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Anti-money laundering: Using data visualization to identify suspicious activity

journal contribution
posted on 23.03.2020, 00:00 by Kishore SinghKishore Singh, Peter BestPeter Best
Annually, money laundering activities threaten the global economy. Proceeds of these activities may be used to fund further criminal activities and to undermine the integrity of financial systems worldwide. For these reasons, money laundering is recognized as a critical risk in many countries. There is an emerging interest from both researchers and practitioners concerning the use of software tools to enhance detection of money laundering activities. In the current economic environment, regulators struggle to stay ahead of the latest scam, and financial institutions are challenged to ensure that they can identify and stop criminal activities, while ensuring that legitimate customers are served more effectively and efficiently. Effective technological solutions are an essential element in the fight against money laundering. Improved data and analytics are key in assisting investigators to focus on suspicious activities. Continually evolving regulations, together with recent instances of money laundering violations by some of the largest financial institutions, have highlighted the need for better technology in managing antimoney laundering activities. This study explores the use of visualization techniques that may assist in efficient identification of patterns of money laundering activities. It demonstrates how link analysis may be applied in detecting suspicious bank transactions. A prototype application (AML2ink) is used for proof-of-concept purposes.

History

Volume

34

Start Page

1

End Page

18

Number of Pages

18

eISSN

1873-4723

ISSN

1467-0895

Publisher

Elsevier, UK

Language

en

Peer Reviewed

Yes

Open Access

No

Acceptance Date

24/06/2019

Era Eligible

Yes

Journal

International Journal of Accounting Information Systems