Real-Time Intelligence for Financial Services.

Our models use online learning with weekly retraining cycles. New fraud patterns are incorporated through a combination of supervised learning on confirmed fraud cases and unsupervised anomaly detection. The system adapts to emerging threats without manual rule creation.

Absolutely. We typically deploy as a scoring layer alongside existing rule engines. The ML models provide risk scores that augment your current rules, with a gradual migration path as confidence builds. No rip-and-replace required.

We provide full model explainability (SHAP values, feature importance), complete audit trails, and model validation documentation that meets OCC, FDIC, and CFPB examination standards. Our compliance team has experience supporting regulatory exams.

Fair lending is built into our model development lifecycle. We run disparate impact analysis across all protected classes, implement bias mitigation techniques during training, and provide ongoing monitoring dashboards for model fairness metrics.

Our fraud detection pipeline operates at sub-50ms latency for 95th percentile requests, processing 50K+ transactions per second. Credit scoring decisions return in under 200ms. Both are designed for real-time user experiences.