Problem Statement
Rule-based fraud detection systems generate 70% false positives on 1.2M+ daily transactions, causing customer friction, while manual compliance reporting requires 240 analyst-hours monthly across 12+ jurisdictions.
Solution Architecture
Deployed vector search architecture analyzing transaction patterns, behavioral data, and historical fraud cases using custom embeddings trained on 50M+ labeled transactions (512-dimensional vectors). Real-time anomaly detection operates at <50ms p99 latency through distributed inference infrastructure processing 5M+ API calls daily with 99.99% availability. Agentic compliance workflows automate regulatory reporting across multiple jurisdictions, generating reports in 8 hours that previously required 240 hours, while risk assessment agents analyze 500K+ customer profiles to generate real-time risk scores.
Impact Metrics
Fraud detection precision improved to 95.2% with 92.8% recall, reducing false positives by 70% compared to rule-based systems
Compliance reporting time reduced from 240 hours to 8 hours monthly (97% reduction) while maintaining 100% regulatory accuracy across 12+ jurisdictions
Annual cost savings of $2.4M from reduced manual review costs ($1.2M) and improved customer experience ($1.2M retention value)
Transaction monitoring detects anomalies in real-time with 94.7% accuracy, flagging suspicious patterns 3-5 days faster than human analysts
AML screening processes 800K+ transactions monthly, identifying 2,400+ suspicious activities with 88% true positive rate
Credit losses reduced 18% through proactive risk intervention enabled by real-time risk scoring across 500K+ customer profiles
Distributed inference infrastructure handles 1.2M+ transactions daily across 45 countries with regional data residency compliance
Technology Stack
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