Abstract
Due to the rapid development of China’s economy in recent years, banking business is booming. Credit business is one of the mainstream business of banks, but how to evaluate the credit risk of borrowers has become one of the hot topics in the Internet finance industry, which has attracted more and more attention. Since the launch of China’s credit system in 2013, the active risk control of credit business has been one of the hot topics in China’s financial field. Its essence is to divide customers into credit customers and non credit customers. With the rapid development of modern computer business, machine learning algorithm has gradually been popularized and applied in the financial field. Combined with gbdt algorithm in machine learning algorithm, the bank customer’s basic information, flow records, user detection information, user detection scale and other relevant data are used for comprehensive evaluation. Finally, an example is given to analyze the correlation.
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Acknowledgements
1. This paper is the phased achievement of the project “air crew” (Project No. 2017sxzx71) of the demonstration and experimental training center of Anhui provincial quality engineering project in 2017, The Science Project of Hainan Province (No. 619QN193), the Science Project of Hainan University (KYQD(ZR)20021).
2. Anhui Provincial Department of education 2017 major teaching and research project of provincial quality engineering project of colleges and Universities Research on issues related to the cultivation of audit talents in the perspective of Higher Vocational Innovation Education (Project No.: 2017jyxm0824).
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Wang, L., An, F. (2021). Machine Learning Algorithm Credit Risk Prediction Model. In: Abawajy, J., Choo, KK., Xu, Z., Atiquzzaman, M. (eds) 2020 International Conference on Applications and Techniques in Cyber Intelligence. ATCI 2020. Advances in Intelligent Systems and Computing, vol 1244. Springer, Cham. https://doi.org/10.1007/978-3-030-53980-1_15
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