Fintech

CASE STUDIES

Better risk strategy for a Fintech start-up in US.

Business Problem:

A FinTech lending start-up in the US wanted to create the underwriting algorithm for their lending platform to predict borrowers' credit worthiness.

Solution Approach:

We worked with the client to extract and merge application and loan data from their loan originations system, billing platform and external credit bureau. After performing reject inferencing, a statistical model to predict default event at application level was developed.

Result:

The predictive power of the underwriting model was much higher than industry leading generic score. We devised the loan approval strategy along with the client's risk management team and the new risk strategy reduced losses by 45% keeping the same level of approval rate. The model was a true-differentiator of the lender given the stiff competition in FinTech lending space.


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