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

Document Type : Original Article

Authors

• Aliyeh Kazemi ¹
• Amir Moghadamfalahi ²
• Ali Abdali ³
• Sara Aryaee ⁴

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

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

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

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

https://doi.org/10.22067/mfe.2023.72676.1117

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

Main Subjects

• Banking and Banking Management

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