INDAGO

Abstract

A seemingly never-ending issue with cryptocurrencies is their association with illegal activities. In 2020, it was estimated that roughly 3% of the transaction volume of Bitcoin consisted of transactions performed by known illicit actors. This is a problem for financial institutions wanting to integrate with cryptocurrencies since they risk incurring large fines if they are found to be complicit in illegal activities. This thesis set out to provide insight into this issue by developing a tool capable of detecting illicit funds on the Ethereum blockchain. By utilising DAR clustering and four different blacklisting algorithms and running them on publicly available Ethereum transaction information, the tool was able to detect approximately 160 million possibly illicit Ethereum addresses at varying levels of suspicion. It was also able to detect 965,719 unique clusters, of which 238,536 contained illicit addresses. The blacklisting algorithms involved had previously been described in the literature, but this is, as far as we know, the first time concrete implementations have been created and tested on real data. The effectiveness of the algorithms was evaluated in isolation and in aggregate. It was found that all of the blacklisting algorithms had possible use cases, though haircut and seniority showed the most potential for use in real-world scenarios as they spread the funds in a desirable way while also having a runtime considerably less than that of FIFO. DAR Clustering in combination with at least one of the blacklisting algorithms also showed potential as it was able to detect illicit addresses inside otherwise clean clusters. The findings of this thesis are limited to Ethereum with only partial generalizability to other cryptocurrencies.