blockchain compliance teams

Abstract: Tracing illicit funds on public blockchains remains largely manual. As funds move across accounts and undergo asset conversions, they become mixed with other inflows, requiring repeated queries and expert judgment to trace. Large language model (LLM) agents offer a way to automate this process, but their ability to reconstruct case-related transfer networks from limited clues remains unclear. To bridge this gap, we present AMLBench, an anti-money laundering (AML) benchmark built from 24 real-world cases and 5.05 million Ethereum transfer records. For each case, agents start from limited clues and explore these transfer records through composable transaction-query and fund-flow analysis tools, deciding which accounts and branches to pursue. The final tracing results are evaluated against case annotations, while tool interaction logs reveal how agents discover and select relevant transfers and at what resource cost. We also develop AMLAgent, which integrates fund-tracing guidance and query feedback to dynamically guide investigative decisions. With GPT-6 Astra, AMLAgent achieves macro averages of 80.82% transfer-level precision and 84.73% query coverage across 24 real-world cases. Further analysis of the large-scale Bybit case reveals substantial differences in transfer retention and token usage across models, even with similar submission precision. AMLBench establishes a reproducible benchmark for evaluating both tracing outcomes and investigative processes, laying the groundwork for more reliable and efficient autonomous AML investigations.

Abstract: Centralized exchanges (CEXs) are key gateways between traditional finance and digital asset markets, yet identifying their blockchain addresses remains challenging. Existing approaches often rely on opaque attribution labels or treat exchanges as secondary classification targets, leaving generalization to unseen exchanges insufficiently examined. This study develops a classification framework using Proof-of-Reserves (PoR) disclosures, with 3,033,857 Ethereum addresses from 15 CEXs and five Non-CEX categories. We derive 58 on-chain behavioral features and evaluate LightGBM against logistic regression (LR) under a leave-one-exchange-out protocol. Across five lookback windows (30-730 days), LightGBM consistently outperformed LR, with macro PR-AUC increasing from 0.6611 to 0.9594 using the selected 17 features, compared with 0.7697 for LR at 730 days. Robustness checks showed comparable performance when feature selection and hyperparameter optimization were repeated across splits and held-out exchanges, while exchange-balanced reweighting improved macro PR-AUC. SHAP analysis and feature-group ablation identified gas behavior, ERC-20 token activity, and internal transactions as key predictive signals in the constructed dataset. These findings support interpretable CEX address screening for blockchain monitoring, while the released feature dataset enables reproducible follow-up research.

Abstract: Illicit actors exploit Ethereum's deep liquidity and diverse on-chain services to launder criminal proceeds by combining decentralized finance (DeFi) protocols, cross-chain bridges, and mixers in multi-stage processes. Existing anti-money laundering tracing methods fail to disentangle the underlying asset-flow paths in complex transactions, potentially misdirecting subsequent tracing; cannot identify cross-chain intent without pre-collected bridge information; and treat mixers as generic exit services, overlooking post-mixer laundering flows. We present FPTracer, a framework for tracing illicit assets across Ethereum services. First, we propose a method based on the Context-Aware Transfer Tree (CATT) to recover intra-transaction asset-flow paths, which guide accurate tracing and service analysis. Second, we extract discriminative features from asset-flow and execution-evidence perspectives and train a classifier to identify transactions with cross-chain intent. Third, we leverage evidence from the upstream laundering network to analyze potential post-mixer laundering activities. Evaluation on 20 real-world laundering cases demonstrates FPTracer's effectiveness. It achieves the highest recall for both known laundering addresses (98.6%) and destinations (94.2%). It identifies all known uses of bridges in these cases and uncovers 30 previously unreported ones. Among 18 cases with identified Tornado Cash usage, FPTracer can identify potential withdrawal-side activities, which are corroborated by public reports where available.