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Design of comprehensive evaluation index system for P2P credit risk of “three rural” borrowers

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Abstract

In the emerging peer-to-peer (P2P) lending industry, risks such as credit risk and default risk will bring huge losses to online lending platforms and investors. Therefore, it is necessary to design a reasonable evaluation index system of credit risk to scientifically evaluate the risk level of borrowers. This paper studies the design of comprehensive evaluation index system for P2P credit risk of “three rural” (i.e., agriculture, rural areas and farmers) borrowers. Concretely, we construct the feature set for P2P credit risk of “three rural” borrowers. Based on the traditional index system, we add the static indexes specific to the agriculture-related borrowers and the dynamic indexes reflect the Internet as the preliminary indexes of the feature set and select the borrowers data of the “Pterosaur loan” platform as the research sample. Then, 35 borrower credit features are extracted as a feature set of credit risk. Then, we present a two-stage feature selection method based on filter and wrapper to select the main features from 35 initial borrower credit features. In the stage of filter, three filter methods are used to calculate the importance of the unbalanced features. In the stage of wrapper, a Lasso-logistic method is proposed to filter the feature subset through heuristic search algorithm. In the end, 21 main independent features are selected according to the classification accuracy, which constitute the evaluation index system of credit risk of “three rural” borrowers.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (No. 71671135), the 2018 Soft Science Research Project of Technology Innovation in Hubei province (No. 2018ADC044) and the 2019 Fundamental Research Funds for the Central Universities (WUT: 2019IB013).

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Authors and Affiliations

  1. School of Science, Wuhan University of Technology, Wuhan, 430070, People’s Republic of China

    Congjun Rao, Hui Lin & Ming Liu

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  1. Congjun Rao
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  2. Hui Lin
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  3. Ming Liu
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Correspondence to Hui Lin.

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Rao, C., Lin, H. & Liu, M. Design of comprehensive evaluation index system for P2P credit risk of “three rural” borrowers. Soft Comput 24, 11493–11509 (2020). https://doi.org/10.1007/s00500-019-04613-z

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