Construction Of Intelligent Credit Risk Early Warning System For Commercial Banks

In the operation and management of commercial banks,credit risk is a significant risk that impacts the operations and management of banks,encompassing various factors that require careful consideration.With the advancement of computer technology,including artificial intelligence,big data,and other related intelligent technologies,these have become ubiquitous across all aspects of social operations.Such intelligent technology has significantly enhanced the efficiency of social operations,thereby promoting economic growth.When viewed from the perspective of commercial banks,this trend is particularly important to note,intelligent technology can provide strong analytical ability and intelligent improvement of traditional business,and its incorporation into the operation of credit business can improve the operation efficiency of business and the identification and early warning ability of credit risks.Therefore,it is necessary to combine intelligent technology with credit business.This thesis delves deeply into the development of an intelligent credit risk system by drawing on concepts and theories related to credit risk,which is based on the traditional credit risk early warning system and the operational status of China Citic Bank.This article provides an overview of China Citic Bank’s overall development,main business,current state of credit risk early warning,and the sources and types of credit risk,within the context of the case.I In developing an intelligent credit risk early warning system,this thesis utilizes a crawler algorithm to extract financial index data from the financial data page of Eastmoney.com for3,385 companies,based on their stock codes.Specifically,16 financial indicators are selected and used to construct the credit risk early warning index system,using financial data as an illustrative example.Genetic algorithm has the characteristics of good index screening and optimization performance.It is introduced into BP neural network to reduce the characteristic that BP neural network is easy to fall into the local optimal solution.The GA-BP neural network model constructed in this thesis based on genetic algorithm and BP neural network algorithm is better than the traditional BP neural network model in terms of credit risk early warning precision after training,which has certain reference significance for the design of credit risk early warning system of Chinese commercial banks.This thesis comprises six chapters.The first chapter outlines the research background,significance,methodology,and structure of the thesis.It also examines the current state of research on credit risk early warning and the application of BP neural network.The second chapter is the relevant theories of the intelligent credit risk early warning system for commercial banks,including the meaning and types of commercial bank credit risk,the causes of commercial bank credit risk,the composition of the intelligent credit risk early warning system for commercial banks and related theoretical basis,which provides theoretical basis for the following research.The third chapter is a brief introduction of the case bank--China CITIC Bank,and then introduces the current situation of the credit risk early warning system of China CITIC Bank,and summarizes the deficiencies of the current system.Chapter four expounds the necessity and feasibility of building an intelligent credit risk early warning system for commercial banks.The fifth chapter is the construction of the intelligent credit risk early warning system of commercial banks based on BP neural network optimized by genetic algorithm.Firstly,the guiding principles of the system construction are expounded.Secondly,the overall design of the intelligent credit risk early warning system is carried out.The collected data are used for further application analysis,and the warning results and system evaluation are obtained.The sixth chapter mainly introduces the data quality assurance and acquisition method optimization,update database,optimization algorithm and other safeguard measures for the system.