Lidan Fan
ABSTRACT
Judging the credit risk level of bank customers can effectively determine whether there is illegal operation of customer account funds, so as to prevent money laundering. Scientific investment portfolio of financial products helps to retain high-quality customers of banks. According to the personal bank flow, this paper establishes a model to divide the credit risk levels of customers, and judges the customers with money laundering risk. SVM Logical regression analysis is carried out according to the newly divided variables to determine the possibility of money laundering. The model can accurately divide the credit rating of the account into five grades: A, B, C, D, and E, and forecast the risk of money laundering at each level, thus exploring the application of pattern recognition and intelligent recognition in different fields.
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- Liu Zhifeng. Research on pre-loan credit risk management of personal consumer credit of Agricultural Bank of China Binzhou Branch based on customer group characteristics [D]. Shandong University of Science and Technology, 2020.Google Scholar
Index Terms
- Credit rating of bank customers and money laundering risk prediction based on pattern recognition: Take Chongqing City as an example
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