Intelligent Risk Control System Based On Deep Learning

In recent years,commercial banks have grown rapidly and various business volumes have grown rapidly.In the past five years,China's banking industry has grown to become the world's largest banking system.As a result,the country has continuously introduced a variety of regulatory policies,stressed that we must resolutely resist and resolve financial risks in the new situation.Financial risks are constantly changing.Therefore,building an intelligent risk control system can effectively help commercial banks to control risks.A successful risk control system should have a certain degree of flexibility,which will enable our business personnel to focus on turning ideas into risk models to help commercial banks find important risks.Commercial banks have accumulated huge amounts of data during the development process.These data include structured data and unstructured data.All risk control models are based on these data.Due to the large amount of data,traditional databases have been very difficult to handle these massive amounts of data.Banks have always wanted to establish their own comprehensive intelligent wind control system.If the bank's intelligent risk control system only pays attention to the bank's internal structured data,it is obviously one-sided because the external risks of the bank's customers cannot be known,and the risk of hiding in unstructured data cannot be revealed.Therefore,it is very necessary to combine external data and internal data for analysis and form a closed loop.This article's intelligent risk control system was developed specifically for the bank's audit department.This project combines big data technology and artificial intelligence technology,and explores the use of built-in analysis tools and monitoring modules to help banks monitor various violations.This article describes in detail the design and implementation of intelligent wind control system.First introduced the software requirements of the banking audit department for the system.In the system outline design section,the system design principles,system architecture design,network topology diagram,function module design,and some database design are shown.The detailed design and implementation of the system is mainly through the class diagram and sequence diagram to introduce the implementation of each module,as well as the specific implementation of the interface display.This project first introduced the artificial intelligence module sentiment analysis.The algorithm used by this module is LSTM.Finally,it is a system test.The function.of the system is tested through corresponding test cases to ensure the quality of the system.The system is currently running on the bank and the system is currently operating stably.Since the system accesses the big data platform,all the analysis operations are completed by the big data platform.Compared with the previous traditional data analysis operations,the efficiency is significantly improved.For the first time,the system has added a sentiment analysis module,which has also helped the bank to understand the hidden deeper risks.