AI Loan & Credit Underwriting

Overview

A loan and credit underwriting platform for lenders, fintechs, credit unions, and BNPL providers. The system pulls traditional credit data (FICO, bureau pulls) plus alternative data (cash flow analytics via Plaid or MX, payroll income verification, rent and utility payment history, employment continuity signals) and runs a calibrated default-probability model. Decisions are explainable — every approval, counter-offer, or decline ships with the top decision factors in plain English. Adverse-action notices are auto-generated when required. Fair-lending disparate-impact checks run on every decision. Compliance retention (25 years for federal loan products) is enforced automatically.

The Challenge

Loan underwriting is the most regulated AI use case in finance. Every decision has to be explainable in a way that satisfies CFPB, FCRA, ECOA, and state regulators. Disparate-impact analysis has to be built into the decisioning, not added afterwards. Adverse-action notices have to cite real reasons (not 'the model said no'). The model has to be measurably better than rules-based or traditional FICO underwriting on default outcomes, or the entire investment doesn't pencil. And the system has to be fast — applicants leave for competitors if you take more than seconds.

Our Approach

We build the decision engine on a gradient-boosted default-probability model trained on your historical loan performance data, calibrated so that score values map to actual default rates. Alternative data integrations (Plaid, MX, Argyle, payroll-verification providers) enrich the feature set. SHAP-based explainability identifies the top decision factors per application. A fair-lending pre-deployment review and ongoing disparate-impact monitoring is mandatory. Adverse-action notices auto-generate citing the top declining factors. The decision trail is retained for 25 years per federal requirements. Sub-second inference at production volume. SOC 2 and FCRA-aware architecture.

Key Features

• Real-time decisioning with sub-second inference
• Traditional + alternative data (Plaid, MX, Argyle, payroll)
• Calibrated default-probability scoring with confidence
• Top-factor explainability per decision (SHAP-based)
• Auto-generated adverse-action notices with cited reasons
• Continuous disparate-impact monitoring for fair-lending compliance
• 25-year decision retention with full audit trail
• SOC 2 and FCRA-aware architecture

Results

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