Interpretable high-stakes decision support system for credit default forecasting

Weixin Sun, Xuantao Zhang, Minghao Li and Yong Wang

Technological Forecasting and Social Change, 2023, vol. 196, issue C

Abstract: Methods for forecasting credit default have long been research focus for financial institutions. In this study, we propose an interpretable high-stakes decision support system for credit default forecasting called CDFS. Because of the high-stake nature of credit default prediction, the proposed CDFS adheres to the principle of "people in the loop." The proposed CDFS comprises six modules: data processing, feature selection, data balancing, forecasting, evaluation, and interpretation. A feature selection method (permutation importance method), nine resampling methods, and six high-performance forecasting methods were employed in the proposed CDFS. The China Taiwan credit card default dataset and South Germany credit dataset were used to test the interpretability and predictive performance of the proposed CDFS. Experiments showed that the feature selection and data balancing modules of the CDFS effectively improve the prediction performance. A comparison with traditional logistic regression models demonstrated that the CDFS can provide decision-makers with satisfactory explanations for prediction results. In summary, the CDFS proposed in this study exhibited excellent predictive performance and satisfactory interpretability. This study contributes to improving the accuracy of credit default forecasting and reducing credit risk in financial institutions.

Keywords: High-stakes decision forecasting; Credit default forecasting; Interpretable machine learning; Imbalanced datasets; Resampling methods (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0040162523005103
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:196:y:2023:i:c:s0040162523005103

DOI: 10.1016/j.techfore.2023.122825

Access Statistics for this article

Technological Forecasting and Social Change is currently edited by Fred Phillips

More articles in Technological Forecasting and Social Change from Elsevier
Bibliographic data for series maintained by Catherine Liu ().