RT Journal Article
SR Electronic
T1 Interpretability of Machine Learning versus Statistical Credit Risk Models
JF The Journal of Financial Data Science
FD Institutional Investor Journals
SP jfds.2022.1.089
DO 10.3905/jfds.2022.1.089
A1 Anand K. Ramteke
A1 Pavan Wadhwa
A1 Monica Yan
YR 2022
UL https://pm-research.com/content/early/2022/03/26/jfds.2022.1.089.abstract
AB Model interpretability is important in the banking industry for three reasons: certain US regulations require creditors to provide consumers with the reasons for taking adverse action (reason codes) on their credit applications; model users want to understand the reasoning behind model predictions; and identification of bias and reinforcement of stakeholders’ trust in the model. In this article, the authors compare the interpretability of an XGBoost versus a logistic model in predicting the probability of default for a credit card customer. They conclude that (1) the reason codes of an XGBoost model and a comparable logistic model are similar, (2) reason codes generated by XGBoost are more trustworthy from the customer’s perspective, and (3) nonlinearity of XGBoost is unlikely to have a significant impact on reason code(s).