Credit-risk models cut fresh-loan defaults by 18%.

The challenge

The lender's credit assessment was manual and inconsistent: every application took days of officer review, approval criteria varied between branches, and the default rate on new loans was creeping up as volumes grew.

Worse, trouble was discovered late — the first sign of a stressed account was usually a missed payment, when the recovery options were already narrow.

The solution

We consolidated repayment history, bureau data, and application details into a clean modeling dataset, then built a credit-risk scoring model that gives every application a calibrated risk grade in seconds — as decision support for credit officers, who keep the final say.

Alongside it, an early-warning system monitors the live loan book and flags accounts showing stress patterns weeks before a missed payment, feeding a portfolio-risk dashboard the management committee reviews weekly.

• Risk-scoring model grading every application in seconds
• Credit officers keep final approval — the model is decision support
• Early-warning system watching the live loan book for stress patterns
• Portfolio-risk dashboard for weekly management review