Money laundering is a global phenomenon with wide-reaching social and economic consequences. Cryptocurrencies are particularly susceptible due to the lack of control by authorities and their anonymity. Thus, it is important to develop new techniques to detect and prevent illicit cryptocurrency transactions. In our work, we propose new features based on the structure of the graph and past labels to boost the performance of machine learning methods to detect money laundering. Our method, GuiltyWalker, performs random walks on the bitcoin transaction graph and computes features based on the distance to illicit transactions. We combine these new features with features proposed by Weber et al. and observe an improvement of about 5pp regarding illicit classification. Namely, we observe that our proposed features are particularly helpful during a black market shutdown, where the algorithm by Weber et al. was low performing.
翻译:洗钱是一种全球性现象,具有广泛的社会和经济后果,由于当局缺乏控制及其匿名性,隐秘性特别容易发生,因此,必须开发发现和防止非法隐秘货币交易的新技术。在我们的工作中,我们根据图表和过去标签的结构提出新的特征,以提高洗钱的机器学习方法的性能。我们的方法,内疚的Walker,在比特币交易图上随机行走,根据与非法交易的距离计算特征。我们将这些新特征与Weber等人提出的特征结合起来,并观察到在非法分类方面大约5pp的改进。也就是说,我们观察到,在黑市关闭期间,我们提议的特征特别有用,因为Weber等人的算法表现很低。