Research On Application Of Bank Card Abnormal Transaction Detection And Supervision

With the development of the economy and technological progress,the emergence of online banking and the rise of online payment services,the transaction volume of bank card accounts has ushered in a spurt of growth.Under the massive transaction data,some illegal transactions are always hidden.With the enrichment of transaction forms,the methods and modes of these illegal transactions have become more complex and more secretive.The illegal and criminal acts behind these transactions will not only bring great harm to the entire society,but also damage the interests of the banks themselves.Therefore,the identification and reporting of money laundering and fraudulent transactions is always one of the important tasks in bank risk prevention and control management.Abnormal transaction detection is the upstream link of anti-money laundering and anti-fraud.Its accuracy determines the upper limit of the follow-up manual verification effectiveness and also determines the workload of risk control personnel.The current anti-money laundering and anti-fraud systems commonly used in the industry are mainly implemented based on rule engines,which have problems such as high false alarm rates,inflexible rule adjustments,and excessively subjective threshold settings,making it difficult to cope with dynamic changes in illegal transaction patterns.The self-adaptability of the machine learning detection model can greatly improve these problems,and the massive transaction data and risk control system knowledge base accumulated by the bank for a long time can effectively support the construction of the model.This paper starts from the development status of the bank's intelligent risk control system for anti-fraud and anti-money laundering,and explain the importance of abnormal transaction detection in the entire system.By analyzing and comparing different abnormal transaction detection technologies,it explains the shortcomings and challenges faced by the current industry-wide abnormal transaction detection technologies based on the rule engine,thus highlighting the advantages of the machine learning models in solving these problems,and clarifying the necessity of them for abnormal transaction detection task.Then use real transaction data from the bank to conduct experiments,and propose a certain transferable detection system to improve the traditional detection process,thereby reducing the burden on risk control personnel and improving the response speed of abnormal transaction detection.Finally,from the perspective of data management and knowledge management,it discusses the construction of an intelligent bank card abnormal transaction supervision system.At the same time,it gives the prospects of related talent development and application expansion,which has certain reference value for the intelligent transformation of the bank's traditional risk control system.The main contributions of this research are: using the existing expert systematic abnormal transaction detection rules to guide the feature derivation in data pre-processing,so that the model can not only capture the behavior patterns of the entire customer group,but also pay attention to the behavior characteristics of the customers themselves.In view of the current large amount of data but few labels and it is difficult to implement manual labeling,the sparse auto-encoding model of unsupervised learning is adopted to avoid training difficulties and poor model effects caused by insufficient training samples and unbalanced categories in supervised learning.This paper puts forward management ideas centered on data management and knowledge management,help banks build teams capable of independent research and development,and continuously mining the value of data.Drive the sustainable development of the bank's intelligent risk control system with data and knowledge.