Detection of money laundering activities in financial transactions by using data mining methods, Benford's law and GANs algorithm

اقتصاد پولی مالی

Article 11, Volume 29, Issue 24, September 2022, Pages 327-358   

Document Type: Original Article

DOI: 10.22067/mfe.2023.72676.1117

Authors

Aliyeh Kazemi^{* 1}; Amir Moghadamfalahi²; Ali Abdali³; Sara Aryaee⁴

¹Professor of Operations Management and Decision Sciences, Faculty of Management, University of Tehran, Tehran, Iran

²MSc. in Industrial Management, Faculty of Management University of Tehran, Tehran, Iran

³Assistant Prof., Amin Police University, Tehran, Iran

⁴Ph.D. student of Industrial Management, Faculty of Management University of Tehran, Tehran, Iran.

Abstract

Nowadays, money laundering has become a serious threat to the world economy. Traditional methods of Anti Money Laundering (AML) are costly and inefficient. Recently, data mining techniques have been developed and have been considered as appropriate methods to detect money laundering activities. The purpose of this research is to detect money laundering suspicious cases which might need more detailed scrutiny using data mining algorithms with real banking transaction datasets. CRISP-DM would be used as the research methodology, the statistical population would be the banking transactions and samples would be the transactions of one of the bank branches. For this purpose, two main approaches are used. In the first approach, using the k-means algorithm, financial transactions of banking accounts are clustered. Then, using anomaly detection techniques, abnormal transactions that might be suspicious of money laundering and need to be scrutinized in more detail have been detected. In the second approach, a novel technique using Benford’s law and GANs algorithm has been introduced. It can detect financial accounts that used concocted amounts in their transactions and might be suspicious of financial fraud and money laundering. The first approach can identify accounts with outliers in their transactions with an accuracy of about 93%, and the second approach can identify suspicious accounts that do not use professional methods to hide fake figures in their transactions with an accuracy of about 60%. to recognize correctly.

Keywords

Money Laundering; Data mining; k-means algorithm; GANs algorithm; Benford’s law

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