P2P personal-credit evaluation method and device based on data mining

Abstract

The invention provides a P2P personal-credit evaluation method and device based on data mining. The method includes: S101, collecting data of a money borrower, wherein the data of the money borrower include personal-credit data, social-network data and e-commerce network data; S102, preprocessing the data of the money borrower in S101; S103, improving a flower pollination algorithm on the basis ofa simulated annealing algorithm, and optimizing parameters of support vector machines on the basis of improvement; S104, inputting the preprocessed personal-credit data to an optimized data trainingmodel to obtain a basic score of the money borrower; S105, using counting or an analytic hierarchy process to analyze the preprocessed social-network data and e-commerce network data to obtain two additional scores of the money borrower; and S106, determining a credit score of the money borrower on the basis of results obtained in S104 and S105. According to the technical solution provided by theinvention, the multi-faceted data can be fused for credit evaluation to improve accuracy of credit evaluation.

Description

technical field [0001] The invention relates to the technical field of data mining processing, in particular to a P2P personal credit evaluation method and device based on data mining. Background technique [0002] With the rapid development of Internet finance, the research on loan credit evaluation is becoming more and more mature. Most of the traditional credit evaluation indicators rely on the basic information of the borrower, that is, "hard information". In the era of big data, with the rapid development of new-generation information technologies such as the mobile Internet, the Internet of Things, and cloud computing, and the continuous expansion of application fields, "all data is credit" has become a new idea for the construction of a social credit system. Big data has a huge impact on credit evaluation, making the evaluation of borrowers no longer rely solely on personal "hard information", the data acquisition channels have become more extensive, and credit evalu...

AI Technical Summary

Problems solved by technology

[0003] At present, due to the imperfection of the P2P credit evaluation system, my country's P2P online lending industry has experienced a wave of large-scale platforms running away and closing down, which makes the online lending process exist market risks, operational risks, legal risks, and credit risks. Many other risks have also brought huge losses to investors.

Among them, the credit risk of borrowers is the most important risk faced by online lending, which seriously damages the healthy and orderly development of the lending industry.

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