Blockchain Forensics: Tracing Funds and Scoring Transaction Risk
Summary
The document outlines methods for investigating suspicious cryptocurrency activity through transaction graphs, address clustering, and tracing funds across chains. It explains that investigators may infer common control from shared transaction inputs, but complex transactions and privacy tools can make those attributions unreliable. Bridges and atomic swaps can complicate tracing as assets move between networks.
It describes connecting on-chain flows with exchange and service records, including KYC information, to associate pseudonymous addresses with verified accounts when funds reach regulated platforms. Transaction monitoring models can score wallets using counterparties, transaction size, frequency, timing, and chain-hopping patterns; anomaly detection and calibrated thresholds can help prioritize alerts. Suggested casework includes sanctions checks and identifying laundering, manipulation, phishing, and other fraud patterns. The material is a broad compliance overview rather than a validated forensic procedure: it supplies no measured accuracy or evidence that any particular heuristic or model reliably identifies illicit conduct.
Key ideas
- Transaction graphs and address clustering can help map fund flows and infer links between wallets.
- Shared-input attribution is only a heuristic and can fail when transaction structures or privacy tools obscure ownership.
- Cross-chain tracing follows funds through bridges, swaps, and their eventual arrival at regulated exchanges.
- Off-chain exchange records and KYC data can connect some pseudonymous wallets to verified identities.
- Risk scores can combine transaction patterns and counterparties, but thresholds need calibration and alerts require investigation.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.