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Big Data Analytics for Complex Credit Risk Assessment of Network Lending Based on SMOTE Algorithm

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  • Aiwen Niu
  • Bingqing Cai
  • Shousong Cai

Abstract

With the continuous development of big data technology, the data of online lending platform witness explosive development. How to give full play to the advantages of data, establish a credit risk assessment model, and realize the effective control of platform credit risk have become the focus of online lending platform. In view of the fact that the network loan data are mainly unbalanced data, the smote algorithm is helpful to optimize the model and improve the evaluation performance of the model. Relevant research shows that stochastic forest model has higher applicability in credit risk assessment, and cart, ANN, C4.5, and other algorithms are also widely used. In the influencing factors of credit evaluation, the weight of the applicant’s enterprise scale, working years, historical records, credit score, and other indicators is relatively high, while the index weight of marriage and housing/car production (loan) is relatively low.

Suggested Citation

  • Aiwen Niu & Bingqing Cai & Shousong Cai, 2020. "Big Data Analytics for Complex Credit Risk Assessment of Network Lending Based on SMOTE Algorithm," Complexity, Hindawi, vol. 2020, pages 1-9, September.
    • Handle: RePEc:hin:complx:8563030
    DOI: 10.1155/2020/8563030
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    Cited by:

    1. Jiaming Liu & Xuemei Zhang & Haitao Xiong, 2024. "Credit risk prediction based on causal machine learning: Bayesian network learning, default inference, and interpretation," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1625-1660, August.

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