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.

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 that can explain how the algorithm works, as well as 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.
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