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.
The Solution.
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 to explain how the algorithm works and that can trace why a specific decision (approval or non-approval) was taken to ensure accountability.
The Results.
Reduced the rate of mortgage default.
Reduced the cost of assessing credit risk.
Improved customer experience by enabling fast credit approval from anywhere.