Real-Time Fraud Detection Engine Powered by LLMs
Built a next-generation fraud detection system using transformer-based models and real-time streaming data, reducing fraud losses by 74% while cutting false positives in half.
Key Results
74% reduction in fraud-related losses within 90 days
52% decrease in false-positive transaction flags
Sub-50ms inference latency on 2M+ daily transactions
Model accuracy improved 18% quarter-over-quarter through automated retraining
The Challenge
FinSecure was processing over 2 million transactions per day with a legacy rules-based fraud system that generated an unacceptably high false-positive rate, freezing legitimate customer accounts and damaging user trust. The system also missed novel fraud patterns that didn't match hardcoded rules.
Our Solution
We designed and deployed a multi-layer AI pipeline combining real-time feature engineering, a fine-tuned transformer model for behavioral pattern analysis, and an LLM-powered anomaly explanation layer. The system runs at sub-50ms latency on streaming data via Apache Kafka, with continuous model retraining using flagged transactions as feedback loops.
Technologies Used
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