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Money laundering is a global phenomenon with wide-reaching social andeconomic consequences. Cryptocurrencies are particularly susceptible due to thelack of control by authorities and their anonymity. Thus, it is important todevelop new techniques to detect and prevent illicit cryptocurrencytransactions. In our work, we propose new features based on the structure ofthe graph and past labels to boost the performance of machine learning methodsto detect money laundering. Our method, GuiltyWalker, performs random walks onthe bitcoin transaction graph and computes features based on the distance toillicit transactions. We combine these new features with features proposed byWeber et al. and observe an improvement of about 5pp regarding illicitclassification. Namely, we observe that our proposed features are particularlyhelpful during a black market shutdown, where the algorithm by Weber et al. waslow performing.

GuiltyWalker: Distance to illicit nodes in the Bitcoin network