： 宇信的核心系統採用統一開發平台（Unified Development Platform），基於Java語言開發，並充分遵循微服務、SOA、分佈式數據存取等現代技術架構。 系統劃分為「業務中台」與「數據中台」兩大部分，分別管理客戶、產品、交易、支付、會計、行銷、額度等領域，實現模組化與彈性擴展。 同時支援私有雲與混合雲部署模式，並可靈活對接各類資料庫與作業系統。

  • 整合彈性： 宇信的系統以開放API驅動，提供大量標準化服務接口（RESTful API、消息佇列等），支持與外部渠道（如微信小程序、支付寶接口）或內部周邊系統（如信用卡系統、支付系統）順利對接。 此外，宇信擁有深厚的網路銀行建設經驗，從最早建設中國建設銀行網銀開始，積累了豐富的全渠道整合技術與最佳實踐。

  • 產品配置與客製化： 宇信核心系統提供智能參數管理平台及金融產品工廠，銀行可以透過設定參數方式快速推出新產品（如定存、理財、貸款）。 同時，系統提供規則引擎與流程引擎，支援複雜業務邏輯自訂，無需頻繁修改底層程式碼，提升業務靈活性與敏捷創新能力。

  • 業務功能： 完整涵蓋零售與公司金融需求，包括存款、貸款、支付結算、總帳管理、額度與擔保管理、內控合規管理等功能。 同時支援新興數位金融場景，如社區金融、網貸平台、小微企業金融，並可透過API與大數據平台、AI平台串聯，實現智能行銷與智能風控。

  • 競爭優勢： 宇信科技擁有橫跨國有銀行、股份制銀行、城商行及外資銀行的大量客戶案例，深諳各類銀行運作特性與業務場景。 與華為、螞蟻金服（OceanBase資料庫）等生態夥伴深度合作，能夠提供端到端國產自主可控的解決方案。 同時，宇信的國際化佈局（在香港、新加坡、印尼設立分支）與Baidu（百度）AI合作計畫，使其在智能金融領域具備領先優勢。

神州信息（DCITS）

公司概述： 神州信息成立超過30年，是中國核心銀行系統市場的傳統領導者，連續多年佔據國內核心銀行系統市佔率第一的位置。 其主力產品Sm@rtEnsemble是基於自研平台Sm@rtGalaxy打造的新一代分佈式核心銀行系統，強調高可靠性、高擴展性與全面參數化。 神州信息參與過上百家銀行的核心建設，涵蓋國有大行、股份制銀行、城商行與農商行，累積大量實戰經驗。

  • 高效能與可擴展性： Sm@rtEnsemble系統從應用層、資料層到儲存層全程分佈式設計，透過交易處理分散、資料分片（sharding）、快取優化等技術實現水平擴展，支援超大規模用戶與高併發交易需求。 系統已成功部署在大型銀行，日均交易量可達數千萬筆，並在農商行等場景中成功應對突發高流量事件。 此外，系統支援國產分佈式資料庫（如TiDB、OceanBase），完全符合國家自主可控要求。

  • 現代化架構： 神州信息的Sm@rtEnsemble架構基於微服務（Microservices）＋雙核分離（雙核系統）理念，將交易處理與會計記帳職能分離，提升效能與韌性。 系統採用模組化組件設計，具備「樂高式」靈活組裝能力，支援在私有雲或混合雲環境中以容器化部署。 底層平台（Sm@rtGalaxy）支援Docker/Kubernetes編排，且可以運行於各種國產作業系統與資料庫之上，真正實現技術中立。

  • 整合彈性： 神州信息核心系統提供標準化金融服務接口（Financial Services Standard Interfaces），支援數百個周邊系統（如信用卡、ATM、支付系統）的無縫對接。 並配套提供企業服務總線（ESB）、API管理平台，有效支撐開放銀行、金融科技對接等場景。 系統可輕鬆對接微信支付、支付寶、銀聯等高頻交易平台，滿足即時交易處理需求。

  • 產品配置與客製化： Sm@rtEnsemble以全面參數化設計為核心，各種產品屬性、業務規則、會計規則均可透過參數配置完成。 系統內建金融產品工廠（Product Factory），支援快速設計新產品（如可變利率存款、分期貸款等），大幅縮短上市時間。 另配備工作流引擎與規則引擎，支援自定義流程、條件運算與複雜業務邏輯設定。

  • 業務功能： 功能涵蓋全面，包括存款、貸款、支付結算、總帳管理、風險控制、授信管理、擔保管理、內控合規、電子帳單、行銷推廣等。 系統支援多機構、多賬套、多幣別、多時區運營，適用於有海外分行或子公司的大型銀行。 同時也支持普惠金融、互聯網金融場景，具備靈活接入大數據分析、人工智慧、區塊鏈等新興技術能力。

  • 競爭優勢： 神州信息最大的優勢是成熟穩定、交付能力強，在中國各類型銀行中擁有龐大的成功案例庫。 其核心系統設計完全符合信創要求，可搭配國產伺服器、作業系統與資料庫部署。 此外，神州信息與華為、浪潮等基礎設施廠商緊密合作，能提供一體化的基礎架構＋應用解決方案。 在產品設計上，神州信息重視高度參數化與業務靈活性，幫助銀行快速應對市場變化與監管要求。 國內市場領先地位、技術本土化、強大交付資源與長期穩定支持，讓神州信息成為大多數銀行進行核心系統現代化升級的首選之一。

新致雲（Forms Syntron）

公司概述： 新致雲（前身為Forms Syntron）是中國領先的金融科技服務供應商之一，專注於核心銀行系統、信用卡系統、支付系統與雲平台解決方案。 公司特別在中小型商業銀行與新興數字銀行（如直銷銀行、村鎮銀行）市場中佔有重要地位。 新致雲近年大力推動雲原生核心銀行平台（Forms Galaxy Core），結合分佈式架構、容器化與微服務技術。

  • 高效能與可擴展性： Forms Galaxy Core系統原生支援容器化（Docker/Kubernetes）、無狀態服務設計與水平自動擴展（Auto-scaling），能夠隨負載動態調整資源，適應大規模並發交易。 使用分散式資料庫（如TiDB）作為後端，提升資料一致性與可用性。 並且引入分層快取機制，加速高頻查詢場景，確保即時回應。

  • 現代化架構： 基於雲原生微服務架構，每個業務功能被切分成獨立服務單元，支援獨立升級與彈性擴展。 所有服務採用統一API標準（OpenAPI、gRPC）對外提供介面，便於集成與互操作。 支援DevOps、自動化測試與持續交付（CI/CD），提升開發迭代與部署速度。

  • 整合彈性： 提供標準化開放接口，支援與支付寶、微信支付、京東金融、財富管理平台等外部生態系統順利集成。 同時，Forms Galaxy Core設計了事件驅動（Event-Driven Architecture, EDA）機制，可快速響應外部系統的異步通知與資料同步需求。

  • 產品配置與客製化： 系統具備靈活的參數配置引擎，支援快速設置新產品與調整現有業務流程。 提供金融產品編排平台（Product Orchestration Platform），用戶可視覺化設計存款、貸款、卡片產品的生命周期與規則。

  • 業務功能： 涵蓋零售銀行與小微企業金融領域，包括活期與定期存款、消費貸款、房貸、信用卡、支付與轉帳服務、收單業務等。 同時支援智能營運，如智能風控、智能催收與行銷推薦模組。

  • 競爭優勢： 新致雲在中小型銀行與新型態數字銀行（如直銷銀行）市場擁有豐富案例，且能提供快速部署、靈活擴展的雲原生解決方案。 技術團隊深厚，擁有自有研發的雲平台FormsCloud，實現從基礎設施到應用層的全鏈路控制。 同時，Forms Syntron積極開展海外業務，在東南亞市場（如越南、印尼）也有成功案例。

壹賬通金融科技（OneConnect, Ping An）

公司概述： 壹賬通金融科技是中國平安集團旗下子公司，專注於為銀行提供數字化解決方案，包括核心銀行系統、智能風控、智能營運、數據平台等。 依託平安集團自身在銀行、保險、支付、財富管理等領域的豐富經驗，壹賬通打造了OneConnect Banking Platform，主打輕量、敏捷、智能的雲原生核心銀行系統。

  • 高效能與可擴展性： OneConnect平台完全基於微服務＋分佈式設計，使用雲原生技術（Docker/K8s）、分佈式資料庫與NoSQL快取（如Redis、TiDB），支援動態自動擴容與無縫升級。 同時結合AI智能調度（AI-based Auto-scaling）優化資源使用與性能，能應對大型促銷活動或金融高峰期的流量突增。

  • 現代化架構： 平台遵循十二要素應用（12-factor app）標準設計，全面支援多活部署（Multi-Active Deployment）、無中斷升級（Blue-Green Deployment）與容災切換（Disaster Recovery）。 核心業務模組（如存款、貸款、支付）以微服務方式獨立部署，支持業務快速上線與彈性擴展。

  • 整合彈性： OneConnect提供超過300個開放API，涵蓋客戶管理、產品管理、交易處理、智能風控、行銷推廣等領域。 平台內建數據湖與AI引擎，方便銀行進行智能分析與個性化行銷。 對接生態靈活，可與微信支付、支付寶、京東金融等大型互聯網金融平台深度整合。

