Financial Services
97% Fraud Detection Accuracy for a Global Investment Bank
96.8%
detection accuracy (up from 87%)
-73%
false positive rate
$1.46M
annual investigation savings
1.8 sec
per transaction (down from 12 hrs)
Challenge
A tier-1 investment bank's compliance team was manually reviewing 40,000+ transactions per day. Detection accuracy sat at 87%. False positives cost $2M+ annually in investigation overhead.
Solution
We architected a real-time fraud detection system combining graph neural networks with LLM-based transaction narrative analysis. The model trained on 5 years of anonymized transaction data and deployed into the bank's existing risk infrastructure in 16 weeks.
Technologies Applied
Large Language ModelsGraph Neural NetworksKafkaReal-time Feature StoreMLOps
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