Reducing Default Rates
A world leading Credit Card provider wanted to improve its already well established techniques for predicting the probability that credit card accounts would default within three months.
A Robust testing framework to evaluate alternative machine learning approaches— many not common in the industry — to find more effective techniques for predicting the probability that credit card accounts would default within three months.
We built and tested numerous advanced algorithms to determine which technique was most effective. We also tested and ranked the predictions from many combinations of the best algorithms as shown in the figure below and found an ensemble model (MPN) that demonstrated record-breaking performance.
Our collaboration using machine learning reduces the number of credit card accounts that defaulted by more than 10% when compared to their original production model.