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An efficient customer search tool within an anti-money laundering application implemented on an internaitonal bank's dataset
Date Issued
2009-07-16
Date Available
2016-09-01T16:15:00Z
Abstract
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 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.
Type of Material
Conference Publication
Language
English
Status of Item
Peer reviewed
Conference Details
2009 International Conference on Information and Knowledge Engineering (IKE'09), Las Vegas, USA, 13-16 July 2009
This item is made available under a Creative Commons License
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