An Efficient Search Tool For An Anti-Money Laundering Application Of An Multi-National Bank's Dataset

Today, money laundering (ML) poses a serious threat not only to financial institutions but also to the nations. This criminal activity is becoming more and more sophisticated and seems to have moved from the cliché of drug trafficking to financing terrorism and surely not forgetting personal gain. Most of the financial institutions internationally have been implementing anti-money laundering solutions (AML) to fight fraud activities. However, the AML systems are so complicated that simple query tools provided by current DBMS may produce incorrect and ambiguous results and they are also very time-consuming due to the complexity of the database system architecture. In this paper, we present a new approach for identifying customers quickly and easily as part of an AML application. This will help AML experts to identify quickly customers who are managed independently across separate databases of the organization. This approach is tested on large and real-world financial datasets. Some preliminary experimental results show that this new approach is efficient and effective.