  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.
