Author(s)

Shuran Yang ¹, Yinghao Zhang ¹, Yining Sun ¹, Lulu Cheng ¹, Shuwei Sun ¹

Affiliation(s)

¹ School of Sciences, Henan University of Technology, Zhengzhou, China

Corresponding Author

Shuran Yang

ABSTRACT

College students not only constitute a desirable target customer base for credit consumption, but also represent a potential group for credit consumption. However, due to the lack of in-depth understanding of credit consumption and relevant information, default events in college student credit consumption have occurred frequently in recent years, such as "campus loans". Therefore, assessing the current credit consumption risk of college students is particularly important. In this study, we combine the algorithmic approach of machine learning with the research methodology of questionnaire surveys to build a college student credit repository based on big data. Firstly, by collecting various influential factors about credit consumption, an analysis of individual credit risk assessment indicators of college students is conducted, and the weights of each index are determined. Then, a BP neural network algorithm-based credit consumption risk evaluation model for college students is established using a training set and a test set.

KEYWORDS

Credit risk model, BP neural network, Grey relational analysis

CITE THIS PAPER

Shuran Yang, Yinghao Zhang, Yining Sun, Lulu Cheng, Shuwei Sun, Research on Constructing a Credit Repository for College Students Based on Big Data and Modeling Credit Risks. Financial Engineering and Risk Management (2023) Vol. 6: 10-16. DOI: http://dx.doi.org/10.23977/ferm.2023.060602.

REFERENCES

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