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 111
OP 124
DO 10.3905/jfds.2022.1.089
VO 4
IS 2
A1 Anand K. Ramteke
A1 Pavan Wadhwa
A1 Monica Yan
YR 2022
UL https://pm-research.com/content/4/2/111.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).