Intelligent Risk Control Platform: Design Background, Full‑Cycle Strategy and Model Management, and Business Architecture

Risk control is a common scenario in finance; the talk explores how to implement an intelligent risk control middle‑platform from both business and technical architecture perspectives.

Design Background : The platform is built around a business‑centric approach, emphasizing six characteristics—real‑time, fine‑grained, collaborative, agile, intelligent, and unified expansion—forming the "five‑all double‑core" concept (full coverage and dual engines of rules and AI).

Strategy Full‑Cycle Management : Describes human‑machine collaboration, the advantages of expert rules, when to use machine learning, and the complete lifecycle of strategy creation, versioning, testing (including A/B tests, champion‑challenger, sandbox), gray‑release, monitoring, and effect evaluation.

Model Full‑Cycle Management : Outlines data ingestion, feature engineering, algorithm selection, hyper‑parameter search, and evaluation, with AutoML handling data merging, feature generation, model training, and online/offline learning for model drift.

Business Architecture and Capability Atomization : Divides the system into data, platform, and application layers; further breaks down capabilities into infrastructure, service, and business module layers, detailing storage, container scheduling, DSL‑based metric definition, visual configuration, and modular service orchestration.

Application Cases : Shows two real‑world examples—a hard‑real‑time transaction fraud detection system achieving sub‑20 ms latency with a dual‑engine approach, and a nationwide bank’s omni‑channel anti‑fraud solution handling 500+ rules and 2,000+ real‑time metrics, intercepting nearly 10,000 high‑risk transactions per month.

The presentation concludes with a summary of the platform’s value in improving risk decision‑making through closed‑loop strategy and AI integration.