Case Study

Risk based pricing, driven by discovered segments

Case Studies / FinTech / Price Optimization

Risk based pricing was driven by asserted segments which led to misalignment in risk. Population distribution was skewed, and alternative variables driving risk were not incorporated.


Our goal was to transform pricing from a purely risk based approach to elasticity and adverse selection driven - Use of machine learning to discover "Homogenous" loss rate segments.


We classified borrowers into 46 Risk segments using a variety of factors (e.g. debt service coverage ratio, duration, personal and household leverage etc..). Segments discovered using ML techniques such that they are homogenous within, equally distributed and maximize risk separation.

Value Delivered

Discovered Segments - Use of Machine Learning to discover "Homogenous" loss rate segments to align pricing by risk. Modeled Adverse Selection and Price Elasticity to deliver 20% higher originations at a lower loss rate.

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