Publication

GuiltyWalker: Distance to illicit nodes in the Bitcoin network

Catarina Oliveira, João Torres, Maria Inês Silva, David Aparício, João Tiago Ascensão, Pedro Bizarro

Published at KDD 2021 - Machine Learning in Finance workshop

Machine Learning

Abstract

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.

Materials
PDF arXiv

Page printed in 26 Nov 2022. Plase see https://research.feedzai.com/publication/guiltywalker-distance-to-illicit-nodes-in-the-bitcoin-network for the latest version.