Custom Transformer-Based Anomaly Detection
A global financial services provider processing 50M+ transactions daily faced critical challenges with their legacy fraud detection system, resulting in operational and reputational risks:
Architected a production-grade transformer-based anomaly detection engine deployed on Azure AI infrastructure with real-time inference capabilities. The system processes transaction streams at scale while maintaining explainability for regulatory compliance.
Core technical implementation:
Built scalable ETL pipeline processing 50M+ daily transactions into feature vectors. Engineered 200+ behavioral features including velocity metrics, merchant patterns, geolocation signals, and temporal sequences. Implemented real-time feature store using Redis for sub-millisecond feature lookups during inference.
Designed specialized transformer model with multi-headed self-attention optimized for sequential transaction data. Implemented positional encoding for temporal patterns and attention masking for variable-length sequences. Model architecture balances accuracy with inference latency requirements (<100ms per transaction).
Developed LSTM-based user behavioral models that learn spending patterns over 90-day windows. System generates user-specific risk profiles and detects anomalies based on deviation from established patterns. Handles concept drift through continuous retraining pipelines.
Deployed model serving layer on Azure AKS with GPU-accelerated inference nodes. Implemented request batching, model quantization (INT8), and ONNX Runtime optimizations achieving <50ms p99 latency. Built fallback mechanisms ensuring 99.99% availability during model updates.
Integrated SHAP (SHapley Additive exPlanations) for model interpretability. Every fraud prediction generates feature attribution scores explaining the decision. Built audit trails logging all predictions with reasoning for regulatory compliance (SOX, PCI-DSS, GDPR).
Established full MLOps pipeline with automated retraining, A/B testing, and gradual rollout mechanisms. Implemented shadow mode deployment allowing new models to run in parallel before production cutover. Built monitoring dashboards tracking model drift, prediction distribution, and business metrics.
60% reduction in false positive rate
Improved customer experience
Real-time transaction scoring
Novel fraud pattern identification
Production availability SLA
Reduced fraud losses and operational costs
Multi-headed self-attention mechanism that captures complex transaction patterns and contextual relationships across merchant categories, amounts, and temporal sequences.
High-performance feature extraction system computing 200+ behavioral signals in real-time, with Redis-backed feature store for sub-millisecond lookups during inference.
SHAP-based explainability layer providing feature attribution for every prediction, ensuring regulatory compliance and building trust with fraud analysts.
I specialize in building production-grade systems that solve complex operational problems. Let's discuss how I can help architect your solution.