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首页> 外文期刊>Soft computing: A fusion of foundations, methodologies and applications >Design of comprehensive evaluation index system for P2P credit risk of 'three rural' borrowers
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Design of comprehensive evaluation index system for P2P credit risk of 'three rural' borrowers

机译：“三农”借款人P2P信用风险综合评价指标体系设计

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摘要

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.
机译：在新出现的同伴（P2P）贷款行业中，信用风险和违约风险等风险将为在线贷款平台和投资者带来巨大的损失。因此，有必要设计合理的评估指标体系的信贷风险，以科学评估借款人的风险水平。本文研究了“三农”（即农业，农村地区和农民）借款人的P2P信用风险综合评价指标体系的设计。具体地，我们构建了“三农”借款人的P2P信用风险所设定的功能。基于传统的指标体系，我们添加了与农业相关借款人特定的静态指标，动态索引将互联网反映为功能集的初步索引，并选择“翼龙贷款”平台作为研究样本的借款人数据。然后，将35个借款人信用功能提取为一系列信用风险。然后，我们介绍了一个基于滤波器和包装器的两级特征选择方法，以选择35次初始借款人信用功能的主要功能。在过滤器的阶段，三种过滤方法用于计算不平衡功能的重要性。在包装器的阶段，建议通过启发式搜索算法来过滤特征子集的套索逻辑方法。最后，根据分类准确性选择21个主要的独立特征，这构成了“三农”借款人的信用风险评估指标体系。

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