我国P2P网络借贷借款方信用风险的评估与防范研究

发布时间：2018-05-27 02:11

本文选题：P2P + 借款方信用风险　；参考：《安徽财经大学》2016年硕士论文

【摘要】：过去的几年中,P2P网络借贷在我国快速发展,其实质是民间借贷与互联网相结合产生的一种创新金融服务。网贷行业的高收益吸引了越来越多的投资者,然而在我国的P2P网络借贷的发展中,由于信用风险管理理念单薄,对借款人的信用风险的评估水平低下制约了我国P2P网贷行业的发展。目前我国P2P网络借贷借款方信用风险评估的研究越来越深入,一些评估方法也被网贷平台运用到借款方信用风险的评估中,但仍然存在许多问题。一是借款方信用风险的评估指标体系不健全;二是在评估中多采用单一的分析方法,这些都严重影响了借款方信用风险评估的准确性,间接地影响了我国网贷行业的发展。首先本文主要讲述了选题背景和意义以及对国内外P2P网络借贷借款方信用风险评估的文献进行综述。通过借鉴前期学者的研究,发现目前我国P2P网络借贷借款方信用风险评估研究中主要存在的问题,并提出了本文的研究思路和研究方法。通过对我国P2P网络借贷的发展现状及面临的信用风险进行深入的分析,发现了我国在P2P网络借贷中还存在很大的信用风险,着重分析了网络借贷中借款方的信用风险。其次是对我国P2P网络借贷借款方信用风险的评估方法和现状进行了研究。通过对传统以及现代的借款方信用风险评估方法的分析和国内外P2P网络借贷借款方信用风险的评估方式和现状进行比较,发现了我国在P2P网络借贷信用风险评估的不足之处,需要建立科学有效的方法对我国P2P网络借贷借款方的信用风险进行评估。再次是对AHP层次分析法和BP神经网络模型相关的理论基础的研究以及信用风险评估指标指标体系的建立从而构建了AHP-BP模型,用该模型对网络借贷借款方的信用风险进行研究。在建立模型时,先单一运用AHP方法将借款人信用风险的评估结果作为BP神经网络模型的输出样本,然后与BP神经网络模型的借款人信用风险的实际输出值进行比较,通过实证研究证明该模型的实用性和准确性。最后是对全文的总结并对我国网贷借款方信用风险的防范提出了建议以及本文的不足之处和展望。
[Abstract]:In the past few years, P2P network lending has developed rapidly in China, and its essence is a kind of innovative financial service which is produced by the combination of private lending and Internet. The high income of the network loan industry attracts more and more investors. However, in the development of P2P network loan in our country, because of the weak idea of credit risk management, The low evaluation level of borrowers' credit risk restricts the development of P2P network loan industry in China. At present, the research on credit risk assessment of P2P network borrowers in China is more and more in-depth. Some evaluation methods are also applied to the credit risk assessment of borrowers by network loan platforms, but there are still many problems. First, the evaluation index system of the credit risk of the borrower is not perfect; second, the single analysis method is used in the evaluation, which seriously affects the accuracy of the credit risk assessment of the borrower and indirectly affects the development of the network loan industry in China. First of all, this paper mainly describes the background and significance of the topic and the domestic and foreign P2P network borrowers credit risk assessment literature review. By referring to the previous research, this paper finds out the main problems in the research of credit risk assessment of P2P network borrowers in China, and puts forward the research ideas and research methods of this paper. Based on the analysis of the current situation and credit risk of P2P network lending in China, it is found that there is still a great credit risk in P2P network lending in China, and the credit risk of borrowers in network lending is analyzed emphatically. Secondly, the paper studies the credit risk assessment methods and current situation of P2P network borrowers in China. Through the analysis of traditional and modern methods of credit risk assessment of borrowers and the comparison of the evaluation methods and present situation of credit risk of borrowers in P2P network at home and abroad, this paper finds out the shortcomings of credit risk assessment in P2P network in China. It is necessary to establish a scientific and effective method to evaluate the credit risk of Chinese P2P network borrowers. Thirdly, the paper studies the theoretical basis of AHP Analytic hierarchy process (AHP) and BP neural network model, and establishes the credit risk evaluation index system, and then constructs the AHP-BP model, which is used to study the credit risk of network borrowers. In the process of establishing the model, the evaluation result of borrower's credit risk is taken as the output sample of BP neural network model by AHP method, and then compared with the actual output value of borrower's credit risk of BP neural network model. The practicability and accuracy of the model are proved by empirical research. Finally, the paper summarizes the full text and puts forward some suggestions on how to prevent the credit risk of Chinese net loan borrowers, as well as the shortcomings and prospects of this paper.
【学位授予单位】：安徽财经大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：F724.6;F832.4

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