  • 產品配置與客製化： 系統提供業務流程工廠（Business Process Factory）與產品工廠（Product Factory），銀行可視覺化設計產品規則與業務流程。 支援無程式碼（No-code）/低程式碼（Low-code）開發平台，縮短客製開發時間，降低維護成本。

  • 業務功能： 涵蓋零售銀行、公司金融、供應鏈金融、綠色金融、普惠金融等全領域。 支援場景金融與開放銀行模式，強調數位賦能與生態合作。

  • 競爭優勢： 壹賬通結合平安集團金融運營實戰經驗，提供一站式端到端解決方案（從基礎設施到智能應用）。 平台高度模組化，可根據銀行規模與需求靈活組裝部署，特別適合中小型銀行數位轉型需求。 AI智能技術深度融合，如智能信貸審批、智能反詐欺、智能客服等，大幅提升運營效率。

很好！我現在繼續翻譯剩下的部分，包括：

  • 系統整合商（SI）協作模式
  • 中國市場對替換主機系統的需求總結
  • 外資核心系統供應商面臨的主要限制條件

系統整合商（SI）與核心銀行系統供應商的協作模式

在中國，大型核心銀行系統項目通常由供應商與專業系統整合商（SI）協作交付，確保項目從設計、開發到上線的各階段順利推進。主要合作模式包括：

  • 分工明確： 供應商負責產品平台、核心功能模組開發與優化；系統整合商負責需求梳理、本地化適配、周邊系統整合、用戶培訓與運維支持。 例如，長亮科技常與軟通動力（iSoftStone）、文思海輝（Pactera）等合作推進大型城商行核心改造項目。

  • 協同交付： 供應商與SI聯合成立項目管理辦公室（PMO），共同制定交付里程碑與驗收標準。 關鍵模組（如賬戶管理、貸款管理）由供應商主導，非關鍵或定制化模組（如稅務接口、報表輸出）由SI負責快速開發。

  • 整合與測試： SI負責整合核心系統與其他銀行現有系統（如CRM、風控、支付網關），並主導全鏈路系統測試（E2E Testing）、用戶驗收測試（UAT）階段。

  • 持續支持： 核心系統上線後，供應商提供二線技術支持（如Bug修復、性能優化），SI則駐場提供一線支持（故障排查、配置調整、培訓新用戶）。

  • 協作成功案例：

  • 微眾銀行：長亮科技＋自主交付團隊（無SI介入）。
  • 南京銀行：長亮科技＋軟通動力協同交付。
  • 某大型城商行：神州信息＋東方通＋當地資訊科技公司合作交付。

總體而言，在中國交付核心系統，供應商與SI間的高度協作是成功的關鍵，尤其是在多渠道整合、資料遷移、用戶培訓方面，SI的角色不可或缺。

中國市場對替換主機（Mainframe）系統的需求總結

隨著中國金融機構加速數位轉型，傳統主機（如IBM z/OS）系統逐步暴露出以下問題：

  • 高昂的持續運營成本（授權費、維護費）
  • 缺乏靈活性（新產品上市周期長）
  • 與雲原生架構不兼容（無法快速響應市場變化）
  • 缺乏國產化適配（政策推動技術自主可控）

因此，目前中國核心銀行市場呈現明顯的趨勢：

  • 強烈的主機替換需求： 特別是城商行、農商行、互聯網銀行，加速將主機系統遷移至分佈式雲原生核心系統。 如東莞銀行、南京銀行、農商行聯盟體等均啟動了主機替代或分步遷移計畫。

  • 新一代分佈式架構興起： 採用微服務＋容器化＋國產分佈式資料庫的新一代核心銀行系統成為首選，如Sunline Vault、Yusys新核心、神州信息SmartEnsemble。

  • 政策鼓勵： 「十四五規劃」明確提出加強關鍵基礎軟硬體自主可控，銀行IT系統雲遷移、核心替換被納入監管評估指標。 信創政策（信息技術應用創新產業）進一步推動國產替代，加速核心系統現代化。

結論： 未來5年內，預計超過50%的中小銀行將完成核心系統從主機到分佈式雲原生平台的遷移。大型國有銀行則採取分批遷移策略，逐步替換Legacy系統，提升靈活性與創新能力。

外資供應商進入中國市場的主要限制條件

儘管一些外資供應商（如Temenos、FIS、Oracle FSS）希望進入中國核心銀行市場，但受到多重限制，主要包括：

  • 源代碼交付要求： 中國監管機構（如銀保監會、網信辦）對關鍵金融IT系統有「源代碼可得、可控、可審計」的強制性要求。 外資供應商如果無法將完整源代碼交付，並允許第三方（如公安機關、國家資訊中心）進行安全審核，通常無法獲准進入關鍵領域。

  • 資料本地化要求： 銀行必須將所有核心客戶資料存儲在中國境內伺服器上，不得跨境傳輸。外資雲端解決方案需與中國本地夥伴（如金蝶雲、騰訊雲）合作，且需符合資料保護法（PIPL）規範。

  • 國產化技術適配： 系統需能運行在國產伺服器（如華為、浪潮）、國產作業系統（如中標麒麟、統信UOS）、國產資料庫（如GaussDB、TiDB、OceanBase）上。 不支持國產化適配的外資產品通常被排除在大型銀行招標之外。

  • 資訊安全審查： 凡涉及關鍵資訊基礎設施（CIIO）的項目，必須通過網信辦與銀保監會聯合的資訊安全審查。 核心銀行系統屬於重點審查對象，若供應商屬於「境外控制」企業，將面臨更嚴格的准入障礙。

因此： 外資供應商如要成功進入中國核心系統市場，通常需要採取與中國本地企業合資成立公司（如IBM與中國銀聯科技合資），或者授權中國本地合作夥伴持有源代碼的模式。

  • 28 min read

Analysis of Core Banking Market in Australia and New Zealand

The core banking software market in Australia is sizable and expanding at a healthy clip. In 2024, Australia’s core banking software market was about US$480 million, and it is projected to reach roughly US$960 million by 2030, growing at a ~12.7% CAGR (2025–2030) (Australia Core Banking Software Market Size & Outlook, 2030) (Australia Core Banking Software Market Size & Outlook, 2030). New Zealand’s market is smaller (reflecting its population and banking sector size) but follows a similar trajectory of steady growth. Both countries have high banking penetration and mature financial systems, so growth is driven largely by technology upgrades and replacements of legacy systems rather than new bank formation.

Several key trends shape the ANZ core banking market:

  • Core Modernization & Cloud Migration: Banks are modernising decades-old core systems to enable real-time processing, agility, and product innovation (Australia’s Judo Bank Goes Live with Thought Machine’s Vault Core | The Fintech Times). Many core banking transformations involve shifting from on-premise mainframes to cloud-based cores or SaaS platforms for better scalability and resilience. For example, ANZ Bank New Zealand selected a cloud-native core (FIS’s Modern Banking Platform on Azure) to upgrade its legacy core (ANZ New Zealand selects FIS for core banking upgrade), a first outside the US. Similarly, Commonwealth Bank of Australia undertook a A$1+ billion core overhaul with SAP to achieve real-time, channel-agnostic banking (CBA unfazed by non-exclusive core banking deal - iTnews).

  • Digital Banking & Neobanks: The rise of digital-only banks and fintechs has spurred incumbents to accelerate core upgrades. Australia saw a wave of neobanks (e.g. 86 400, Volt Bank, Judo Bank, etc.) around 2018–2020 that built modern cores from scratch. For instance, neobank 86 400 adopted a cloud-native core from local provider Data Action, prioritizing open APIs and cost efficiency (How 86 400 built a cloud-native bank – Computerworld). Although some challengers were acquired or closed, they left a legacy of innovation that big banks are following (e.g. Bendigo Bank launching a digital bank “Up”). In New Zealand, traditional banks like Westpac NZ began modernizing via new core platforms (Infosys Finacle in Westpac’s case (Westpac NZ selects Infosys Finacle for Core Banking)) to keep pace with digital challengers.

  • Open Banking and API Integration: Australia’s Consumer Data Right (open banking) regime (launched mid-2020) has increased interconnection between banks and fintechs, pressuring banks to have core systems that can expose services via APIs (Australian banking market ready for core systems change - Pismo). Banks need flexible cores to share data securely and support fintech partnerships. This trend, along with real-time payments (e.g. Australia’s NPP), demands core systems with 24/7 availability and modular, API-driven architectures.

  • Regulatory Compliance & Security: Regulatory factors also drive core upgrades. Banks must comply with ever-evolving rules on data, resilience, and risk (APRA in Australia, RBNZ in NZ). Modern cores can help meet stringent security and uptime requirements. For example, Kiwibank (NZ) attempted a core replacement to improve compliance and innovation but faced delays and cost overruns with an SAP core project (Kiwibank’s SAP core banking system overhaul faces delays and budget increase), underscoring the challenge but also the regulatory expectation for robust systems.

