A Study On Credit Risk Management Of Rural Commercial Bank Under The Background Of Digital Economy

Due to the impact of the global spread of the COVID-19 epidemic in the past two years,international trade has suffered a heavy blow,and the international economic situation is not optimistic.At the same time,although China has implemented "Class B and Class B control" for novel coronavirus infection,domestic economic and social development still faces severe challenges,many small,medium-sized and micro enterprises’ operations have been seriously affected,and some real economies transmit operational risks to banks,This directly led to a surge in bank credit risk and other issues.For commercial banks,especially rural commercial banks(hereinafter referred to as "RCB")whose credit risk management level is lagging behind,it is imperative to build a scientific,reasonable,accurate and efficient credit risk management system.Under the background of digital economy,RCB are also actively exploring and applying big data thinking to improve their own credit risk management level,reduce the incidence of bad debts of banks and promote stable and healthy development.Digital economy is an economic concept.It is an economic form in which human beings can guide and realize the rapid optimal allocation and regeneration of resources and achieve high-quality economic development through the identification,selection,filtering,storage and use of big data(digital knowledge and information).In the era of digital economy,big data technology has been applied to all walks of life,and has achieved significant results in solving many traditional problems,including banking.In China,many commercial banks use big data thinking and technology to reduce information asymmetry,achieve data cooperation and intelligent decision-making to a certain extent,and improve the bank’s credit risk management level.This thesis starts with the credit risk management framework,combines the connotation and characteristics of big data,and uses big data thinking to study the current credit risk management of RCB.First,this thesis summarizes the current situation of credit risk management at home and abroad;Secondly,it combs the problems existing in the credit risk management of RCB,analyzes the significance of big data for the credit risk management of RCB,and takes T RCB as an example to extract the credit production data of T RCB in the previous year.By establishing a logistic regression analysis model and a decision tree model,it compares the effectiveness of the two models,and forms a more practical credit risk evaluation model,which is based on this,Propose credit risk management optimization plan.At the moment when the big data technology is gradually mature and the domestic economic environment is increasingly complex,RCB need to continue to do a good job in the rural and micro customer market,and at the same time,use big data thinking to do a good job in credit risk prevention and control,which puts forward new requirements for RCBs’ credit risk management capabilities.The relevant problems of T RCB exist in different degrees in other domestic RCB.Through this study,I hope to strengthen the credit risk management and control ability of T RCB,and I hope to provide reference and reference for other domestic RCB to effectively implement credit risk management.