Credit scoring prediction leveraging interpretable ensemble learning

Liu, Yang1; Huang, Fei2; Ma, Lili2,5; Zeng, Qingguo3; Shi, Jiale4

2023-09-01

10.1002/for.3033

JOURNAL OF FORECASTING   Impact Factor & Quartile

0277-6693

1099-131X

Credit scoring models based on machine learning often need to work on accuracy and interpretability in practical applications. Original KCDWU has a more prominent adaptive property but ignores intra-class and inter-class distances in the clustering process, resulting in the possibility of inaccurate identification of class features and cluster structure of data, which compromises the clustering effect. Therefore, we improve the automatic K-means clustering based on the Calinski-Harabasz index, thus achieving a clustering output for improved results. We also scrutinize representative five single classification models and six ensemble learning models for credit scoring prediction. We empirically test the superior performance of ensemble learning models and identify the best model CatBoost by comparing them based on multiple evaluation indicators. Empirical results reveal that the SHAP method conforms well to CatBoost and delivers a global and local interpretation of the predictions. This work provides financial institutions with a promising candidate for interpretable credit scoring models.

credit scoring prediction data imbalance problem ensemble learning interpretability

SSCI

English

Others

National Natural Science Foundation of China[72204190] ; Research Foundation of Ministry of Education of China[22YJZH114] ; China Postdoctoral Science Foundation[2022M722476]

Business & Economics

Economics ; Management

WOS:001067509100001

WILEY

ECONOMICS BUSINESS

Web of Science

1.Wuhan Univ, Sch Informat Management, Wuhan, Peoples R China
2.Wuhan Univ, Econ & Management Sch, Wuhan, Peoples R China
3.Southern Univ Sci & Technol, Shenzhen Inst Quantum Sci & Engn, Shenzhen, Peoples R China
4.Wuhan Univ, Sch Comp Sci, Wuhan, Peoples R China
5.Wuhan Univ, Econ & Management Sch, Wuhan 430072, Peoples R China

Liu, Yang,Huang, Fei,Ma, Lili,et al. Credit scoring prediction leveraging interpretable ensemble learning[J]. JOURNAL OF FORECASTING,2023.

Liu, Yang,Huang, Fei,Ma, Lili,Zeng, Qingguo,&Shi, Jiale.(2023).Credit scoring prediction leveraging interpretable ensemble learning.JOURNAL OF FORECASTING.

Liu, Yang,et al."Credit scoring prediction leveraging interpretable ensemble learning".JOURNAL OF FORECASTING (2023).