The Credit Risk Warning Evaluation Of Commercial Banks In The Era Of Big Data

In recent years,with the domestic macroeconomic going into the new normal state of growth slowing down,structural optimization and dynamic pluralism,various types of social transformation risks are accelerating to the bank,the domestic banking industry generally are facing the risk of pressure.Throughout the domestic commercial banks,there are some shortcomings in the aspects of credit risk control: First,the prominent contradiction between the rapid growth of loans and the limited customer management capacity;Second,the prominent contradiction between the borrower business complex form and the limited and simple means of bank customer monitoring;Third,the contradiction between the highly unified information of borrowing enterprises and the fragmentation of information transmission before and after the bank credit is more prominent;Fourth,the contradiction between the dynamic changes of the business risk of borrowing enterprises and the backwardness of the risk of bank lending is more prominent.;Fifth,the contradiction between the need of the whole process management of loading and the lack of bank loans after the intervention is more prominent.With the effective application and extensive promotion of big data technology,commercial banks can reconstruct the bank credit risk management structure around large data analysis,strengthen the off-site monitoring and analysis through the massive data and powerful analysis technology inside and outside the commercial banks.Single business line,single customer,single account,single species of localized,fragmented wind control management,to business association,upstream and downstream linkage,cross-account transaction information flow,capital flow control mode change,improve bank credit Risk management effectiveness,and thus stabilize the credit asset quality cycle fluctuations.The personal credit early warning model of the existing risk management monitoring system has been revised to establish the neural network model to make it more suitable for the actual situation and more operationality of the branches of Bank of Communications in Hubei Province;In the existing bill checking process,the evaluation method of using the crawler algorithm to collect customer external information is introduced to improve the accuracy rate of credit evaluation and the timely rate of post-loan dynamic monitoring.