Research On The Identification Of Money Laundering Suspicious Groups Based On Customer Transactions

This paper conducts an empirical study on the methods of identifying money laundering suspicious groups using case data of a commercial bank to be screened for suspicious money laundering transactions.The paper concludes that the clustering analysis method,logistic regression method and visualization of financial transaction networks can effectively identify money laundering suspicious groups and show the roles played by individual members within the money laundering suspicious group organization.This paper provides effective analytical tools and methods for commercial banks to further improve the quality of their anti-money laundering screening work from the perspective of uncovering suspicious money laundering groups.This paper uses cluster analysis method and logistic regression method to classify the cases of suspicious money laundering transactions to be screened into suspicious cases,cases of concern and normal cases."The higher the number of counterparties,the higher the percentage of funds transferred within 10 minutes,and small test transactions before large transactions,the higher the degree of suspicion of the corresponding cases.Secondly,this paper mines the transaction groups based on financial transactions,and visualizes the customer identity information and transactions of a single case,which can effectively determine the link and position the member is in within the group organization.Then,based on the money laundering suspicion level of the group’s constituent members,the suspicion level of the group is judged,and if the suspicion level of the members is high,the group is a money laundering suspicious group in a high probability.Finally,this paper summarizes the research results,analyzes the shortcomings of this paper,and proposes possible further research directions in the light of the actual situation of anti-money laundering suspicious case screening work in commercial banks.