Ensemble methods for credit scoring of Chinese peer-to-peer loans

Wei Cao (), Yun He, Wenjun Wang (), Weidong Zhu and Yves Demazeau ()
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Wei Cao: HFUT - Hefei University of Technology
Yun He: HFUT - Hefei University of Technology
Wenjun Wang: HFUT - Hefei University of Technology
Weidong Zhu: HFUT - Hefei University of Technology
Yves Demazeau: LIG - Laboratoire d'Informatique de Grenoble - CNRS - Centre National de la Recherche Scientifique - UGA - Université Grenoble Alpes - Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology - UGA - Université Grenoble Alpes

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Abstract: Risk control is a central issue for Chinese peer-to-peer (P2P) lending services. Although credit scoring has drawn much research interest and the superiority of ensemble models over single machine learning models has been proven, the question of which ensemble model is the best discrimination method for Chinese P2P lending services has received little attention. This study aims to conduct credit scoring by focusing on a Chinese P2P lending platform and selecting the optimal subset of features in order to find the best overall ensemble model. We propose a hybrid system to achieve these goals. Three feature selection algorithms are employed and combined to obtain the top 10 features. Six ensemble models with five base classifiers are then used to conduct comparisons after synthetic minority oversampling technique (SMOTE) treatment of the imbalanced data set. A real-world data set of 33 966 loans from the largest lending platform in China (ie, the Renren lending platform) is used to evaluate performance. The results show that the top 10 selected features can greatly improve performance compared with all features, particularly in terms of discriminating "bad" loans from "good" loans. Moreover, comparing the standard

Keywords: credit scoring; ensemble learning; feature selection; synthetic minority oversampling technique (SMOTE) treatment; Chinese peer-to-peer (P2P) lending (search for similar items in EconPapers)
Date: 2021
New Economics Papers: this item is included in nep-big, nep-cmp, nep-pay and nep-rmg
Note: View the original document on HAL open archive server: https://hal.science/hal-03434348v1
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Published in Journal of Credit Risk, 2021, Vol. 17 (3), pp.79-115. ⟨10.21314/JCR.2021.005⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03434348

DOI: 10.21314/JCR.2021.005

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