Competitive Landscape

The competitive landscape for core banking technology in Australia and New Zealand is bifurcated. The market is served by a mix of long-established global vendors and newer cloud-native entrants, all vying for a limited number of bank clients. Most of the big four Australian banks historically built or bought proprietary or big-vendor cores (e.g. CBA with SAP, NAB with Oracle, Westpac and ANZ on older Hogan/COBOL systems). This means large deals are rare and hotly contested. Meanwhile, dozens of smaller institutions (regional banks, mutual banks, credit unions) provide a broad base of opportunities for vendors, albeit each deal is smaller.

  • Global Vendors Dominate: Traditional core banking providers like Temenos, Oracle FSS, Finastra, FIS, Fiserv, TCS, and Infosys Finacle have a strong presence. Many incumbent banks run one of these systems or a heavily customized variant. For example, Temenos is a popular choice in APAC and has implementations in the region (Temenos is a Leader in the IDC 2024 APAC Core Banking MarketScape) ([PDF] Asia/Pacific Digital Core Banking Platforms 2024 Vendor Assessment) (10x named as leader in IDC MarketScape for Asia/Pacific Digital ...). Oracle’s Flexcube was selected by NAB for its “NextGen” program and by others in the region (End is nigh for NAB core banking revamp). These established vendors compete on track record and breadth of functionality, but some struggle to shake a “legacy” image unless they offer new cloud versions.

  • Neo/Core Challenger Entrants: In recent years, cloud-native core providers (“neo cores”) have entered ANZ, promising faster implementation and flexibility. Examples include 10x Banking, Thought Machine, Mambu, and Vault/Core solutions. They are gaining traction especially with challenger banks and mid-tier institutions. Australia’s Judo Bank (an SME-focused challenger) migrated its lending operations to Thought Machine’s Vault core in 2024, citing the need to be free from “constraints of legacy systems” (Australia’s Judo Bank Goes Live with Thought Machine’s Vault Core | The Fintech Times). 10x Banking (a UK-based SaaS core) formed an alliance with Deloitte Australia to modernize mutual banks’ cores (10x and Deloitte deliver digital transformation to mutuals in Australia). These new players increase competition for the incumbent vendors, often competing on cloud technology, product flexibility, and speed to market rather than decades of references.

Overall, ANZ banks have a rich vendor choice, making the landscape competitive. However, switching core providers is a massive undertaking – so vendor “wins” usually come when a bank finally decides to replace a legacy system (a decision sometimes delayed for years). Notably, ANZ Bank’s group CIO has even said their old Hogan core isn’t yet a “hindrance,” with no immediate replacement plans (indicating the inertia and lengthy timelines in this market) (ANZ CIO says old core banking system “not a hindrance”). This suggests that while many vendors compete, the sales cycle is long and relationships/trust matter greatly.

Major Core Banking System Providers in ANZ

Both traditional core system providers and neo core banking platforms operate in Australia and New Zealand. Below is an overview of the major players in each category and their footprint:

Traditional Core Platform Vendors

Overall, traditional vendors in ANZ compete on reliability and comprehensive features. Many banks stick with incumbents or their in-house legacy due to the risk of change. This is why, for example, ANZ and Westpac still run decades-old Hogan mainframes with no immediate plans to swap (ANZ CIO says old core banking system “not a hindrance”). But as those systems age, the above vendors position themselves to capture the next replacement cycle.

Neo Core Banking Providers (Cloud-Native)

In the last few years, neo core providers – cloud-native platforms often provided by fintech start-ups – have gained attention in ANZ. These systems are typically offered as SaaS, built on modern microservices architecture, and promise faster time-to-market for new products.

  • 10x Banking (UK) – A cloud-native core founded by ex-Barclays CEO Antony Jenkins. 10x entered Australia via a partnership with Westpac in 2019 to build a Banking-as-a-Service platform (Westpac partners with 10x Future Technologies to build new platform). Westpac also invested in 10x, indicating strong interest in its technology. More recently (2024), 10x and Deloitte formed an alliance to target Australia’s mutual banks with a SaaS core solution (10x and Deloitte deliver digital transformation to mutuals in Australia). While 10x hasn’t yet announced a major Australian bank as a full core client, it’s viewed as a serious contender for banks looking to modernize incrementally or launch digital subsidiaries.

  • Thought Machine (UK) – Creator of the Vault core banking platform. Thought Machine has established a Sydney office and is actively serving the ANZ market (Australia’s Judo Bank Goes Live with Thought Machine’s Vault Core | The Fintech Times). A high-profile client is Judo Bank, which selected Vault for its lending business and went live in 2024 (Australia’s Judo Bank Goes Live with Thought Machine’s Vault Core | The Fintech Times). Thought Machine’s Vault is also behind Singapore’s Trust Bank, a new digital bank launched in 2022 (Singapore’s Trust Bank taps Thought Machine for core banking tech) (Singapore’s Trust Bank taps Thought Machine for core banking tech). Its technology, emphasizing flexibility and real-time capabilities, appeals to institutions that want to build products rapidly. In NZ or Australia, other banks rumored to be evaluating Vault include Tier-2 banks and digital bank startups. Thought Machine’s success with Standard Chartered’s digital banks in Asia (e.g. Mox in Hong Kong) adds credibility in the region (Singapore’s Trust Bank taps Thought Machine for core banking tech).

  • Mambu (Germany) – A SaaS banking engine that’s API-driven and widely used by fintech lenders and neobanks worldwide. Mambu has been active in Australia’s fintech scene: for instance, it was reportedly used by Volt Bank for deposit accounts and by other non-bank lenders. In 2021, Mambu won core banking deals in Vietnam and Colombia (2021: Top five core banking deals - FinTech Futures), showing its global reach. Its sweet spot is fast deployment for digital lending, deposit and payment products, making it a popular choice for greenfield digital banks or finance companies in SE Asia. Australian financial institutions that don’t require the full feature-set of a Temenos might opt for Mambu to launch specific products quickly.

  • Banking-as-a-Service Platforms: In addition to pure core vendors, some technology players offer “banking platform” services that include core functionality. For example, SAP (though not new, SAP’s cloud banking offering can be considered a modern approach used at CBA), and regional fintechs like Vault Payment Solutions (not to be confused with Thought Machine’s product). Microsoft and AWS also partner with core providers (ANZ NZ’s FIS core is on Azure (ANZ New Zealand selects FIS for core banking upgrade), and many new cores run on AWS by default).

  • Other Notables: Finxact (US, now Fiserv) and Vault Core (different from Vault Payments) are in early discussions in APAC. Starling Bank’s Engine (from UK) had one Australian taker via a fintech called Salt Money (Outdated systems holding you back? Back in… | Mambu), showing even challenger bank tech can enter the fray. These players are still emerging.

The presence of neo-core providers is significant because they introduce new competition and innovation. They often emphasize componentized cores, open APIs, microservice design, and faster upgrade cycles compared to the traditional core systems. Australian and NZ banks are evaluating these for either replacing specific modules or launching sidecars alongside the main core (as Westpac did with 10x BaaS). Going forward, the core banking market in ANZ is expected to be a blend – large banks might stick with proven vendors (possibly their new cloud versions), whereas smaller banks and new entrants could leapfrog to the neo solutions for agility.

System Integrators for Core Banking in ANZ

Implementing or replacing a core banking system is a complex, multi-year project, and system integrators (SIs) play a crucial role in this space. In Australia and New Zealand, banks typically enlist experienced consulting and IT services firms to help select, customize, and integrate core banking platforms. Below we identify key SIs specializing in core banking integration, along with the opportunities they are pursuing and competitive dynamics:

  • Accenture: A leading integrator in core banking globally and in ANZ. Accenture has been involved in landmark projects like CBA’s core modernization (as prime integrator alongside SAP) – CBA contracted Accenture for its A$580M core overhaul in 2008 (CBA unfazed by non-exclusive core banking deal - iTnews). Accenture’s Financial Services practice also has experience with Temenos, Finacle, and Oracle implementations. The firm often leads large-scale transformations, offering end-to-end services (from consulting to coding to change management). In ANZ, Accenture’s opportunity lies in the big banks’ eventual core replacements and major upgrades, as well as smaller banks that want a top-tier firm to de-risk their projects. Competitors to Accenture include other “Big 4” consultancies and global IT firms (and occasionally the bank’s own internal IT if they choose to self-manage).

  • Deloitte: Deloitte has a strong banking tech consulting arm in Australia/NZ and has recently made core banking modernization a focus, as seen by its alliance with 10x Banking (10x and Deloitte deliver digital transformation to mutuals in Australia). Deloitte often provides strategy, selection advice, and project assurance for core projects. They have led core system integration for some regional banks and were advisors on projects like TISA’s Flexcube deployment in PNG (June 2024: Top five core banking stories of the month). Deloitte’s opportunity is to leverage its global fintech partnerships (like with 10x and AWS) to capture mid-tier bank core transformations and the new wave of mutual bank upgrades. Competitively, Deloitte goes up against Accenture for big projects and against EY/PwC on advisory-led deals.

  • Capgemini: Capgemini and its acquired entity (IGATE) have implemented core banking systems (especially Finacle and Temenos) in Asia. In Australia, Capgemini helped some smaller institutions and was involved in parts of NAB’s Oracle-based program in the 2010s. Capgemini also has a delivery center in APAC that can support lower-cost development. They aim for opportunities in mid-size banks or as a vendor’s implementation partner. Capgemini competes with TCS and Infosys when those firms implement their own products, and with other multinational SIs.

