aiqbench analysis
This research introduces TCEE, a framework that integrates frozen time-series foundation models into graph neural networks to significantly enhance the detection of illicit financial activities by capturing temporal patterns in transaction histories. The approach demonstrates substantial performance gains across multiple datasets while reducing training time, offering a practical advancement for anti-money laundering systems that rely on complex network analysis.
Leveraging Time-Series Foundation Models for Financial Crime Detection
arXiv:2610.29667v1 Announce Type: new Abstract: Financial crime detection requires identifying illicit activity within large transaction networks, where suspicious behaviour is rare and unfolds over time. Existing graph-based methods capture relationships among accounts but typically underuse the temporal behaviour encoded in account transaction histories. We introduce Temporal-Context Edge Enrichment (TCEE), a framework that addresses this gap by summarising each account's transaction history with a frozen time-series foundation model (TSFM), and enriching each transaction's representation with the relevant temporal context from these summaries. These enriched representations can be scored directly for edge classification, or fed into a graph neural network (GNN) as edge features to capture broader structural information. Across six AMLWorld datasets, TCEE improves PR-AUC over the strongest evaluated baseline on five by a relative 10.8% on average, while training 6.0x faster end-to-end. Feeding TCEE's representations into a GNN raises PR-AUC a further 4.8% on the four smaller datasets. On an Ethereum phishing dataset, we show that TSFM-generated account representations fed into a GNN are also effective for node classification, improving PR-AUC by 14.5% over the strongest baseline. As TSFMs continue to advance, our results suggest they can strengthen financial crime detection by capturing temporal signals that complement graph-based approaches.
Abstract:Financial crime detection requires identifying illicit activity within large transaction networks, where suspicious behaviour is rare and unfolds over time. Existing graph-based methods capture relationships among accounts but typically underuse the temporal behaviour encoded in account transaction histories. We introduce Temporal-Context Edge Enrichment (TCEE), a framework that addresses this gap by summarising each account's transaction history with a frozen time-series foundation model (TSFM), and enriching each transaction's representation with the relevant temporal context from these summaries. These enriched representations can be scored directly for edge classification, or fed into a graph neural network (GNN) as edge features to capture broader structural information. Across six AMLWorld datasets, TCEE improves PR-AUC over the strongest evaluated baseline on five by a relative 10.8% on average, while training 6.0x faster end-to-end. Feeding TCEE's representations into a GNN raises PR-AUC a further 4.8% on the four smaller datasets. On an Ethereum phishing dataset, we show that TSFM-generated account representations fed into a GNN are also effective for node classification, improving PR-AUC by 14.5% over the strongest baseline. As TSFMs continue to advance, our results suggest they can strengthen financial crime detection by capturing temporal signals that complement graph-based approaches.
9 pages, 5 figures, 5 tables. Accepted at the ACM International Conference on AI in Finance (ICAIF '26), Milan, Italy
Machine Learning (cs.LG)
arXiv:2610.29667 [cs.LG]
(or arXiv:2610.29667v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.29667 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 308 words