P2P网贷借款人信用风险模糊综合评价——基于模糊数学理论的视角
发布时间：2018-11-04 10:37
【摘要】：依托于互联网金融的P2P网贷是推进金融创新、实现普惠金融的有效途径之一,因其便捷性、低门槛而成为时下最受欢迎的小额融资方式,但其信用风险防控面临着巨大挑战。首先提出了基于五标度法计算指标权重的层次分析法,结合模糊数学的综合评价方法,建立了P2P网贷平台借款人信用风险模糊综合评价模型。然后,依据某P2P平台的交易数据,该模型评价结果的准确性达到了83%,为P2P网贷平台精准定位借款人提供一个有价值的决策支撑参考。
[Abstract]:P2P network loan based on Internet finance is one of the effective ways to promote financial innovation and realize inclusive finance. Because of its convenience and low threshold, P2P network loan has become the most popular microfinance method nowadays, but its credit risk prevention and control is facing great challenge. This paper first puts forward the analytic hierarchy process (AHP) based on the five-scale method to calculate the index weight, and establishes the fuzzy comprehensive evaluation model of the credit risk of the borrowers on the P2P network lending platform combined with the comprehensive evaluation method of fuzzy mathematics. Then, according to the transaction data of a P2P platform, the accuracy of the model is 833, which provides a valuable decision support reference for the P2P network loan platform to accurately locate the borrower.
【作者单位】： 西南财经大学统计学院;西南财经大学西部商学院;
【分类号】：F724.6;F832.4
本文编号：2309570
[Abstract]:P2P network loan based on Internet finance is one of the effective ways to promote financial innovation and realize inclusive finance. Because of its convenience and low threshold, P2P network loan has become the most popular microfinance method nowadays, but its credit risk prevention and control is facing great challenge. This paper first puts forward the analytic hierarchy process (AHP) based on the five-scale method to calculate the index weight, and establishes the fuzzy comprehensive evaluation model of the credit risk of the borrowers on the P2P network lending platform combined with the comprehensive evaluation method of fuzzy mathematics. Then, according to the transaction data of a P2P platform, the accuracy of the model is 833, which provides a valuable decision support reference for the P2P network loan platform to accurately locate the borrower.
【作者单位】： 西南财经大学统计学院;西南财经大学西部商学院;
【分类号】：F724.6;F832.4
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