Ledgit: A Service to Diagnose Illicit Addresses on Blockchain using Multi-modal Unsupervised Learning

Abstract

Distributed ledger technology benefits society by enabling an ecosystem of decentralised finance. However the pseudo-anonymised nature of transactions has also been an enabler of new routes for illicit activities ranging from individual scams to organised crimes. Current solutions for identifying addresses involved in illicit activities (illicit addresses) rely on commercial intelligence services, which are costly due to the intensive investigative efforts required. We propose Ledgit, an automatic real-time service for diagnosing illicit addresses on the Bitcoin blockchain. Ledgit is based solely on publicly available data, and uses an unsupervised clustering method that combines information from textual reports and the blockchain graph to assign a risk score that a Bitcoin address is involved in illicit activities. We verify the system with labeled addresses, showing high performance in identifying illicit addresses. Finally, we provide an intuitive user interface that provides accessible risk assessment with graph and report analytics.