Combating money laundering with machine learning : A study on different supervised-learning algorithms and their applicability at Swedish cryptocurrency exchanges
Sammanfattning: In 2018, Europol (2018) estimated that more than $22 billion dollars were laundered in Europe by using cryptocurrencies. The Financial Action Task Force explains that moneylaunderers may exchange their illicitly gained fiat-money for crypto, launder that crypto by distributing the funds to multiple accounts and then re-exchange the crypto back to fiat-currency. This process of exchanging currencies is done through a cryptocurrency exchange, giving the exchange an ideal position to prevent money laundering from happening as it acts as middleman (FATF, 2021). However, current AML efforts at these exchanges have shown to be outdated and need to be improved. Furthermore, Weber et al. (2019) argue that machine learning could be used for this endeavor. The study's purpose is to investigate how machine learning can be used to combat money laundering activities performed using cryptocurrency. This is done by exploring what machine learning algorithms are suitable for this purpose. In addition, the study further seeks to understand the applicability of the investigated algorithms by exploring their fit at cryptocurrency exchanges. To answer the research question, four supervised-learning algorithms are compared by using the Bitcoin Elliptic Dataset. Moreover, with the objective of quantitively understanding the algorithmic performance differences, three key evaluation metrics are used: F1-score, precision and recall. Then, in order to understand the investigated algorithms applicability, two complementary qualitative interviews are performed at Swedish cryptocurrency exchanges. The study cannot conclude if there is a most suitable algorithm for detecting transactions related to money-laundering. However, the applicability of the decision tree algorithm seems to be more promising at Swedish cryptocurrency exchanges, compared to the other three algorithms.
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