Purpose

In the age of DarkNetMarkets proliferation, combatting money laundering has become even more complicated. Constantly evolving technologies add a new layer of difficulty to already intricated schemes of hiding the cryptocurrency’s origin. Considering the latest development of cryptocurrency- and blockchain-related use cases, this study aims to scrutinize Italian and Russian antimoney laundering regulations to understand their preparedness for a new era of laundering possibilities.

Design/methodology/approach

One of the most recommended ways to buy and sell cryptocurrencies for illegal drug trade on DarkNet was discovered using machine learning, i.e. natural language processing and topic modeling. This study compares how current Italian and Russian laws address this technique.

Findings

Despite differences in cryptocurrency regulation, both the Italian Republic and the Russian Federation fall behind on preventing cryptolaundering.

Originality/value

The main contributions of this paper: consideration of noncustodial wallet projects and nonfungible token platforms through the lens of money laundering opportunities, comparison of Italian and Russian antimoney laundering regulations related to cryptocurrency, empirical analysis of the preferred method of trading/exchanging cryptocurrency for DarkNet illegal trade using machine learning techniques and the assessment of how Italian and Russian regulations address these money laundering methods.

中文翻译：

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意大利共和国和俄罗斯联邦针对加密货币洗钱技术的法规比较分析

目的

在 DarkNetMarkets 泛滥的时代，打击洗钱变得更加复杂。不断发展的技术为已经复杂的隐藏加密货币来源的方案增加了新的难度。考虑到加密货币和区块链相关用例的最新发展，本研究旨在审查意大利和俄罗斯的反洗钱法规，以了解他们为新时代的洗钱可能性做好准备。

设计/方法/方法

使用机器学习（即自然语言处理和主题建模）发现了在 DarkNet 上为非法毒品交易买卖加密货币的最推荐方法之一。本研究比较了当前意大利和俄罗斯法律如何处理这种技术。

发现

尽管加密货币监管存在差异，但意大利共和国和俄罗斯联邦在防止加密货币洗钱方面都落后了。

原创性/价值

本文的主要贡献：从洗钱机会的角度考虑非托管钱包项目和不可替代的代币平台，比较意大利和俄罗斯与加密货币相关的反洗钱法规，对暗网非法交易/交换加密货币的首选方法进行实证分析使用机器学习技术进行贸易，并评估意大利和俄罗斯的法规如何处理这些洗钱方法。