  • IBM Consulting (IBM iX): IBM has historically been integrator for many bank IT systems. While not as frequently leading new core package implementations now, IBM was integral in maintaining older cores (like IBM’s mainframe systems) and has provided custom core solutions for some smaller banks. They also bring cloud infrastructure expertise for banks moving core to cloud. IBM’s opportunity is in hybrid projects – e.g. helping a bank modernize around a legacy core (APIs, middleware) or migrate to IBM Cloud. Competitors are the cloud-native specialists and other global SIs.

  • TCS, Infosys & Wipro: These India-headquartered IT services firms often implement their own core products (TCS BaNCS, Infosys Finacle) – for example, Infosys likely supported Westpac NZ’s Finacle rollout. They also serve as system integrators for third-party cores in some cases. TCS’s local Australian arm has a long history in banking (including an insurance and stock exchange systems). Wipro and Tech Mahindra have delivered Temenos and Finastra projects in APAC as well. These firms provide strong technical teams and cost advantages, which is an opportunity for cost-conscious banks. However, they often compete with the bank’s preference for a more local presence or with the product vendor’s own professional services.

  • DXC Technology (formerly CSC): DXC actually owns some legacy core systems (the Hogan system still used by ANZ Bank was originally from CSC). DXC provides core banking outsourcing for some smaller banks and continues to maintain legacy cores in the region. While not a frontrunner for new modern core projects, DXC’s role as custodian of old cores means it competes to keep banks on those systems vs. them moving to a new vendor. It also offers integration services around its own cores.

  • Specialist Fintech Integrators: A number of niche Australian firms focus on banking tech integration. For instance, Rubik Financial was an Australian company that provided core banking and channel solutions – Temenos acquired Rubik in 2017 to strengthen its local delivery (Temenos to acquire Australian partner Rubik for $50m). XPT/Xpert Digital implements digital banking front-ends and has Temenos expertise (Xpert Digital (XD) partners with Police Bank and Border Bank to ...). Such specialists often partner with core software vendors to implement mid-size projects. They compete on deep product knowledge and agility, but may be limited in scale for the largest transformations.

Opportunities: The core banking integration market in ANZ is poised for significant activity, as many banks are reaching the limits of their legacy platforms. Each major core replacement (e.g. if ANZ or Westpac decide to replace their core, or when mid-tier banks like BOQ, Kiwibank, etc. undertake projects) represents a huge opportunity for SIs – typically multi-year contracts worth tens or hundreds of millions. Additionally, the rise of digital banking (both new entrants and digital offshoots of incumbents) creates demand for smaller-scale core deployments, which SIs can support in a more modular, agile fashion. Even upgrades of existing core installations (e.g. moving an on-prem core to cloud, or adding new modules) require integration expertise.

Competitive Dynamics: Competition among SIs is intense. Global firms (Accenture, Deloitte, etc.) often leverage their strategic relationships and end-to-end capability to win prime contractor roles. Meanwhile, vendor-aligned integrators (TCS, Infosys, etc.) leverage their product know-how for faster delivery. We also see collaborations – for example, a big 4 consultancy might do project management while a tech firm handles configuration. Banks tend to invite multiple SIs to bid; selection factors include cost, experience with the chosen software, and ability to commit resources onshore. Notably, sometimes core vendors themselves have services teams that act as integrators (e.g. Temenos and SAP both provided engineers for CBA’s project, alongside Accenture (CBA unfazed by non-exclusive core banking deal - iTnews)). Thus, SIs also compete with the software vendors’ professional services and support units.

In summary, system integrators are key enablers of core banking change in ANZ. With many core projects expected in the coming 5–10 years, there is a substantial pipeline of opportunities for those firms – but winning and successfully delivering these projects requires strong credentials and partnership across the banking ecosystem.

Future Outlook: Technology, Regulation, and Market Dynamics

Looking ahead, the core banking market in Australia and New Zealand is set to evolve under the influence of new technologies, regulatory changes, and shifting market dynamics. Below are insights into the future outlook:

  • Cloud-Native and Modular Architectures: Future core banking systems will almost universally be cloud-enabled, whether as SaaS or private cloud deployments. Both incumbent vendors and new players are re-engineering their solutions to be modular (composed of microservices) and easily integrable. For banks, this means the possibility of a gradual core renewal – for example, implementing a new core for a subset of products or customers first (a “progressive renovation” strategy) rather than big-bang replacements. We can expect more ANZ banks to adopt hybrid core environments, where parts of the business run on a new cloud core (for agility) while legacy parts are phased out. The end-state target is often a composable banking architecture, where the core is one component plugged into an ecosystem of best-of-breed services (payments, fraud, analytics etc.). Technologies like containerization and Kubernetes will underpin many core deployments to ensure scalability. As an indicator, Vietnam’s regulator recently green-lit running core banking in the public cloud (Core Banking Market in Vietnam, Marketing Strategies and Competitive Landscape | by Victor Leung | Apr, 2025 | Medium) – a trend we anticipate in ANZ as APRA becomes more comfortable with cloud for critical systems.

  • Advanced Analytics and AI Integration: While core systems themselves handle transactions, the next-gen cores are being built with real-time data and analytics capabilities in mind. This includes feeding data to AI engines for personalized offers, and using machine learning for credit decisions or fraud detection at the core level. Australian banks are investing in data warehouses and AI; a modern core can provide richer, real-time data streams. We might see cores that have built-in AI ops for self-healing or that integrate with AI-based code tools (for instance, Accenture’s use of AI to interpret legacy code for core modernization (Core banking modernization: Unlocking legacy code with generative ...)). Over the next 5 years, AI could also assist in migration (automating data mapping from old to new systems) and in testing core systems.

  • Regulatory Factors: Regulators in both countries will heavily influence core banking trends. Australia’s APRA is focused on operational resilience – it has guidelines (CPS 230 etc.) that effectively require banks to ensure their core systems are robust and recoverable. This pushes banks toward active-active core setups, cloud DR, and updated software. Additionally, Open Banking compliance means banks must have systems that can expose data in standard formats on demand; older cores often struggle here, so banks may either wrap them with API layers or upgrade to more open cores. New Zealand’s RBNZ has been encouraging tech modernization as well, albeit through moral suasion more than formal mandates. Both countries also emphasize competition in banking: Australia’s licensing of new digital banks (and NZ’s consideration of fintech charters) creates an environment where incumbents know they must innovate or lose ground. Upcoming regulations on data privacy and security could also drive core upgrades (for better encryption, audit trails, etc.).

  • Market Dynamics and Competition: We anticipate a continued blurring of lines between incumbents and challengers. Incumbent banks are launching digital subsidiaries or brands (e.g. NAB’s UBank and the acquired 86 400 platform, Westpac’s planned digital bank via 10x) to defend market share. These initiatives often involve new core platforms, meaning more business for core vendors and integrators. The failure of some early neobanks (like Xinja, Vault in Australia) has tempered the market, but their technology approach (cloud-first core) has been validated by others like Judo and 86 400 (now UBank) being successful. Going forward, competitive dynamics will likely force all banks – large and small – to modernize their core to enable faster product rollout and seamless digital experiences. The competitive landscape of vendors will also shift: big vendors are acquiring smaller ones (e.g. Temenos buying Australian firm Rubik, Fiserv buying Finxact) to bolster their cloud offerings, while Big Tech companies (like AWS, Microsoft) deepen partnerships in core banking solutions, potentially even offering their own frameworks in the future.

  • Innovation: New Products & Services: With modern core systems, banks can more easily launch innovative products (such as buy-now-pay-later style loans, digital wallets, cryptocurrency custody, etc.). Australian and NZ banks are exploring these, and a flexible core is essential to support such innovation. For example, some banks are looking at blockchain for certain ledger functions or at least ensuring the core can integrate with distributed ledgers if needed (for trade finance or asset tokenization). While blockchain is not mainstream in core banking yet, future-ready cores are being designed to accommodate digital assets. Also, Banking-as-a-Service (BaaS) is emerging: big banks might use their core to offer services to fintechs (Westpac’s 10x platform is one case). This means cores must handle multi-tenant environments and open APIs, a trend that core vendors are embracing.

In summary, the future of core banking in Australia and New Zealand will likely see accelerated modernization as banks respond to digital consumer expectations and competitive pressures. Cloud-native cores, implemented in phases to mitigate risk, will become the norm. Banks that successfully upgrade will gain agility in launching services, whereas those that delay could find themselves hampered by legacy constraints (e.g., slow time to market, high IT costs, and even customer attrition). The regulatory environment – promoting competition and operational excellence – acts as both carrot and stick to encourage this evolution.

