Purpose

Money laundering is the process of concealing unlawfully obtained funds by presenting them as coming from a legitimate source. Criminals use crypto money laundering to hide the illicit origin of funds using a variety of methods. The most simplified form of bitcoin money laundering leans hard on the fact that transactions made in cryptocurrencies are pseudonymous, but open data gives more power to investigators and enables the crowdsourcing of forensic analysis. With the motive to curb these illegal activities, there exist various rules, policies and technologies collectively known as anti-money laundering (AML) tools. When properly implemented, AML restrictions reduce the negative effects of illegal economic activity while also promoting financial market integrity and stability, but these bear high costs for institutions. The purpose of this work is to motivate the opportunity to reconcile the cause of safety with that of financial inclusion, bearing in mind the limitations of the available data. The authors use the Elliptic dataset; to the best of the authors' knowledge, this is the largest labelled transaction dataset publicly available in any cryptocurrency.

Design/methodology/approach

AML in bitcoin can be modelled as a node classification task in dynamic networks. In this work, graph convolutional decision forest will be introduced, which combines the potentialities of evolving graph convolutional network and deep neural decision forest (DNDF). This model will be used to classify the unknown transactions in the Elliptic dataset. Additionally, the application of knowledge distillation (KD) over the proposed approach gives finest results compared to all the other experimented techniques.

Findings

The importance of utilising a concatenation between dynamic graph learning and ensemble feature learning is demonstrated in this work. The results show the superiority of the proposed model to classify the illicit transactions in the Elliptic dataset. Experiments also show that the results can be further improved when the system is fine-tuned using a KD framework.

Originality/value

Existing works used either ensemble learning or dynamic graph learning to tackle the problem of AML in bitcoin. The proposed model provides a novel view to combine the power of random forest with dynamic graph learning methods. Furthermore, the work also demonstrates the advantage of KD in improving the performance of the whole system.

中文翻译：

---

使用不断发展的图卷积和深度神经决策森林改进比特币的反洗钱

目的

洗钱是通过将非法获得的资金伪装成来自合法来源来隐藏这些资金的过程。犯罪分子利用加密货币洗钱，通过各种方法隐藏资金的非法来源。比特币洗钱最简单的形式很大程度上依赖于这样一个事实：加密货币进行的交易是匿名的，但开放数据为调查人员提供了更多权力，并实现了法医分析的众包。为了遏制这些非法活动，存在各种规则、政策和技术，统称为反洗钱（AML）工具。如果实施得当，反洗钱限制可以减少非法经济活动的负面影响，同时也促进金融市场的诚信和稳定，但这些都会给机构带来高昂的成本。这项工作的目的是激发机会将安全事业与金融普惠事业相协调，同时牢记现有数据的局限性。作者使用 Elliptic 数据集；据作者所知，这是任何加密货币中公开可用的最大标记交易数据集。

设计/方法论/途径

比特币中的 AML 可以建模为动态网络中的节点分类任务。在这项工作中，将引入图卷积决策森林，它结合了进化图卷积网络和深度神经决策森林（DNDF）的潜力。该模型将用于对 Elliptic 数据集中的未知交易进行分类。此外，与所有其他实验技术相比，知识蒸馏（KD）在所提出的方法上的应用给出了最好的结果。

发现

这项工作证明了利用动态图学习和集成特征学习之间的串联的重要性。结果表明，所提出的模型对 Elliptic 数据集中的非法交易进行分类的优越性。实验还表明，当使用 KD 框架对系统进行微调时，结果可以进一步提高。

原创性/价值

现有的工作使用集成学习或动态图学习来解决比特币中的 AML 问题。所提出的模型提供了一种将随机森林的力量与动态图学习方法相结合的新颖观点。此外，该工作还展示了KD在提高整个系统性能方面的优势。