Automated Load Approval Use Case | Banking

The Challenge.

Many financial institutions have rules-based algorithms to determine whether a customer is eligible for a loan, often using payment history as the main factor.
However, there are many other factors, such as demographics, that can impact the probability that a customer will default on a loan.
Advanced Machine Learning algorithms like Deep Learning can be powerful at predicting default on loans, however the decisions are difficult to interpret and explain.

Designed and built a Machine Learning model to predict the risk that a customer will default on a loan, using interpretable ML algorithms.
Integrated the model into enterprise systems for automated mortgage approval within minutes using a few key pieces of information.
Built reports that can explain how the algorithm works, as well as trace why a specific decision (approval or non-approval) was taken to ensure accountability.