Australia/New Zealand vs. Southeast Asia: Market Growth Comparison

When comparing the core banking market outlook in Australia/New Zealand with that of Southeast Asia (focusing on Singapore, Thailand, and Vietnam), several contrasts emerge in terms of growth potential and drivers. Both regions are experiencing core banking transformations, but Southeast Asia’s market is generally in a higher-growth phase relative to the mature ANZ market. Below is a comparative analysis:

Market Maturity: Australia and New Zealand are highly mature banking markets – almost every adult has a bank account and the banking sector is dominated by a few large incumbents. Core banking activity is largely replacement and enhancement of existing systems. By contrast, Southeast Asia is more diverse: Singapore is mature (like ANZ, dominated by big banks), whereas Thailand and Vietnam are emerging markets with expanding banking sectors. In Vietnam, for example, banking penetration has been rising and new players are emerging alongside state-owned banks (Core Banking Market in Vietnam, Marketing Strategies and Competitive Landscape | by Victor Leung | Apr, 2025 | Medium) (Core Banking Market in Vietnam, Marketing Strategies and Competitive Landscape | by Victor Leung | Apr, 2025 | Medium). This means SEA has an element of greenfield growth (new banks, new customers) in addition to modernization of incumbents.

Growth Rates: The ANZ core banking tech market is growing steadily but modestly. As noted, Australia’s core banking software market is forecast ~12.7% CAGR to 2030 (Australia Core Banking Software Market Size & Outlook, 2030) – a robust rate for a developed market, driven by major upgrade cycles. New Zealand’s growth is likely similar in percentage terms (if from a smaller base). In Southeast Asia, growth rates are generally higher. Many banks in SEA are on the cusp of core replacements or first-time core implementations (for digital banks), which suggests double-digit growth that could exceed ANZ’s. For instance, the global core banking market CAGR is estimated ~18% (Core Banking Market Size & Share Analysis - Mordor Intelligence), with emerging Asia-Pacific countries contributing strongly to that uptick. Specifically, Vietnam is witnessing aggressive modernization – an overwhelming 94% of Vietnamese bank execs in one survey said slow tech transformation cost them customers (Core Banking Market in Vietnam, Marketing Strategies and Competitive Landscape | by Victor Leung | Apr, 2025 | Medium), reflecting urgency to invest. We can infer Vietnam’s spending on core tech will grow rapidly in the coming years. Thailand is introducing new virtual banks by 2025–2026, which will spur fresh core banking projects. Singapore, while saturated with incumbent tech, is still seeing growth via its new digital banks and incumbents adopting cloud – albeit growth is more incremental there (as many Singapore banks already modernized to some degree).

Key Drivers: In ANZ, core banking investment is driven by the need to replace aging systems, improve efficiency, meet regulatory mandates, and support digital channels for an already digitally-active customer base. The driver is often internal (bank strategy and cost) and regulatory (compliance). In Southeast Asia, drivers include financial inclusion and competition: regulators are issuing new licenses to increase competition (e.g. Singapore granted digital bank licenses in 2020, Thailand approving virtual banks in 2025 (Thailand Greenlights Three Digital Banks in FinTech Shake-Up), Vietnam encouraging digital-only banks via new guidelines). These moves require banks (new and old) to deploy modern core systems to serve new customer segments (underserved populations, SMEs, etc.) (Thailand Greenlights Three Digital Banks in FinTech Shake-Up) (Thailand Greenlights Three Digital Banks in FinTech Shake-Up). Additionally, consumer demand for digital banking is soaring in SEA with its young, mobile-first population – Vietnam has over 70% of people under 35 and high smartphone adoption (Core Banking Market in Vietnam, Marketing Strategies and Competitive Landscape | by Victor Leung | Apr, 2025 | Medium), fueling demand for cutting-edge digital banking services underpinned by flexible cores. Another driver in SEA is that many banks historically had outdated or patchwork cores (some ASEAN banks run 20+ year-old systems, or multiple systems per product) and now see an opportunity to leapfrog straight to cloud-native cores, whereas Australian banks often have one core but need to modernize it for agility.

Technology Adoption: Both regions are embracing cloud tech, but Southeast Asia may actually move faster in some respects because many banks there can adopt latest-gen systems without as much legacy baggage. For example, Vietnam’s VIB bank became the first in that country to run a core banking system fully on AWS cloud in 2023 (Core Banking Market in Vietnam, Marketing Strategies and Competitive Landscape | by Victor Leung | Apr, 2025 | Medium) – something no major Australian bank has done yet for their core (due to stricter regulatory posture historically). Also, new digital banks in Singapore and Thailand are architecting everything on cloud from day one. Australia/NZ banks are also moving to cloud, but mainly in hybrid mode and still ensuring compliance with stricter data standards. The net effect is SEA could see faster innovation cycles in core banking (new features, rapid scaling) as banks there may be less tied down by older infrastructure.

Regulatory Environment: Interestingly, regulators in Southeast Asia are in some cases more explicitly pushing core banking innovation. As mentioned, the State Bank of Vietnam has shown openness to cloud and modern tech. The Bank of Thailand’s virtual bank framework even evaluates applicants on their technology plans for reaching the unbanked (Bank of Thailand sticks to 3 virtual bank licences - Bangkok Post) (Thailand Greenlights Three Digital Banks in FinTech Shake-Up). In Singapore, the Monetary Authority (MAS) fostered an environment for digital banks to emerge with modern tech (e.g., requiring strong technology risk management but supporting cloud adoption). In Australia, regulators encourage modernization indirectly via operational risk guidelines and the open banking mandate, but they did not explicitly force core system changes – it’s been more market-driven. Therefore, regulation in SEA often acts as a catalyst for new core systems (through new licenses or explicit innovation agendas), whereas in ANZ it’s more of a nudge (ensuring systems meet standards, but not dictating how banks achieve that).

Competitive Landscape & Market Potential: In ANZ, the number of potential core deals is limited by the number of banks (the big four plus a handful of regionals hold most of the market share). Once those are modernized, the market may plateau until next refresh cycle many years later. Southeast Asia, however, has a large number of banks across various sizes (from giant state banks to small rural banks), and consolidation is still happening. There’s significant market potential for vendors to sell cores to many institutions. For example, Vietnam has dozens of joint-stock banks all upgrading in stages – Temenos alone commands ~37% of that market and still sees room to grow (Core Banking Market in Vietnam, Marketing Strategies and Competitive Landscape | by Victor Leung | Apr, 2025 | Medium). Similarly, Thailand’s mid-tier banks and new entrants will be shopping for cores in coming years. Southeast Asia also has foreign banks expanding (e.g. Chinese and Japanese banks setting up operations, requiring new systems), adding to demand.

In summary, Australia/New Zealand’s core banking market is in a mature, replacement-driven growth phase (steady but not explosive), whereas Southeast Asia’s is more dynamic with higher growth potential, fueled by financial sector expansion and digital entrants. ANZ banks benefit from strong existing infrastructure and are focusing on modernization for efficiency and product agility. Southeast Asian banks, on the other hand, are often building new capabilities outright – catching up or even leapfrogging – which translates to potentially faster growth in core banking investments.

The table below encapsulates some of the comparative points between the two regions:

Factor Australia & New Zealand Southeast Asia (Singapore, Thailand, Vietnam)
Market Maturity Very high – nearly 100% banked population, few new banks forming. Core projects are mainly replacements or upgrades in established banks. Mixed – ranges from mature (Singapore) to developing (Vietnam). New banks are being licensed (e.g. virtual banks), adding greenfield core implementations.
Core Market Growth Moderate 12–13% CAGR in software spend (Australia) (Australia Core Banking Software Market Size & Outlook, 2030); growth driven by tech refresh cycles. Total market size relatively small (hundreds of $M annually). Generally higher growth trajectory. Emerging markets show strong double-digit growth as many banks invest for the first time. Vietnam and others aggressively modernizing (94% of banks cite urgency) ([Core Banking Market in Vietnam, Marketing Strategies and Competitive Landscape
Key Growth Drivers Legacy replacement (aging mainframes -> modern core), digital channel demands from customers, and regulatory compliance (open banking, resilience). Competition is primarily incumbent vs incumbent, so efficiency and CX are drivers. Financial inclusion & competition – regulators enabling new entrants (digital banks in SG (Singapore’s Trust Bank taps Thought Machine for core banking tech), TH (Thailand Greenlights Three Digital Banks in FinTech Shake-Up)) pushing incumbents to upgrade. Also high customer growth in emerging economies and desire to leapfrog to digital-first services.
Technology Adoption Moving steadily to cloud/hybrid cloud cores, but often incrementally. Emphasis on integrating new modules (e.g. real-time payments) with stable legacy cores in interim. Cautious approach due to system criticality. Some banks skipping legacy tech entirely, going straight to cloud-native cores. Regulators increasingly open to cloud deployments ([Core Banking Market in Vietnam, Marketing Strategies and Competitive Landscape
Regulatory Environment Strong oversight (APRA, RBNZ) focusing on stability. Open Banking mandated in AU (since 2020) drives API capabilities (Australian banking market ready for core systems change - Pismo). No direct mandate to replace cores, but implicit pressure via operational risk standards. Proactive stance to boost innovation: new licenses come with expectation of innovative tech. E.g. Thai virtual banks must use innovative tech to reach the underbanked (Bank of Thailand sticks to 3 virtual bank licences - Bangkok Post). Regulators encourage modernization to support digital economy goals.
Vendor/Integrator Opportunity Limited number of large banks – each big core deal is huge but infrequent. Vendors face long sales cycles; SIs compete for a few big projects (e.g. one Big4 bank core replacement could be a once-in-decades event). Smaller bank segment provides continuous but smaller opportunities. Many banks at various stages of core upgrade – a broad base of opportunities. Multiple mid-sized banks and new banks seeking solutions simultaneously. Vendors can win many smaller deals that add up. SIs can partner across countries; local tech talent gaps mean outside integrators are welcomed.

