An Evidential Reasoning Rule-Based Ensemble Learning Approach for Evaluating Credit Risks with Customer Heterogeneity

Ying Yang, Ting Gao, Gencheng Xu and Gang Wang
Additional contact information
Ying Yang: School of Management, Hefei University of Technology, Hefei 230009, P. R. China2Key Laboratory of Process Optimization and Intelligent Decision-making, Ministry of Education, Hefei 230009, P. R. China3Ministry of Education Engineering Research Center for Intelligent Decision-Making & Information System Technologies, Hefei 230009, P. R. China
Ting Gao: School of Management, Hefei University of Technology, Hefei 230009, P. R. China2Key Laboratory of Process Optimization and Intelligent Decision-making, Ministry of Education, Hefei 230009, P. R. China3Ministry of Education Engineering Research Center for Intelligent Decision-Making & Information System Technologies, Hefei 230009, P. R. China
Gencheng Xu: School of Management, Hefei University of Technology, Hefei 230009, P. R. China2Key Laboratory of Process Optimization and Intelligent Decision-making, Ministry of Education, Hefei 230009, P. R. China3Ministry of Education Engineering Research Center for Intelligent Decision-Making & Information System Technologies, Hefei 230009, P. R. China
Gang Wang: School of Management, Hefei University of Technology, Hefei 230009, P. R. China2Key Laboratory of Process Optimization and Intelligent Decision-making, Ministry of Education, Hefei 230009, P. R. China3Ministry of Education Engineering Research Center for Intelligent Decision-Making & Information System Technologies, Hefei 230009, P. R. China

International Journal of Information Technology & Decision Making (IJITDM), 2024, vol. 23, issue 02, 939-966

Abstract: Credit risk evaluation has been vital for financial institutions to identify default customers and to avoid financial loss. Machine learning and data mining techniques have been adopted to develop scoring models for enhancing the prediction performance of default customers. However, it is difficult for these machine learning models for explaining the rejection or approval decision-making process to customers and other non-technical personnel. This paper presents an evidence reasoning (ER) rule-based ensemble learning approach for credit risk evaluation considering customer heterogeneity. Firstly, customers are segmented into different groups by k-means clustering algorithms and a two-stage weighting method is proposed to determine the significances of attributes by their discriminating powers between groups and within groups. Then, the attribute-related evidence is obtained by Bayesian statistics to represent the relationships between the attributes and credit risks, and a two-stage weighting evidential reasoning (TER) is developed as a base learner for credit scoring. Lastly, multiple base learners TERs are aggregated for evaluating customers’ credit risks. An empirical study on three credit datasets demonstrated that the proposed approach can achieve high performance with good explainability. The predicted results of the model can be well comprehended by providing the contribution of attributes and the activated rules in evidential reasoning processes.

Keywords: Credit risk evaluation; evidence reasoning rule; customer heterogeneity; ensemble learning (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S0219622023500281
Access to full text is restricted to subscribers

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:wsi:ijitdm:v:23:y:2024:i:02:n:s0219622023500281

Ordering information: This journal article can be ordered from

DOI: 10.1142/S0219622023500281

Access Statistics for this article

International Journal of Information Technology & Decision Making (IJITDM) is currently edited by Yong Shi

More articles in International Journal of Information Technology & Decision Making (IJITDM) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().