Research Of Commercial Bank Credit Risk Warning System Construction Based On Big Data Technology

In recent years,the credit risk management of the commercial bank is facing increasing challenges.The non-performing loan balances of commercial banks remain high.It is imminent to reduce the credit risk of commercial banks and ensure the safety of commercial banks' credit business.In the bank credit business,corporate customers account for far more than individual customers,so risk management should focus on corporate customers.In the process of commercial bank credit risk management,the benefits of early warning risk are much higher than ex post management.However,the current commercial bank early warning system fails to make full use of data and cannot achieve real time.The rise of big data technology provides a technical foundation for bank credit risk early warning.Using big data technology,data information can be collected in real time,a comprehensive early warning index system can be established,and analysis can be based on data mining technology to achieve real-time monitoring and accurate assessment of credit risk.So that commercial banks can make correct decisions in a timely manner when conducting credit business.The paper uses literature research method,a combination of quantitative and qualitative research methods,summarizes relevant literature at home and abroad,sorts out concepts such as bank credit risk early warning,bank credit risk early warning system,and discusses credit risk early warning theory and big data theory.Based on this,the necessity analysis and feasibility analysis of the credit risk early-warning system of commercial banks based on big data were carried out,and big data analysis and functional requirement analysis were made.Then,big data technology was used to adopt a hierarchical design method to construct An early-warning system including four modules: data acquisition layer,data storage and processing layer,data analysis layer,and data application layer.Finally,this article analyzes the application scenarios.The main contribution of this article is to build a credit risk early warning system for commercial banks based on big data.Compared with traditional early warning systems,this system collects data information from banks' internal and external systems and the Internet,and the data sources are more comprehensive.The ranking is divided into six categories,which are more relevant.In terms of real-time performance,this article uses a number of big data technology methods throughout the system to achieve real-time early warning.In terms of risk measurement,this paper uses AHP and BP neural network.Combined methods,the model calculation is more accurate.However,due to my lack of experience,and the system is only explained from a theoretical level,it is yet to be verified whether it can be used in actual business and whether the performance can achieve the best in actual use.