Monsoon CreditTech's technology has been able to help a diverse set of
leading Indian lenders reduce portfolio delinquency rates, boost approval rates
and maximize loan-loss adjusted net interest income on a variety of
loan portfolios using traditional financial data and alternate data.
Leveraging a combination of traditional financial data, historical repayment data and alternate data such as mobile exhaust and social media exhaust, our machine learning technology identifies nuanced patterns and creates credit underwriting models that exploit these patterns to identify risky borrowers at the time of underwriting. These underwriting models are unique to each lender and account for differences in the sourcing, underwriting and collections processes followed by these lenders.
We use the credit underwriting models thus built to generate actionable recommendations and analytics for the client's loan application portfolio. For ease of interpretation, we generate risk scorecards for each loan application that captures a quantitative risk score, the key aspects of the application from a risk perspective, a recommendation on the application and a set of red flags (if applicable) for that application.
All of this is designed to be available in real-time to sales officers, credit officers as well as risk managers to enable efficient loan processing at scale.
Monsoon is happy to work with clients to tailor our offerings or build out new offerings to address their unique needs. Our technology is highly scalable and is built using architectural paradigms that enable easy customization, low latency and easy integration with external systems via APIs and SDKs.