Background
Credit risk management is of vital importance to financial institutions that provide loans to individuals and businesses. Credit loans have a risk of defaulting which can result in substantial financial losses for these institutions. By improving credit risk management, financial institutions can greatly improve overall performance and secure a competitive advantage.
Statistical and predictive modeling techniques can be used to analyze the risk levels associated with loans. Financial institutions often collect and archive vast amounts of information on borrowers which data scientists can use to build these predictive models. These models can help these institutions to identify patterns in credit default ratios, predict risk levels of future credit loans, and make data-driven credit investment decisions.
Emprata built predictive models for one of the largest banks in the country.
Result
- Incorporate millions of historical records of data and hundreds of variables, from various sources into a series of predictive models to identify which loans to (1) Reject (to minimize financial loss), and (2) Accept (to maximize potential of good loans that may have otherwise been rejected).
