AML-view: A Realistic Institution-Centric Synthetic Dataset and Generator for Anti-Money Laundering

Anti-money-laundering (AML) investigation requires reasoning over a rich financial ecosystem using only one institution's evidence. Public data rarely expose this setting, while synthetic releases typically emphasize transactions or assume complete visibility. We present AML-view, a dataset and configurable generator that separates a shared, investigation-rich financial world from institution-local observations of it. AML-view integrates statistically grounded parties, ownership, accounts, due-diligence attributes, and payments, instantiates 38 documented laundering scenarios, and propagates illicit value through downstream transfers. Local views retain private records for customers but mainly transactional and public information for external counterparties. Each evaluated world contains about 10 million transactions over 70,000 accounts. AML-view covers all 13 audited investigation axes, versus 7 for the closest comparator, and passes all 35 population-calibration checks. By exposing matched institution-local views of the same financial world, AML-view reveals effects hidden by global-only evaluation: detection becomes harder for most models under realistic information boundaries, and the relative ranking of models can change. In blinded review, AML practitioners recovered laundering patterns from synthetic suspicious cases at a rate comparable to real cases, with recovery increasing from 0.500 using transactions alone to 0.765 with richer investigation context, while showing no consistent ability to distinguish synthetic from real cases. AML-view provides a large-scale, expert-validated, reproducible foundation for AML detection and investigation research.