Periscope: A Single Bank's View into Cross-Institutional Laundering

Graph neural networks have become the dominant approach to anti-money laundering (AML), yet progress is held back by the scarcity of public data that reflect how banking systems actually operate. Existing datasets typically compress banking records into account-to-account transfer graphs, discarding the ownership ties and cross-entity relations on which practical risk analysis relies. This mismatch restricts both the credibility of public evaluation and the development of models able to exploit richer financial structure. We present PERISCOPE, a simulation-based AML testbed grounded in real banking data. Unlike prior public datasets, PERISCOPE retains the complete customer, account, and transaction schema of a single-bank operational setting, including ownership relations over both internal and external accounts, together with fine-grained transaction-level timestamps. We document the construction pipeline, assess the fidelity of the simulation, and provide a detailed characterization of anomalous users and accounts. Our analysis shows that associations among external entities reveal additional structural links between suspicious multi-account users that remain invisible in typical AML scenarios. Building on PERISCOPE, we conduct a systematic empirical study at both the user and the account level. The results show that retaining the richer structure consistently improves AML performance over simplified transfer-graph constructions. Meanwhile, current leading specialized AML models bring only limited gains over generic graph baselines and scale poorly on our dense data. These findings indicate that the main bottleneck of public AML research lies not only in data scarcity but also in the overly simplified graph abstractions adopted by existing datasets. Overall, PERISCOPE offers a grounded single-bank AML testbed, together with concrete evidence on how customer, account, and transaction structure and external-entity associations advance graph-based AML evaluation and modeling.