Unintended bias against protected groups has become a key obstacle to the widespread adoption of machine learning methods.

Machine learning algorithms have come to dominate several industries. After decades of resistance from examiners and auditors, machine learning is now mov- ing from the research desk to the application stack for credit scoring and a range of other applications in credit risk.

Machine learning models have been used extensively for credit scoring, but the architectures employed su er from a significant loss in accuracy out-of-sample and out-of-time. Further, the most common architectures do not e ectively integrate economic scenarios to enable stress testing, cash flow, or yield estimation. The present research demonstrates that providing lifecycle and environment functions from Age-Period-Cohort analysis can significantly improve out-of-sample and out-of-time performance as well as enabling the model’s use in both scoring and stress testing applications. This method is demonstrated for behavior scoring where account delinquency is one of the provided inputs, because behavior scoring has historically presented the most diculties for combining credit scoring and stress testing. Our method works well in both origination and behavior scoring. The results are also compared to multihorizon survival models, which share the same architectural design with Age-Period-Cohort inputs and coecients that vary with forecast horizon, but using a logistic regression estimation of the model. The analysis was performed on 30-year prime conforming US mortgage data. Nonlinear problems involving large amounts of alternate data are best at highlighting the advantages of machine learning. Data from Fannie Mae and Freddie Mac is not such a test case, but it serves the purpose of comparing these methods with and without Age-Period-Cohort inputs. In order to make a fair comparison, all models are given a panel structure where each account is observed monthly to determine default or non-default.