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arXiv:2608.22389v1 Announce Type: cross Abstract: Regulation (EU) 2024/886 obliges European payment service providers to settle euro credit transfers in under ten seconds, around the clock. This remo...
FinalityBench is an executable benchmark that tests how agents decide on shipping, re‑capturing, refunding, or waiting when a merchant’s payment processor, ledger, ERP, and bank feed receive delayed, duplicated, dropped, or reordered messages, causing contradictory beliefs about an order. The benchmark uses a hidden canonical event log and faulted delivery streams to generate system views, scoring each episode by the merchant’s terminal economic position relative to a privileged reference. It contains 321 tasks, including 45 twin pairs where all four views are identical yet the correct disposition differs, and evaluates nine programmatic policies, revealing that a ship‑on‑first‑sign policy performs best by accuracy but worst by paired loss, while a runtime‑gated irreversible‑action policy achieves 85.4% accuracy without losing money.
Money-mule networks move criminal funds through many small transfers, each of which looks ordinary on its own. What marks an account as a mule lies outside any single transfer: in the counterparties a...
The paper presents a graph‑based method for detecting money‑mule accounts by treating a bank’s relational schema as a heterogeneous graph and scoring each account using multi‑hop neighbourhoods, per‑column encoders, relative‑time encoding, and HeteroGraphSAGE message passing. On a five‑year RBIH retail‑banking dataset of 400 million transactions, the model achieves 83 % precision and a 0.5 % false‑alarm rate, recovering nine out of ten held‑out mules and outperforming tree‑based baselines in recall while maintaining precision deep into the alert queue. The approach can be deployed directly on a bank’s existing database without constructing a custom feature graph.
arXiv:2604. 17420v2 Announce Type: replace-cross Abstract: Money laundering poses severe risks to global financial systems, driving the widespread adoption of machine learning for transaction monitoring.
arXiv:2608. 02664v1 Announce Type: cross Abstract: Deploying ML models in regulated decision-making (credit underwriting, fraud detection, loan approval) requires demonstrating fairness and robustness to auditors without exposing model weights or customer data.