A Leading NBFC Reduced Default Rates by an Estimated 50% by Leveraging a Customized Credit Risk Assessment Application

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Custom AI Application

Customized Credit Application Scorecard

Overview of the ML Classification Attributes


(Fig i): Overview of the process improvement

Efficiency Enablement: Out of the 8 business lines, three critical lines of business were identified, and credit application scorecards were developed for the same. The dynamic nature of the business was taken into account, and observation and performance windows were chosen, accordingly. Strong data sources including those relating to application, bureau and field visits were identified to reduce dependency on incomplete data.

Optimum Deployment: A variety of math-based techniques such as binning, group-wise segmentation, and classification models like logistic regression and decision trees were developed in the process of scorecard generation.


ML-driven solutioning: By leveraging ML techniques, TheMathCompany further diversified application in application scorecards, such as boosting accuracy through surrogate models, improving model stability with ML based sampling & k-fold validation techniques.

The resulting application scorecard had a 93% model prediction accuracy, which would only improve with time. The results generated through the scorecard helped decision makers to extensively assess rejection and improve trust in credit quality. Furthermore, the time taken from information capture to response was delivered within 120 seconds. The existing default rate was 15.82% and this was brought down to an estimated 8.8%, i.e., brought down by almost 50%.