The Challenge

A major fintech lender processed over 50,000 credit applications monthly. Each required manual review of financial documents, employment verification, and risk assessment. The average processing time was 72 hours, leading to customer drop-off and competitive disadvantage.

The Solution

We deployed a fine-tuned LLM pipeline that could parse, understand, and assess credit applications in natural language. The system was trained on 5 years of historical approvals and denials, learning the nuanced decision patterns of senior underwriters.

  • Document Intelligence: Automated extraction from bank statements, tax returns, and employment letters.
  • Risk Scoring Engine: Multi-factor analysis combining traditional credit metrics with LLM-derived behavioral signals.
  • Human-in-the-Loop: Flagged edge cases for senior review while auto-approving clear-cut applications.

The Results

Processing time dropped from 72 hours to under 4 hours for 85% of applications. The team saved over 400 hours of manual review work each month. Default rates remained stable, confirming the model's accuracy matched human judgment.