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Case Study · AI

Fintech Risk Intelligence Engine

Background

A city commercial bank lost over ¥30M/year to credit card fraud. Traditional rule engines had high false positives and couldn't handle new fraud patterns. They wanted an AI graph neural network for smart fraud detection.

Solution

Built graph neural network anti-fraud model for transaction relationship mining. Millisecond real-time inference engine at 10K TPS. Risk dashboard with dynamic rules. Automated model monitoring and iteration.

Results

99.5%

Interception

-60%

False Positives

<50ms

Response

¥2500万

Loss Reduced

  • 欺诈交易拦截率提升至99.5%
  • 误报率降低60%
  • 风控响应时间<50ms
  • 年减少欺诈损失超2500万元

Client Feedback

HechuangDataLink's GNN risk engine brought our fraud losses to an all-time low.

Bank Risk Management GM