Credit Risk Modeling Using Transfer Learning and Domain Adaptation - Suryanto, Hendra; Mahidadia, Ashesh; Bain, Michael; Guan, Charles; Guan, Ada (Frontiers in Artificial Intelligence, 2022)
In the domain of credit risk assessment lenders may have limited or no data on the historical lending outcomes of credit applicants. Typically this disproportionately affects Micro, Small, and Medium Enterprises (MSMEs), for which credit may be restricted or too costly, due to the difficulty of predicting probability of default. However, if data from other related credit risk domains is available Transfer Learning may be applied to successfully train models, e.g., from the credit card lending and debt consolidation domains to predict in the small business lending domain. In this paper we report successful results from an approach using transfer learning to predict probability of default based on the novel concept of Progressive Shift Contribution from source to target domain. Towards real-world application by lenders of this approach we further address two key questions. The first is to explain transfer learning models, and the second is to adjust features when the source and target domains differ. To address the first question we apply Shapley values to investigate how and why transfer learning improves model accuracy, and also propose and test a domain adaptation approach to address the second. These results show that adaptation improves model accuracy in addition to the improvement from transfer learning. We extend this by proposing and testing a combined strategy of feature selection and adaptation to convert values of source domain features to better approximate values of target domain features. Our approach includes a strategy to choose features for adaptation and an algorithm to adapt values of these features. In this setting transfer learning appears to improve model accuracy by increasing the contribution of less predictive features. Although the percentage improvements are small, such improvements in real world lending could be of significant economic importance.