Research On Risk Identification Of Enterprise Account In Commercial Banks

In recent years,with the implementation of the policy of canceling bank account licenses for enterprises carried out by the central regulatory authorities,the convenience of opening bank and enterprise accounts has greatly improved.However,the illegal and criminal activities of illegal gangs such as money laundering and telecommunications network fraud through opening enterprise accounts happens often.At the national legislative level,the first law aimed at comprehensively combating telecommunications network fraud-the "Anti Telecommunications Network Fraud Law of the People’s Republic of China"-was implemented on December 1,2022.It shows the responsibilities of commercial banks and increases the penalties.Therefore,the work of enterprise account risk management in commercial banks has been greatly influenced by external factors,and has become a new issue.However,the traditional enterprise account risk identification models of commercial banks are lagging behind in application,lack of precision,and inefficient,which cannot effectively meet the current bank’s account risk management demands.With the continuous development of financial technology,scholars have gradually focused on how to apply advanced machine learning algorithms to establish intelligent enterprise account risk identification models driven by data value,as a powerful means of risk prevention for commercial banks.This article delves into the characteristics and impacts of current corporate account risk management,and investigates the challenges and issues faced by commercial banks in managing corporate account risks.Based on the needs of commercial banks for account risk prevention and control,and the need to construct models,the relevant datasets were statistically analyzed and preprocessed.Using random forest algorithm,which is one of the most accurate computational methods in the field of machine learning for solving risk classification problems,a random forest model for risk identification was constructed.The model was empirically tested using data from Bank F’s corporate accounts,and the model’s performance indicators were evaluated and optimized.The model was then compared and assessed using Logistic and GBDT models for classification analysis on the same data.The study demonstrates the effectiveness and feasibility of the random forest model in identifying corporate account risks,providing new insights for commercial banks in the research of corporate account risk identification.The research results show that the risk identification random forest model constructed in this paper can effectively address the issues of traditional model application lag and support commercial banks in real-time monitoring of corporate account fund transactions.By identifying risky corporate accounts in a timely manner,the model enables banks to adopt real-time transaction blocking measures,achieving a seamless integration of risk monitoring,risk assessment,and risk control operations.Furthermore,the construction ideas and methods of this model have a certain degree of transferability,which can be applied to other areas of commercial bank account risk management.This provides reference significance for commercial banks to improve their account risk management level,increase account operation efficiency,enhance risk control capabilities,and offer better technological support for high-quality bank development.