Abstract
With the rapid development of the P2P (peer-to-peer) online lending industry, which is facing significant credit risk, personal credit evaluation is an important method to reduce credit risk. Based on the various indexes of personal credit risk evaluation of domestic and foreign commercial banks, and according to the characteristics of P2P online lending, this paper analyzes the factors that affect the credit risk of P2P online borrowers, introduces the unique risk factors in the field of Internet information, and constructs an index system of personal credit risk evaluation of P2P online lending, which combines qualitative and quantitative indexes, including six major indexes and 21 small indexes. It then quantifies each index and defines the judgment standard of the evaluation results. Using analytic hierarchy process (AHP), expert scoring method, and fuzzy comprehensive evaluation method, this paper establishes a personal credit risk evaluation model of P2P online lending based on AHP method. The public information of two borrowers on the “PaiPai Lending” platform are selected for experimental verification. The results show that the improved personal credit risk evaluation model has better applicability and can evaluate the borrower’s credit status more scientifically, accurately, and comprehensively; thus, it is an effective method of personal credit risk evaluation of P2P online lending.
Highlights
In recent years, with the advancement of inclusive financial policies and the development of Internet plus, big data, and cloud computing, Internet finance has achieved leapfrog growth
The results show that the algorithm improves the classification performance of standard rough set theory in credit risk assessment, and they believed that the algorithm is suitable for other application fields
Using analytic hierarchy process (AHP), expert scoring method, and fuzzy comprehensive evaluation method, this paper establishes a personal credit risk evaluation model of P2P online lending based on AHP method
Summary
With the advancement of inclusive financial policies and the development of Internet plus, big data, and cloud computing, Internet finance has achieved leapfrog growth. The Internet finance peer-to-peer lending platform (P2P) marked the rise of the platform with the establishment of “PaiPai Lending” platform (https://www.ppdai.com/). In 2007, and the number and scale of the platform showed a new trend of rapid growth. (https://www.wdzj.com/), as of August 2020, the total number of P2P online lending platforms in China has reached 6607, the number of closed and problematic platforms has reached 6277, and only 330 have been in normal operation. With the rapid development of the industry, risks are exposed; the default of borrowers has caused serious losses to. This paper attempts to build a model to evaluate the personal credit risk of P2P online lending, so that the lender and online lending platform can better understand the credit status of borrowers and reduce the possibility of loss
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