Real-Time Fraud Detection Platform

Overview

Built a production ML-powered fraud detection system processing 1M+ transactions with <500ms latency SLA, balancing accuracy improvements with real-time performance requirements.

The Challenge

Existing fraud detection models suffered from high false positive rates and couldn't meet the <500ms latency requirement needed for real-time transaction processing, forcing manual review queues that slowed customer experience.

The Approach

Led 0-to-1 development starting with problem analysis of false positives and latency bottlenecks. Partnered with data science to establish model selection framework optimizing precision/recall trade-offs. Defined <500ms latency as non-negotiable SLA while targeting 15% accuracy improvement. Architected integration with transaction pipeline infrastructure, implemented real-time scoring API, and built comprehensive monitoring dashboards for drift detection and performance tracking.

Key Outcomes

• Maintained <500ms latency while improving fraud detection accuracy by 15%
• Processed 1M+ transactions with real-time ML scoring in production
• Reduced false positive rate through precision/recall optimization
• Implemented monitoring and drift detection for model reliability
• Built model evaluation framework for ongoing performance tracking

The Result

Successfully launched real-time fraud detection serving 1M+ customers, achieving both the <500ms latency SLA and 15% accuracy improvement. Reduced false positives requiring manual review, improving customer experience while maintaining fraud protection. Established ongoing model evaluation and drift monitoring processes ensuring sustained reliability.