Purpose

This study aims to explore how to deanonymize cryptocurrency money launderers with the help of machine learning (ML). Money is laundered through cryptocurrencies by distributing funds to multiple accounts and then reexchanging the crypto back. This process of exchanging currencies is done through cryptocurrency exchanges. Current preventive efforts are outdated, and ML may provide novel ways to identify illicit currency movements. Hence, this study investigates ML applicability for combatting money laundering activities using cryptocurrency.

Design/methodology/approach

Four supervised-learning algorithms were compared using the Bitcoin Elliptic Dataset. The method covered a quantitative analysis of the algorithmic performance, capturing differences in three key evaluation metrics of F1-scores, precision and recall. Two complementary qualitative interviews were performed at cryptocurrency exchanges to identify fit and applicability of the algorithms.

Findings

The study results show that the current implemented ML tools for preventing money laundering at cryptocurrency exchanges are all too slow and need to be optimized for the task. The results also show that while not one single algorithm is most suitable for detecting transactions related to money-laundering, the specific applicability of the decision tree algorithm is most suitable for adoption by cryptocurrency exchanges.

Originality/value

Given the growth of cryptocurrency use, this study explores the newly developed field of algorithmic tools to combat illicit currency movement, in particular in the growing arena of cryptocurrencies. The study results provide new insights into the applicability of ML as a tool to combat money laundering using cryptocurrency exchanges.

中文翻译：

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用机器学习打击洗钱——监督学习算法在加密货币交易所的适用性

目的

本研究旨在探索如何借助机器学习 (ML) 对加密货币洗钱者进行去匿名化。通过将资金分配到多个账户然后将加密货币重新兑换回来，通过加密货币洗钱。这个交换货币的过程是通过加密货币交换完成的。当前的预防措施已经过时，机器学习可能会提供识别非法货币流动的新方法。因此，本研究调查了机器学习在打击使用加密货币的洗钱活动方面的适用性。

设计/方法/方法

使用比特币椭圆数据集比较了四种监督学习算法。该方法涵盖了算法性能的定量分析，捕捉了 F1 分数、精度和召回率三个关键评估指标的差异。在加密货币交易所进行了两次互补的定性访谈，以确定算法的适合性和适用性。

发现

研究结果表明，当前实施的用于防止加密货币交易所洗钱的机器学习工具都太慢了，需要针对该任务进行优化。结果还表明，虽然没有一种算法最适合检测与洗钱有关的交易，但决策树算法的特定适用性最适合加密货币交易所采用。

原创性/价值

鉴于加密货币使用的增长，本研究探讨了新开发的算法工具领域，以打击非法货币流动，特别是在不断增长的加密货币领域。研究结果为 ML 作为使用加密货币交易所打击洗钱的工具的适用性提供了新的见解。