Client is one of India’s premier Housing Finance Company providing low ticket size home loans to priority sector in India. Priority sector is classified as a relatively unbanked segment with a non-steady income stream. Through its operations, the client wanted to impact millions at the bottom of the pyramid.
Technology plays a very critical role in accessing and managing the risk of lending to priority segment.
Client has in place extensive lending risk evaluation algorithms that have evolved through its many years of operations. These algorithms have extensive rules for various geographies, income segments to access default event in near future and have held really well. One of the risk metrics for any loan is 3 consecutive payment defaults within the first year of disbursement. Portfolio default rate stood at 6 % translating into Non-Performing Assets to a tune of 3M USD.
It was desired to improve detection of risky applicants at the time of loan origination process using Machine Learning.
Risk detection platform with Machine Learning algorithms at the backend for detecting 3 consecutive payment defaults within the first year of disbursement. Platform generated default score between 0-100, where higher the score, higher the chances of 3 consecutive defaults in the first year.
Key features of the platform:
- Robust Machine Learning Algorithms built on three years of historical loan portfolio data and tested for different population segments.
- Ingests approx 200 structured and 30 plus unstructured consumer application data points.
- Machine Learning algorithms monitored periodically for any new risk patterns.
- Decrease in portfolio default rate from 6% to 2% over the period of 18 months.
- Loan disbursements increased by 10 % to 55M USD.