Utilizing Big Data Analytics Lifecycle for Early Detection of Suspicious Financial Operations: A Proposed Model for Money Laundering Detection

Abstract

This chapter proposes a model for discovering suspicious financial operations such as money laundering. To achieve this, the authors reviewed research papers on money laundering and financial institutions’ cases and problems, especially those related to financial transfers. They also collected primary data through face-to-face semi-structured interviews with financial companies’ owners and experts in financial transfers to identify hypotheses that help discover suspicious transfers. The chapter discusses the six big data analysis cycle phases from problem discovery to model deployment to identify suspicious transfers. The chapter uses hypothetical data and models to discuss the results and focuses on exchange companies willing to analyze financial operations. The chapter proposes tools that exchange companies can use to monitor and prevent suspicious transfers including data visualization and machine learning algorithms.

Citation

Elastal, M., Allaymoun, M.H. and Elbastawisy, T.K. (2024), "Utilizing Big Data Analytics Lifecycle for Early Detection of Suspicious Financial Operations: A Proposed Model for Money Laundering Detection", Hamdan, A., Alareeni, B. and Khamis, R. (Ed.) Digital Technology and Changing Roles in Managerial and Financial Accounting: Theoretical Knowledge and Practical Application (Studies in Managerial and Financial Accounting, Vol. 36), Emerald Publishing Limited, Leeds, pp. 3-14. https://doi.org/10.1108/S1479-351220240000036001