Tracks major fintech trends, focusing on fintech management, technology development, IT operations, information security, indigenous innovation, data governance, and business innovation. Aims to promote integrated industry‑academia‑research‑application development, offering a sharing platform for tech practitioners and valuable insights for institutional decision‑makers.
The article analyzes the challenges of testing response codes in a micro‑service‑based credit‑card core system, proposes a standardized "govern‑walk‑verify‑monitor" methodology with layered validation, reports quantitative risk‑reduction results, and outlines future AI‑driven testing directions.
The article analyzes the increasing complexity and cost of commercial banks’ agency treasury payment systems, identifies three core challenges—demand‑standardization conflict, high R&D expenses, and multi‑dimensional compliance—and proposes a three‑dimensional fine‑grained management framework with demand mapping, component reuse, low‑code, and proactive monitoring to improve precision and efficiency.
The article analyzes common challenges in bank batch scheduling task testing, proposes a four‑step “classify‑check‑plan‑execute” standardized framework, demonstrates its application on a credit‑card core system, and reports a 30‑fold reduction in testing cycle time and 30% efficiency gain.
The article analyzes how the Trump government’s policy shifts aim to replace a conventional digital dollar with a blockchain‑based “new dollar” built on crypto reserves, stablecoins, and tokenized assets, outlining its structure, features, and strategic goals for preserving U.S. monetary dominance.
The article analyzes how Chinese commercial banks are addressing AI data governance and domestic‑technology (信创) challenges by replacing server‑local disk replicas with professional SAN storage integrated into a true compute‑storage‑separated architecture, highlighting survey results, performance benchmarks, and ecosystem collaborations that demonstrate improved stability, latency, and scalability.
The Postal Savings Bank built a domain‑specific legal large model using a fine‑tuned base LLM, RAG‑enhanced retrieval and LoRA incremental pre‑training, cutting contract‑review time by more than 50%, standardising opinions and enabling a scalable intelligent compliance platform for the banking industry.
The paper presents a systematic testing framework for large‑model interaction capabilities, identifies five unique testing challenges, defines four core abilities (basic dialogue, information extraction, intent recognition, process decision), proposes a semantic‑vector automated verification method, and reports pilot results showing significant efficiency gains and defect detection improvements in a banking context.
The article details how Postal Savings Bank designed and deployed a fully autonomous, distributed‑unit remittance platform with five‑level modeling and an intelligent routing engine, achieving 99.99% availability, billions of annual transactions, second‑level latency, and a roadmap toward AI‑driven self‑evolving finance.
The Postal Savings Bank of China built an AI‑driven, engineering‑plus‑agent framework that evaluates large models, decouples generation from formatting, integrates a knowledge‑base with RAG, and delivers a pilot‑tested service that boosts test case adoption rates by up to 3.7 times while cutting manual effort to hours.
The article analyzes the 2023 Ministry of Finance interim measures that recognize data resources as assets, outlines the four prerequisite conditions and key accounting steps, and explains how proper data‑asset entry can reshape banks' balance sheets, decision‑making and business models in the digital economy.
