AI-Driven Early Warning System for SME Loans

The Challenge

A mid-size Indian bank faced rising defaults in its SME loan portfolio. Borrower stress was often detected only after 30–60 days past due, by which point recovery was difficult and costly.

Key barriers included:

• Monitoring spread across Excel sheets, emails, and calls with no single dashboard
• No early warning signals to flag at-risk borrowers
• Late interventions resulting in higher NPAs and wasted effort on low-risk accounts

The Opportunity

The bank had an opportunity to apply AI for proactive risk management. By detecting stress early, the bank could:

• Reduce defaults and protect its loan book
• Save crores in potential losses
• Strengthen compliance with RBI expectations
• Free staff capacity to focus only on high-risk accounts

The Approach

We designed an AI-powered early warning system with a simple traffic-light framework to make adoption easy:

• Risk scoring: Combined repayment history, account transactions, GST filings, and credit bureau data to calculate borrower risk scores weekly.