期刊
APPLIED SOFT COMPUTING
卷 95, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.asoc.2020.106570
关键词
P2P network lending; Three Rurals borrowers; Credit risk evaluation; Random forest
资金
- China Scholarship Council (CSC) scholarship [201906955002]
- National Natural Science Foundation of China [71671135]
- 2019 Fundamental Research Funds for the Central Universities, China [WUT: 2019IB013]
With the rapid growth of the P2P online loan industry in the Three Rurals (agriculture, rural areas, and farmers) sector, it is imperative to manage the borrowing risk of borrowers in the rural areas. A credit risk assessment model is proposed to classify the credit worthiness of the Three Rurals borrowers. We select the loan data of the Pterosaur Loan platform as the research sample, and establish a 2-stage Syncretic Cost-sensitive Random Forest (SCSRF) model to evaluate the credit risk of the borrowers. From the random forest, we construct a cost relationship from the actual distribution of the data categories, introduce a weighted Mahalanobis distance using the entropy weight method in the cost function, and adopt a weighted voting for the cost-sensitive decision tree base classifier. The parameters of the SCSRF model are optimized via a grid search. We validate the SCSRF classification model against several established credit evaluation models. (C) 2020 Elsevier B.V. All rights reserved.
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