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Academic Journal of Business & Management, 2023, 5(5); doi: 10.25236/AJBM.2023.050509.

Research on Personal Credit Risk Assessment Based on Data Fusion


Tian Wei, Shuai Li

Corresponding Author:
Shuai Li

School of Management Science and Engineering, Central University of Finance and Economics, Beijing, China


In recent years, the domestic emphasis on systemic risk prevention has continued to increase. Chinese credit department, which is a crucial department to avoid significant systemic risks, has a particular responsibility to manage and control personal credit risks related to the lives of the public. Therefore, strengthening the management of the individual credit department, especially to guard against personal credit risks, build a data-driven quantitative model for scoring applicants’ credit, and improve the personal credit identification and evaluation methods on this basis is a vital part of individual credit risk prevention and systemic financial risk. In this paper, we first study the index system of personal credit risk identification, standard personal credit risk identification models, and some data fusion techniques. Then, we build an individual credit risk identification model based on the data fusion method on the above theoretical basis. After that, the selection of indicators was carried out among 20 variables of German credit data. Finally, an empirical study was conducted to compare the effect of the two data fusion methods on the performance of the individual credit risk identification model. The defects of single models in the personal credit risk problem domain are apparent. However, data fusion technology can help classification models to gain better uniformity in accuracy and robustness, but it also needs to be improved in terms of interpretability and promotion.


Personal Credit Risk; Data Fusion; Classification Model; Bagging

Cite This Paper

Tian Wei, Shuai Li. Research on Personal Credit Risk Assessment Based on Data Fusion. Academic Journal of Business & Management (2023) Vol. 5, Issue 5: 62-74. https://doi.org/10.25236/AJBM.2023.050509.


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