Financial Regulation

With the rapid development of financial markets, regulatory authorities face new supervisory challenges:

— Huge volume of trading data, with daily new data reaching TB level, straining traditional system processing capabilities

— Diverse patterns of abnormal trading behaviors, including matched trades, circular trades, self-dealing variations, etc.

— Extremely high real-time requirements, demanding second-level responses to market anomalies

— Complex cross-market, cross-product trading behaviors that are difficult to monitor

Core issues include:

— How to achieve real-time processing of massive trading data?

— How to accurately identify various abnormal trading patterns?

— How to balance supervisory efficiency with system performance?

Technical Architecture Upgrade: A distributed graph computing engine is adopted to support real-time data stream processing, ensuring complex graph queries and analysis are completed within seconds.

Intelligent Monitoring System: A multi-dimensional monitoring indicator framework is established, including trading frequency, amount distribution, correlation levels, etc. Machine learning algorithms automatically identify abnormal patterns.

Collaborative Supervision Mechanism: A cross-departmental information sharing platform is built to enable real-time transmission and collaborative processing of regulatory information.