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Entropy method of constructing a combined model for improving loan default prediction: A case study in China

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  • Yiheng Li
  • Weidong Chen

Abstract

In recent years, credit scoring has become an efficient tool to assist financial institutions in identifying potential default borrowers, and the combined model is widely viewed as a useful vehicle. In this study, after pre-processing based on random forest, we propose a combined logistic regression algorithm and artificial neural network model to improve the predictive performance based on actual data from a rural commercial bank under the condition that loan quality directly affects the profitability of the bank. The combined model requires a step with an entropy method to determine the entropy weights of the logistic regression model and artificial neural network model. The experimental results reveal that the proposed combined model outperforms the two base models on four evaluation metrics: accuracy (ACC), area under the curve (AUC), Kolmogorov-Smirnov statistic (KS), and Brier score (BS). Moreover, the model is superior to a state-of-the-art ensemble model, stacking.

Suggested Citation

  • Yiheng Li & Weidong Chen, 2021. "Entropy method of constructing a combined model for improving loan default prediction: A case study in China," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 72(5), pages 1099-1109, May.
    • Handle: RePEc:taf:tjorxx:v:72:y:2021:i:5:p:1099-1109
    DOI: 10.1080/01605682.2019.1702905
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    Cited by:

    1. Sun, Yue & Chai, Nana & Dong, Yizhe & Shi, Baofeng, 2022. "Assessing and predicting small industrial enterprises’ credit ratings: A fuzzy decision-making approach," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1158-1172.
    2. Yuan, Kunpeng & Chi, Guotai & Zhou, Ying & Yin, Hailei, 2022. "A novel two-stage hybrid default prediction model with k-means clustering and support vector domain description," Research in International Business and Finance, Elsevier, vol. 59(C).

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