TRACE – Transparency and Risk Assessment for Cross-Chain Compliance Evaluation
Project Summary
Blockchains are rapidly becoming part of financial infrastructure, but intelligence on high-risk addresses remains highly fragmented. Different institutions maintain their own lists and databases with incompatible formats, uneven update cycles and limited machine-readable access. Many sources report that an address is high-risk without giving clear, verifiable reasons. As a result, protocols and wallets lack a holistic view, find it hard to cross-check information, and often fall back on crude blacklist aggregation that can harm ordinary users and legitimate projects and amplify guilt-by-association.
This project will turn that fragmented, hard-to-audit ecosystem into an open, transparent and explainable risk intelligence layer. We will integrate data from official lists such as OFAC [1], commercial providers such as Chainalysis [2] and Elliptic [3], and community security projects, cleaning, deduplicating and standardising multi-chain address data. For each address, the system will generate a combined label-and-evidence record, including contributing sources, key behavioural patterns and cross-chain fund flows. Using rules and machine learning, we will distinguish genuinely dangerous addresses from those mislabelled or passively contaminated, reducing unnecessary stigmatisation. The results will be provided through an open website, query interface and downloadable datasets, supporting more robust counterparty risk management and research-grade transparency for regulators and the wider public.
Meet The Team
Dr Jiahua Xu
University College London
Dr Stefanos Chaliasos
University College London