AI Loan & Credit Underwriting

Overview

A fintech lender processing small business loans was stuck in a manual underwriting process that took 5 days, cost $200 per application, and still resulted in a 6% default rate. They couldn't scale without proportionally scaling their underwriting team. We built an AI underwriting system that evaluates applications in seconds using traditional credit data enhanced with alternative data sources and behavioral signals.

The Challenge

Small business lending is inherently risky — many applicants have thin credit files. The AI needed to make better risk assessments than human underwriters while processing applications in seconds instead of days. It needed to incorporate alternative data (bank account cash flows, online presence, industry trends) without introducing regulatory risk. Explainability was critical — adverse actions require legally compliant reason codes.

Our Approach

We built a multi-factor risk model that combines traditional credit data with alternative signals: bank account transaction analysis (via Plaid), business revenue patterns, industry risk factors, and digital footprint indicators. The model produces a risk score with explainable factors that map to compliant adverse action codes. Low-risk applications auto-approve instantly; moderate-risk gets enhanced review with AI-pre-filled analysis; high-risk auto-declines with explanation. The model continuously improves from loan performance data.

Key Features

• Instant underwriting decisions (<10 seconds)
• Alternative data risk assessment
• Bank account transaction analysis
• Industry-adjusted risk scoring
• Compliant adverse action reason codes
• Continuous model improvement from loan performance
• Fraud detection integrated into underwriting

Results

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