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.

翻译：暂无翻译