Big Data and Related Model Algorithms in Commercial Bank Credit Evaluation

Jiawei Zhang

doi:10.54691/bcpbm.v38i.3676

Authors

Jiawei Zhang

DOI:

https://doi.org/10.54691/bcpbm.v38i.3676

Keywords:

Big data; commercial banks; risk evaluation; model algorithms.

Abstract

Contemporarily, with the development of big data and related machine learning models and algorithms, commercial banks no longer possess advantages in traditional risk control and need to introduce big data and its algorithmic models as a new means of risk evaluation. The introduction of big data and its model algorithms by some commercial banks and fintech companies has proven its outstanding effect in the field of risk control. Based on the evaluations of XGBoost model, OneClassSVM model and other models, the article analyses the application of related technologies in the fields of default prediction and public opinion identification. Besides, this study combines the analysis with the scenarios in which commercial banks can implement big data and model algorithms for risk evaluation. In this case, it provides a new feasible method for commercial banks' risk evaluation and control, and proves the possibility of the application of related technologies with great effect. These results play an important role in promoting the application of big data and its models for risk evaluation and control in commercial banks.

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References

Pan Rui. Research on risk measurement and management of commercial banks in China based on the New Basel Accord. Dissertation of Shandong University, 2018.

Al-Shakrchy E J. The impact of credit risk managing on bank profitability an empirical study during the pre-and post-subprime mortgage crisis: The case of Swedish commercial banks. Journal of business and finance, 2017, 3(1): 31-42.

Magnifique U J. The effect of credit risk management on the financial performance of commercial banks in Rwanda. Unpublished doctoral dissertation), University of Nairobi, Kenya, 2013.

Kumbirai M, Webb R. A financial ratio analysis of commercial bank performance in South Africa. African Review of Economics and Finance, 2010, 2(1): 30-53.

Bholat D. Big data and central banks. Big Data & Society, 2015, 2(1): 2053951715579469.

Jiang J, Liao L, Lu X, et al. Deciphering big data in consumer credit evaluation. Journal of Empirical Finance, 2021, 62: 28-45.

Einav L, Levin J. Economics in the age of big data. Science, 2014, 346(6210): 1243089.

Chen J, Tao Y, Wang H, et al. Big data based fraud risk management at Alibaba. The Journal of Finance and Data Science, 2015, 1(1): 1-10.

Wang Y, Xiuping S, Zhang Q. Can fintech improve the efficiency of commercial banks?—An analysis based on big data. Research in international business and finance, 2021, 55: 101338.

Mahesh B. Machine learning algorithms-a review. International Journal of Science and Research (IJSR), 2020, 9: 381-386

Schonlau M, Zou R Y. The random forest algorithm for statistical learning. The Stata Journal, 2020, 20(1): 3-29.

Breiman L. Random forests. Machine learning, 2001, 45(1): 5-32.

Chen T, Guestrin C. Xgboost: A scalable tree boosting system. Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. 2016: 785-794.

Erfani S M, Rajasegarar S, Karunasekera S, et al. High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning. Pattern Recognition, 2016, 58: 121-134.

Hirschberg J, Manning C D. Advances in natural language processing. Science, 2015, 349(6245): 261-266.

Butaru F, Chen Q, Clark B, et al. Risk and risk management in the credit card industry. Journal of Banking & Finance, 2016, 72: 218-239.

Wang R, Liu J, Luo H. Fintech development and bank risk taking in China. The European Journal of Finance, 2021, 27(4-5): 397-418.