Both regions will continue to invest in core banking transformation, but Southeast Asia’s banking market is expected to grow faster in terms of new core system adoptions. Australia and New Zealand, while growing more slowly, will still see significant modernization given the critical importance of banking (and the need to keep up with global digital banking standards). In fact, ANZ banks often observe the SEA experiments – for instance, seeing Singapore’s successful digital bank launches on cloud cores provides a valuable case study that may eventually encourage more aggressive moves in Australia’s big banks. Conversely, the experience of Australia’s large banks in executing massive core projects (CBA’s success, NAB’s challenges) offers lessons to banks in developing markets.

In conclusion, Australia and New Zealand present a stable but innovation-focused core banking market, whereas Southeast Asia offers a rapidly expanding and evolving landscape. A vendor or integrator evaluating these markets would find higher immediate growth potential in Southeast Asia, but also must navigate diverse requirements country by country. Meanwhile, the ANZ market, though slower, cannot be ignored – the deals there are large and the banks are often regional trendsetters in banking technology. Both regions are ultimately converging toward the same vision: modern, flexible core banking systems enabling the digital banking era, but they are starting from different points on the curve and moving at different speeds.

Sources:

  1. Grand View Research – Australia Core Banking Software Market Outlook (Australia Core Banking Software Market Size & Outlook, 2030) (Australia Core Banking Software Market Size & Outlook, 2030)
  2. FinTech Futures – ANZ New Zealand selects FIS Modern Banking Platform (ANZ New Zealand selects FIS for core banking upgrade); ANZ CIO on legacy core (Hogan) (ANZ CIO says old core banking system “not a hindrance”)
  3. FinTech Futures – June 2024 Core Banking Tech stories (Flexcube replacing Ultracs at TISA) (June 2024: Top five core banking stories of the month) (June 2024: Top five core banking stories of the month)
  4. iTnews – CBA’s Core Modernisation (SAP+Accenture) (CBA unfazed by non-exclusive core banking deal - iTnews)
  5. Thought Machine – Trust Bank (Singapore) selects Vault core (Singapore’s Trust Bank taps Thought Machine for core banking tech) (Singapore’s Trust Bank taps Thought Machine for core banking tech)
  6. Thought Machine – Judo Bank goes live on Vault (The Fintech Times) (Australia’s Judo Bank Goes Live with Thought Machine’s Vault Core | The Fintech Times)
  7. 10x Banking – Alliance with Deloitte Australia for mutual banks (10x and Deloitte deliver digital transformation to mutuals in Australia)
  8. Apps Run The World – Westpac NZ selects Finacle (2020) (Westpac NZ selects Infosys Finacle for Core Banking)
  9. FinTech Futures – Reserve Bank of Australia selects TCS BaNCS (TCS Bancs wins AU$13.6m core banking system contract with Reserve Bank of Australia)
  10. FinTech Futures – Kiwibank SAP core project delays (Kiwibank’s SAP core banking system overhaul faces delays and budget increase)
  11. Medium (Victor Leung) – Vietnam Core Banking Market Overview (modernization urgency, market share of vendors) (Core Banking Market in Vietnam, Marketing Strategies and Competitive Landscape | by Victor Leung | Apr, 2025 | Medium) (Core Banking Market in Vietnam, Marketing Strategies and Competitive Landscape | by Victor Leung | Apr, 2025 | Medium)
  12. Nation Thailand – Thailand greenlights 3 virtual banks (2025) (Thailand Greenlights Three Digital Banks in FinTech Shake-Up) (Thailand Greenlights Three Digital Banks in FinTech Shake-Up)
  13. Basiq/Pismo – Open Banking Australia arrival in 2020 (Australian banking market ready for core systems change - Pismo)
  14. Computerworld – How 86 400 built a cloud-native bank (Data Action core) (How 86 400 built a cloud-native bank – Computerworld)
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  • 1 min read

澳洲與紐西蘭核心銀行市場研究報告

澳洲的核心銀行軟體市場在2024年約為4.8億美元，預計到2030年將成長至9.6億美元，年複合成長率（CAGR）約為12.7%。 紐西蘭市場規模較小，但成長趨勢相似。兩國的銀行滲透率極高，成長動力主要來自技術升級與老舊系統更換。

關鍵趨勢

  • 核心現代化與雲端遷移
  • 數位銀行與新創銀行興起
  • 開放銀行（Consumer Data Right）推進
  • 監管合規與資安要求提升

競爭格局

市場呈現老牌供應商與新興雲端供應商並存的局面。大型銀行核心更換週期長，中小型銀行則提供持續機會。

澳洲與紐西蘭的主要核心銀行系統供應商

傳統核心平台供應商

  • Temenos：在澳紐與東南亞地區擁有高市佔率，積極推動SaaS轉型。
  • Oracle FSS（Flexcube）：大型銀行核心升級的主力供應商。
  • Finastra：滲透於支付領域與小型銀行。
  • FIS（Modern Banking Platform）：進軍亞太市場，獲得ANZ NZ採用。
  • Infosys Finacle：數位渠道整合強項，支援Westpac NZ核心升級。
  • TCS BaNCS：中型銀行與中央銀行（如RBA）選用。
  • 本地供應商 Ultradata、Data Action：服務中小型金融機構。

新興雲端核心銀行平台

  • 10x Banking：與Westpac、Deloitte合作，主攻BaaS與信用社市場。
  • Thought Machine（Vault Core）：服務Judo Bank與其他新創銀行。
  • Mambu：快速部署型SaaS核心，支援新創金融科技企業。

澳洲與紐西蘭的核心銀行系統整合商

主要系統整合商

  • Accenture：大型專案首選，如CBA核心轉型案。
  • Deloitte：10x Banking夥伴，活躍於信用合作社與中型銀行。
  • Capgemini：支援Finacle、Temenos等系統導入。
  • IBM Consulting：傳統主機系統維護與中介層升級。
  • TCS、Infosys、Wipro：自家產品導入與第三方整合服務。
  • DXC Technology：Hogan核心維護與外包服務。
  • 專精型金融科技整合商（Rubik Financial、Xpert Digital）：中小型銀行市場專家。

市場機會與競爭動態

  • 大型核心更換專案（Big4銀行）單案規模龐大。
  • 中小型機構持續升級需求穩定。
  • 全球SI與本地專家競爭激烈，專案交付能力與本地化支援成關鍵。

未來展望：技術、監管與市場動態

技術趨勢

  • 全面雲端化、微服務架構
  • 即時數據處理與AI整合
  • 核心系統兼容分布式帳本與數位資產

監管趨勢

  • 澳洲APRA推動營運韌性（CPS 230）
  • 消費者資料權利（開放銀行API）
  • 強化數據隱私與資安要求

市場動態

  • 傳統銀行與新興數位銀行雙軌並進
  • 供應商與整合商競爭加劇
  • 核心系統現代化成為市場共識

澳洲／紐西蘭 vs 東南亞市場成長比較

項目 澳洲與紐西蘭 東南亞（新加坡、泰國、越南）
市場成熟度 高度成熟，核心升級為主 成長中，綠地市場機會多
成長速度 CAGR約12.7% CAGR約18%以上
成長驅動因素 系統老化替換、數位渠道需求 金融普及、虛擬銀行新設
技術採用 雲端混合模式、穩健升級 雲端原生快速普及
監管政策 間接促進數位化（開放銀行） 積極推動創新與普惠金融
供應商與整合商機會 少量大型專案，競爭激烈 多元中型專案，遍佈多國市場
  • 4 min read

Vibe Coding - A New Era of AI-Accelerated Software Development

Software development is undergoing a major transformation. With the rise of large language models (LLMs), developers are adopting a new methodology called Vibe Coding — a conversational, iterative process where AI plays a central role in moving ideas into working software efficiently. At its core, Vibe Coding emphasizes logical planning, leveraging AI frameworks, continuous debugging, checkpointing, and providing clear context to AI tools. It focuses on speed, experimentation, and AI-human collaboration.

Vibe Coding, or vibecoding, is a modern approach to software development that uses natural language prompts to instruct AI systems to generate code. The term was coined by computer scientist Andrej Karpathy in February 2025 and quickly gained widespread adoption across the tech industry. Vibe Coding aims to minimize manual coding by relying heavily on AI coding assistants like ChatGPT, Claude, Copilot, and Cursor.

In practice, users describe the desired functionality in plain language. AI interprets these prompts and generates code automatically. Users test the output, troubleshoot by interacting with the AI, and iterate until the software operates as expected. This highly conversational approach centers around collaboration with AI, with Karpathy summarizing the experience as: "I just see things, say things, run things, and copy-paste things, and it mostly works."

Several key principles define the Vibe Coding mindset. It prioritizes natural language input over manual code writing, trusts the AI to handle the majority of development work, and favors rapid prototyping over immediate code perfection. The goal is to build a working version first, refine only when necessary, and accept that some imperfection is tolerable — particularly for non-critical or experimental projects. Vibe Coding also lowers the barrier to entry, making it possible for even beginners to create functional software.

