  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 clichy 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 investment fraud activities. In
AML, the customer identification is an important task which helps AML experts
to monitor customer habits: some being customer domicile, transactions that
they are involved in etc. However, simple query tools provided by current DBMS
as well as naive approaches in customer searching may produce incorrect and
ambiguous results and their processing time is also very high due to the
complexity of the database system architecture. In this paper, we present a new
approach for identifying customers registered in an investment bank. This
approach is developed as a tool that allows AML experts to quickly identify
customers who are managed independently across separate databases. It is tested
on real-world datasets, which are real and large financial datasets. Some
preliminary experimental results show that this new approach is efficient and
effective.
