Business Case – Portfolio Risk from Mortgage Default

To minimize loss accrued from loan default, it is crucial for Lenders, Servicers and Investors to review and assess mortgage portfolios periodically. Typically, loans that are deemed risky could have their terms renegotiated, or other measures taken through Early Resolution. In order to efficiently handle this problem on a large-scale for thousands of loans, automated tasks and machine learning tools can be leveraged to predict the default risk of a loan and overall default rating of a portfolio of loans.

The current problem is building an interface that bridges the gap between the power of these cutting-edge tools and the people who can benefit from their strengths.

Our Approach

Speridian has combined their domain knowledge with the appropriate data science models and relevant technologies to design the solution. Below are the different components of the solutions:

Algorithms

After carefully considering and testing multiple statistical models on our prepared data, Gradient Boosting was chosen for its high precision, fast training times, and overall performance. The class imbalance of the data was addressed by conservatively applying a pipeline of resampling algorithms; SMOTE (Synthetic Minority Oversampling Technique), and RUS (Random Under Sampling). Ultimately, our model is able to achieve an overall weighted precision of close to 97%, with an 80% accurate recall rate of the minority class.

About the Authors