Typical use cases for Vibe Coding include rapid prototyping of new ideas, building small personal productivity tools, learning new frameworks or programming languages with AI guidance, and accelerating minimum viable product (MVP) development for startups and small teams. However, it also carries limitations. AI-generated code may be messy or inefficient. Debugging can be more difficult when the user doesn't deeply understand the AI-written code. Vibe Coding is not recommended for production-grade systems that require high reliability, security, and maintainability. Overreliance on AI outputs without human review can introduce significant risks.

Compared to traditional AI-assisted programming, Vibe Coding involves deeper trust in the AI system. In Vibe Coding, users allow the AI to generate most or all of the code, perform minimal code review, and focus primarily on achieving working results quickly. In traditional AI-assisted coding, the human developer remains in control, uses AI mainly as a helper, conducts thorough reviews, and maintains responsibility for the final product. While Vibe Coding suits fast-moving projects and non-critical applications, traditional coding remains essential for production systems.

To succeed with Vibe Coding, developers need several core skills. Logical planning is crucial — clearly structuring what needs to be built before starting prompts. Awareness of AI-friendly frameworks like Rails, Django, and Next.js enables faster development. Frequent checkpointing using Git or cloud snapshots ensures stability and reduces the risk of irreversible mistakes. Developers must maintain discipline in debugging, often resetting to clean baselines to prevent technical debt. Context management is equally critical: providing the AI with full project context, documentation, and environment details significantly improves code generation accuracy.

Selecting the right tools also plays a major role. Cursor offers a deep AI integration experience inside a professional, local environment ideal for more serious projects. Windsurf is optimized for rapid prototyping and fast-paced prompting. Replit provides instant online coding, strong multiplayer capabilities, and is perfect for collaborative experiments and demos.

Tom Blomfield, a partner at Y Combinator, shares advanced Vibe Coding techniques that emphasize planning, testing, and modularity. Developers are encouraged to plan project structures in markdown before coding, prioritize integration tests over unit tests, and use AI across the stack for tasks like hosting and asset generation. When encountering problems, switching between LLMs (such as Gemini, Claude, or Sonnet) can be highly effective. Voice input and screenshots can accelerate communication with AI, and keeping the code modular — with small, clean files — supports easier collaboration between humans and AI. Regular refactoring is necessary to maintain code quality even as prototypes grow.

The Vibe Coding workflow is straightforward: describe the intended functionality clearly to the AI, generate the implementation, test the output, debug collaboratively if needed, save progress, and repeat. This iterative loop enables developers to build complex applications faster without being constrained by traditional coding bottlenecks.

Vibe Coding is reshaping the software development landscape by making building software faster, more accessible, and more experimental. It enables quick exploration of ideas at low cost but demands careful oversight to ensure that quality, security, and maintainability are not compromised. While Vibe Coding is highly effective for rapid prototyping, side projects, learning exercises, and early-stage MVPs, traditional coding practices remain indispensable for mission-critical and enterprise-grade applications. By mastering both the advantages and limitations of Vibe Coding, developers can unlock new levels of productivity and innovation in modern software development.

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  • 1 min read

Vibe Coding - AI加速軟體開發的新時代

軟體開發正在經歷一場重大轉變。隨著大型語言模型（LLMs）的興起，開發者正在採用一種名為 Vibe Coding 的新方法論——這是一種以對話和迭代為核心，讓AI在將想法轉化為可運作軟體過程中扮演關鍵角色的開發方式。本質上，Vibe Coding 強調邏輯規劃、活用AI框架、持續除錯、建立檢查點，以及向AI工具提供明確上下文。它聚焦於速度、實驗性與AI與人類之間的協作。

Vibe Coding，或稱為 vibecoding，是一種現代化的軟體開發方法，透過自然語言提示來指導AI系統產生程式碼。這個術語由電腦科學家 Andrej Karpathy 於2025年2月提出，並迅速在科技界廣泛傳播。Vibe Coding 的目標是大量減少手動編碼，依賴如 ChatGPT、Claude、Copilot 和 Cursor 等AI編碼助手。

在實踐中，使用者以自然語言描述希望軟體具備的功能，AI解讀這些指示並自動生成程式碼。使用者測試輸出結果，與AI互動進行除錯，並反覆迭代，直到軟體按預期運作。這種高度對話式的方法以與AI的協作為中心，Karpathy 將這種經驗總結為：「我只看到事情、說出需求、執行程式、複製貼上，結果大多能運作。」

Vibe Coding 的心態由幾個關鍵原則定義。它優先考慮以自然語言輸入需求，而非手動撰寫程式碼，信任AI負責大部分開發工作，並且重視快速原型製作而非一開始就追求完美。目標是先構建出能運作的版本，僅在必要時進行細部優化，並接受一定程度的瑕疵，特別是在非關鍵或實驗性專案中。此外，Vibe Coding 降低了軟體開發的門檻，讓即使是初學者也能創造出功能性軟體。

Vibe Coding 的典型應用場景包括新想法的快速原型開發、小型個人效率工具的構建、在AI指導下學習新框架或程式語言，以及加速新創公司和小團隊的MVP（最小可行產品）開發。然而，它也有局限性。AI生成的程式碼可能混亂或低效，當使用者無法深刻理解AI編寫的程式時，除錯可能更加困難。對於需要高度可靠性、安全性和可維護性的生產等級系統，並不建議採用Vibe Coding。過度依賴未經充分審查的AI輸出，亦可能帶來重大風險。

與傳統的AI輔助程式設計相比，Vibe Coding 涉及更高程度的對AI系統的信任。在Vibe Coding中，使用者允許AI生成大部分甚至全部程式碼，進行最小限度的人工審查，並專注於快速實現可運作的成果。而在傳統AI輔助編碼中，開發者仍然掌握主導權，將AI作為輔助工具，並且嚴格進行代碼審查，對最終產品負責。儘管Vibe Coding適合快速推進的項目和非關鍵應用，傳統的編碼方法在生產系統中依然不可或缺。

為了成功運用Vibe Coding，開發者需要具備幾項核心技能。邏輯規劃至關重要——在開始提示之前，清楚地規劃要構建的內容。了解如 Rails、Django、Next.js 等對AI友善的框架，可以加速開發進程。透過Git或雲端快照頻繁建立檢查點，能確保穩定性並降低不可逆錯誤的風險。開發者必須在除錯時保持紀律，經常回到乾淨的基礎狀態以防止技術債堆積。上下文管理同樣關鍵：向AI提供完整的專案背景、相關文件及環境細節，可顯著提升生成程式碼的準確性。

選擇合適的工具亦扮演重要角色。Cursor 提供在專業本地環境中與AI深度整合的體驗，適合需要專注開發的項目。Windsurf 則針對快速原型開發和高頻率提示優化，非常適合進行實驗。Replit 則提供即時線上編碼和強大的多人協作能力，非常適合用於共同實驗和展示原型。

來自 Y Combinator 的合夥人 Tom Blomfield 分享了進階的 Vibe Coding 技巧，強調規劃、測試與模組化的重要性。他建議開發者在編碼前用Markdown規劃好專案結構，優先考慮整合測試而非單元測試，並在各層面上善用AI（如網站託管、資產生成等）。遇到問題時，切換不同的LLM（如Gemini、Claude或Sonnet）往往能找到更好的解法。利用語音輸入和截圖工具（如Aqua）可以加速與AI的溝通。同時，保持程式碼的模組化（小且清晰的檔案）有助於人與AI的協作，即使專案規模擴大，也能透過定期重構維持程式品質。

Vibe Coding 的工作流程十分直接：清晰地向AI描述功能需求，生成初步實作，測試結果，必要時與AI協作除錯，儲存進度，然後重複這個循環。這種迭代流程讓開發者能夠更快速地建構複雜應用程式，而不受傳統開發瓶頸的限制。

Vibe Coding 正在重塑軟體開發的格局，使建構軟體變得更快速、更具可及性與更具實驗性。它讓開發者能以低成本迅速探索各種創意，但也需要謹慎管理，以確保品質、安全性與可維護性不被犧牲。雖然Vibe Coding非常適合用於快速原型、個人專案、學習練習和早期MVP開發，但對於任務關鍵型或企業等級的應用，傳統的編碼實踐依然至關重要。透過理解並掌握Vibe Coding的優勢與限制，開發者能在現代軟體開發中解鎖更高的生產力與創新力。

  • 4 min read

Building Code Agents with Hugging Face smolagents

In the fast-evolving world of AI, agents have emerged as one of the most exciting frontiers. Thanks to projects like Hugging Face's smolagents, building specialized, secure, and powerful code agents has never been easier. In this post, we'll walk through the journey of agent development, explore how to build code agents, discuss secure execution strategies, learn how to monitor and evaluate them, and finally, design a deep research agent.

A Brief History of Agents

Agents have evolved dramatically over the past few years. Early LLM applications were static: users asked a question; models generated an answer. No memory, no decision-making, no real "agency."

But researchers dreamed of more: systems that could plan, decide, adapt, and act autonomously.

