 Measuring the Default Risk of Small Business Loans: Improved Credit Risk Prediction Using Deep LearningYiannis Dendramis, Elias Tzavalis and Aikaterini Cheimarioti Journal of Forecasting, 2025, vol. 44, issue 7, 2277-2297 Abstract: This paper proposes a multilayer artificial neural network (ANN) method to predict the probability of default (PD) within a survival analysis framework. The ANN method captures hidden interconnections among covariates that influence PD, potentially leading to improved predictive performance compared to both logit and skewed logit models. To assess the impact of covariates on PD, we introduce a generalized covariate method that accounts for compositional effects among covariates and employ stochastic dominance analysis to rank the importance of covariate effects across both the ANN and logit model approaches. Applying the ANN method to a large dataset of small business loans reveals prediction gains over the logit models. These improvements are evident for short‐term prediction horizons and in reducing type I misclassification errors in the identification of loan defaults, an aspect crucial for effective credit risk management. Regarding the generalized covariate effects, our results suggest that behavior‐related covariates exert the strongest influence on PD. Moreover, we demonstrate that the ANN structure stochastically dominates the logit models for the majority of the covariates examined. Date: 2025 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:wly:jforec:v:44:y:2025:i:7:p:2277-2297 Access Statistics for this article Journal of Forecasting is currently edited by Derek W. Bunn More articles in Journal of Forecasting from John Wiley & Sons, Ltd. |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.