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Anti-Money Laundering: Using data visualization to identify suspicious activity

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  • Singh, Kishore
  • Best, Peter

Abstract

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 anti-money 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.

Suggested Citation

  • Singh, Kishore & Best, Peter, 2019. "Anti-Money Laundering: Using data visualization to identify suspicious activity," International Journal of Accounting Information Systems, Elsevier, vol. 34(C), pages 1-1.
    • Handle: RePEc:eee:ijoais:v:34:y:2019:i:c:3
    DOI: 10.1016/j.accinf.2019.06.001
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    References listed on IDEAS

    as
    1. Kishore Singh & Peter Best, 2016. "Interactive visual analysis of anomalous accounts payable transactions in SAP enterprise systems," Managerial Auditing Journal, Emerald Group Publishing, vol. 31(1), pages 35-63, January.
    2. Buchanan, Bonnie, 2004. "Money laundering--a global obstacle," Research in International Business and Finance, Elsevier, vol. 18(1), pages 115-127, April.
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    Citations

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    Cited by:

    1. Alexey Ruchay & Elena Feldman & Dmitriy Cherbadzhi & Alexander Sokolov, 2023. "The Imbalanced Classification of Fraudulent Bank Transactions Using Machine Learning," Mathematics, MDPI, vol. 11(13), pages 1-15, June.
    2. Zeinab Rouhollahi, 2021. "Towards Artificial Intelligence Enabled Financial Crime Detection," Papers 2105.10866, arXiv.org.
    3. Parsaee Tabar , Azam & Abdolvand , Neda & Rajaee Harandi , Saeedeh, 2021. "Identifying the Suspected Cases of Money Laundering in Banking Using Multiple Attribute Decision Making (MADM)," Journal of Money and Economy, Monetary and Banking Research Institute, Central Bank of the Islamic Republic of Iran, vol. 16(1), pages 1-20, March.
    4. Dawei Cheng & Yao Zou & Sheng Xiang & Changjun Jiang, 2024. "Graph Neural Networks for Financial Fraud Detection: A Review," Papers 2411.05815, arXiv.org, revised Nov 2024.
    5. Lisa Perkhofer & Conny Walchshofer & Peter Hofer, 2020. "Does design matter when visualizing Big Data? An empirical study to investigate the effect of visualization type and interaction use," Journal of Management Control: Zeitschrift für Planung und Unternehmenssteuerung, Springer, vol. 31(1), pages 55-95, April.
    6. Ogbeide, Henry & Thomson, Mary Elizabeth & Gonul, Mustafa Sinan & Pollock, Andrew Castairs & Bhowmick, Sanjay & Bello, Abdullahi Usman, 2023. "The anti-money laundering risk assessment: A probabilistic approach," Journal of Business Research, Elsevier, vol. 162(C).

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