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 design these systems 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 build for full model explainability (SHAP values, feature importance), complete audit trails, and model validation documentation designed to OCC, FDIC, and CFPB examination standards. Explainability is a first-class design requirement, not an afterthought.

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.

We architect fraud detection pipelines for sub-50ms latency at the 95th percentile and credit decisions in under 200ms — design targets we agree upfront and validate in the PROVE phase under your real transaction volumes.