Research on the Influencing Factors of Personal Credit Based on a Risk Management Model in the Background of Big Data

Abstract

Between states, between enterprises and enterprises, between people, it can be stated that credit is full of every corner of our lives. But the current lack of social credit is fundamental. Credit risk is particularly prominent. In the extensive data generation today, the information on personal credit statistics is very large, but still lack the data system processing and screening. Through the information retrieval of 200 credit information reports, this paper constructs the evaluation system of personal credit by using the basic information of the individual. The basic information of these individuals has great convenience in information collection and information statistics, and this basic information covers all aspects that are likely to result in the breach of contract. Through the use of single factor analysis and logistic model to solve the index system, you can not only find the impact of individual indicators on the degree of personal credit, but also see the overall impact of indicators on the degree of credit, that is, the weight of the indicators. Finally, four different credit ratings are divided by assigning the indicators to the scores. Credit rating can clearly measure the respective credit situation. Through the classification of these levels, measuring the credit line when a person in the individual credit operation, at the same time, it can provide reference and proval to administrative departments, which is benefit for managing credit risks. It has a substantial meaning and value in use. The solution to the rating system cannot only be applied to individuals, but also to the enterprises, with a wide range of versatility.

Hybridizing association rules with adaptive weighted decision fusion for personal credit assessment

“…With the rapid development of inclusive finance, especially the development of online loan, consumer finance and other industries, personal credit industry possesses unlimited demand and potential, which has become the concern in credit industry. Therefore, it is necessary to build a reliable personal credit evaluation system, to differentiate the credit state for a customer (Lv, Li, & Zhang, 2017). Under the background of big data (Jones, Johnstone, & Wilson, 2015), several data mining techniques, including clustering (Huang, Hung, & Jiau, 2006), classification (Jurgovsky et al, 2018), association rules (Ma & Cheng, 2016) and prediction (García, Marqués, & Sánchez, 2019), have been conducted to perfect personal credit rating mechanisms.…”