Credit evaluation method of online borrowers based on multidimensional data

Abstract

The invention discloses a P2P borrower credit evaluation method based on big data. The invention comprises a data acquisition module, a data processing module and a model building module. In the era of big data, credit data sources are expanding, mainly including the following four aspects: credit data generated by financial institutions, credit data generated by relevant government departments, credit data generated by other public utilities, Internet credit data generated by the network. The data module is mainly divided into two parts, the credit data generated by financial institutions, relevant government departments and public utilities are qualitatively defined as structured data collection; Social media data, such as WeChat friends and Sina Weibo, are collected as unstructured datain Internet credit data. Data processing module is mainly aimed at structured data, including data balance processing and feature selection. As that imbalance phenomenon exist in the structured dataof personal credit, the invention uses CART-SMOTE algorithm for data balance processing; Under the background of big data, the characteristics of personal credit evaluation data are complicated, and irrelevant and redundant variables will have adverse influence on the accuracy of model prediction. The invention uses random forest and gradient descent decision tree to select evaluation characteristics. The structured data model uses an improved lightGBM for preliminary credit ratings; Feature extraction from unstructured social text data, credit evaluation and affective tendency analysis usingin-depth learning. Then the emotional tendencies in personal social media text data are fed back to the credit evaluation of P2P borrowers to study the correlation between them. Provide a reference for the final credit evaluation structure.

Description

technical field [0001] The invention discloses a P2P credit evaluation method, which relates to the fields of information technology and credit service technology. Background technique [0002] In 2015, P2P network lending, as a new financial model relying on the Internet, began to rise and grow rapidly under the national strategic guidance of "Internet +" and big data serving the real economy. As of the end of July 2017, P2P network lending The cumulative number of industry platforms reached 5,916. However, due to the late start of P2P online lending in China, the imperfect credit system, and the lack of relevant laws and regulations, problems such as platform running away and borrowers not repaying on time or even absconding with money still occur from time to time, exposing serious financial security. question. [0003] The State Council pointed out in the "Promotion of Inclusive Finance Development Plan" that "Internet finance has played a positive role in promoting th...

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Credit assessment of P2P borrowers with un

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