We can think of agency on a continuum:

  • Level 0: Stateless response (classic chatbots)
  • Level 1: Short-term memory and reasoning (ReAct pattern)
  • Level 2: Long-term memory, dynamic tool use
  • Level 3: Recursive self-improvement, autonomous goal setting (still experimental)

Early attempts at agency faced an "S-curve" of effectiveness. Initially, more agency added more confusion than benefit. But with improvements in prompting, tool use, and memory architectures, we're now climbing the second slope: agents are finally becoming truly effective.

Today, with frameworks like smolagents, you can build capable agents that write, execute, and even debug code in a secure and monitored environment.

Introduction to Code Agents

Code agents are agents specialized to generate and execute code to achieve a goal. Instead of just answering, they act programmatically.

Let's build a basic code agent with Hugging Face's smolagents:

from smolagents import Agent

agent = Agent(system_prompt="You are a helpful coding agent. Always solve tasks by writing Python code.")

response = agent.run("Write a function that calculates the factorial of a number.")

print(response)

What's happening?

  • We initialize an Agent with a system prompt.
  • We run a user query.
  • The agent responds by writing and executing Python code.

Sample Output:

def factorial(n):
    if n == 0:
        return 1
    else:
        return n * factorial(n-1)

Secure Code Execution

Running arbitrary code is risky. Even a well-meaning agent could:

  • Try to use undefined commands.
  • Import dangerous modules.
  • Enter infinite loops.

To build safe agents, we must:

  1. Capture Exceptions:
try:
    exec(agent_code)
except Exception as e:
    print(f"Error occurred: {e}")

  1. Filter Non-Defined Commands:

  2. Use a restricted execution environment, e.g., exec with a sanitized globals and locals dictionary.

  3. Prevent OS Imports:

  4. Scan code for forbidden keywords like os, subprocess, etc.

  5. Or disable built-ins selectively.

  6. Handle Infinite Loops:

  7. Run code in a separate thread or process with timeouts.

  8. Sandbox Execution:

  9. Use Python's multiprocessing or even Docker-based isolation for truly critical applications.

Example Secure Exec:

import multiprocessing

def safe_exec(code, timeout=2):
    def target():
        try:
            exec(code, {"__builtins__": {"print": print, "range": range}})
        except Exception as e:
            print(f"Execution error: {e}")

    p = multiprocessing.Process(target=target)
    p.start()
    p.join(timeout)
    if p.is_alive():
        p.terminate()
        print("Terminated due to timeout!")

Monitoring and Evaluating the Agent

Good agents aren't just built; they are monitored and improved over time.

Enter Phoenix.otel — an open telemetry-based tool to monitor LLM applications.

Key Metrics to Track:

  • Latency (response time)
  • Success/Error rates
  • Token usage
  • User feedback

Integration Example:

from phoenix.trace import init_tracing

init_tracing(service_name="code_agent")

# Your agent code here
agent.run("Write a quicksort algorithm.")

With this, every agent interaction is automatically traced and sent to your telemetry backend.

You can visualize execution traces, errors, and resource usage to continuously fine-tune the agent.

Building a Deep Research Agent

Sometimes, writing code isn't enough — agents need to research, retrieve information, and act based on live data.

We can supercharge our code agent with Tavily Browser, a retrieval-augmented generation (RAG) tool that lets agents browse the web.

Example:

from smolagents import Agent
from tavily import TavilyBrowser

browser = TavilyBrowser()
agent = Agent(
    system_prompt="You are a deep research coding agent.",
    tools=[browser]
)

response = agent.run("Find the latest algorithm for fast matrix multiplication and implement it.")
print(response)

Now your agent can:

  • Search academic papers.
  • Extract up-to-date methods.
  • Code the solution dynamically.

Building agents that combine reasoning, execution, and real-world retrieval unlocks a whole new level of capability.

Final Thoughts

We are entering a new era where agents can autonomously reason, code, research, and improve.

Thanks to lightweight frameworks like Hugging Face's smolagents, powerful browsing tools like Tavily, and robust monitoring with Phoenix.otel, building secure, powerful, and monitored code agents is now within reach for any developer.

The frontier of autonomous programming is wide open.

What will you build?

  • in zh
  • 2 min read

使用 Hugging Face smolagents 建立程式代理人

在快速演進的 AI 世界中，代理人（Agents） 成為最令人興奮的前沿領域之一。多虧了 Hugging Face 的 smolagents，現在建立專業化、安全且功能強大的程式代理人變得前所未有地簡單。在本文中，我們將探索代理人發展歷程、學習如何建立程式代理人、討論安全執行策略、了解如何監控與評估代理人，最後設計一個深入研究型的代理人。

代理人簡史：走向更高自主性的道路

代理人在過去幾年中經歷了巨大的演變。早期的 LLM 應用是靜態的：用戶提問，模型回答。沒有記憶、沒有決策、也沒有真正的 "自主性"。

但研究人員渴望更多：能夠規劃、決策、適應、並自主行動的系統。

我們可以將自主性視為一個連續光譜：

  • Level 0：無狀態回應（傳統聊天機器人）
  • Level 1：短期記憶與推理（ReAct 模式）
  • Level 2：長期記憶、動態工具使用
  • Level 3：遞迴自我改進、自主設定目標（仍在研究中）

早期的代理人嘗試面臨 "S 曲線" 效益挑戰。最初，自主性增加反而帶來更多混亂。但隨著提示工程、工具使用與記憶架構的進步，我們正攀登第二段斜坡：代理人終於變得真正有效。

今天，藉由像 smolagents 這樣的框架，你可以輕鬆建立能撰寫、執行、甚至除錯程式碼的代理人。

介紹程式代理人（含範例）

程式代理人 是專門用來生成並執行程式碼以達成目標的代理人。他們不只是回答，而是以程式行動。

讓我們用 Hugging Face 的 smolagents 建立一個基本的程式代理人：

from smolagents import Agent

agent = Agent(system_prompt="You are a helpful coding agent. Always solve tasks by writing Python code.")

response = agent.run("Write a function that calculates the factorial of a number.")

print(response)

發生了什麼事？ - 初始化一個具有系統提示的 Agent。 - 使用 run 來執行使用者查詢。 - 代理人透過撰寫並執行 Python 程式碼回應。

範例輸出： 

def factorial(n):
    if n == 0:
        return 1
    else:
        return n * factorial(n-1)

安全執行程式碼

執行任意程式碼具有風險。即使是善意的代理人也可能： - 嘗試使用未定義的指令。 - 匯入危險模組。 - 進入無限迴圈。

要建立安全代理人，必須做到：

  1. 捕捉例外： 

    try:
    exec(agent_code)
except Exception as e:
    print(f"Error occurred: {e}")

  2. 過濾未定義指令：

  3. 使用受限的 globals 和 locals 字典執行 exec。

  4. 防止危險匯入：

  5. 掃描程式碼中是否包含如 os、subprocess 等危險關鍵字。

  6. 或選擇性地禁用部分 built-ins。

  7. 處理無限迴圈：

  8. 在獨立執行緒或程序中運行程式碼並設定超時。

  9. 沙箱化執行：

  10. 使用 Python 的 multiprocessing，甚至是 Docker 隔離關鍵應用。

安全執行範例： 

import multiprocessing

def safe_exec(code, timeout=2):
    def target():
        try:
            exec(code, {"__builtins__": {"print": print, "range": range}})
        except Exception as e:
            print(f"Execution error: {e}")

    p = multiprocessing.Process(target=target)
    p.start()
    p.join(timeout)
    if p.is_alive():
        p.terminate()
        print("Terminated due to timeout!")

監控與評估代理人

好的代理人不僅要建構，還要持續監控與改進。

使用 Phoenix.otel —— 一個基於 OpenTelemetry 的工具，來監控 LLM 應用程式。

需追蹤的關鍵指標： - 延遲（回應時間） - 成功／錯誤率 - Token 使用量 - 用戶回饋

整合範例： 

from phoenix.trace import init_tracing

init_tracing(service_name="code_agent")

# 你的代理人程式碼
agent.run("Write a quicksort algorithm.")

透過此方式，每次代理人互動都會自動追蹤並傳送到遙測後端。

你可以視覺化執行過程、錯誤與資源使用情況，持續優化代理人。

建立深入研究型代理人（使用 Tavily Browser）

有時候，單純撰寫程式碼還不夠 —— 代理人需要研究、檢索資訊，並基於即時資料行動。

我們可以使用 Tavily Browser 為程式代理人加持，打造檢索增強生成（RAG）能力。

範例： 

from smolagents import Agent
from tavily import TavilyBrowser

browser = TavilyBrowser()
agent = Agent(
    system_prompt="You are a deep research coding agent.",
    tools=[browser]
)

response = agent.run("Find the latest algorithm for fast matrix multiplication and implement it.")
print(response)

現在你的代理人可以： - 搜尋學術論文。 - 抽取最新的方法論。 - 動態撰寫並執行程式碼。

結合推理、執行與即時檢索的代理人，開啟了全新層級的能力。

結語

我們正進入一個代理人能自主推理、編程、研究與持續改進的新時代。

有了像 Hugging Face smolagents 這樣的輕量級框架，加上 Tavily 的強大檢索功能與 Phoenix.otel 的監控工具，建立安全、強大且可監控的程式代理人已觸手可及。

自主編程的疆界已全面展開。

你會打造什麼？