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.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.
arXiv:2606. 26189v1 Announce Type: new Abstract: Money Laundering Group Discovery (MLGD) aims to identify hidden criminal groups and recover their complete structures in large-scale financial networks.
arXiv:2608. 15177v1 Announce Type: cross Abstract: The increasing complexity of digital financial systems has reshaped financial fraud detection from isolated transaction classification into relational risk reasoning over interconnected financial entities.
arXiv:2605. 29526v2 Announce Type: replace-cross Abstract: Ever-evolving transaction patterns have significantly hindered anomaly detection on emerging cryptocurrency blockchains due to the vast number of addresses and diverse anomalous behaviors.
arXiv:2607. 19350v1 Announce Type: new Abstract: Financial institutions face significant challenges in detecting sophisticated money laundering patterns, such as smurfing and layering, due to extreme data imbalance (0.
arXiv:2607. 10131v1 Announce Type: cross Abstract: Money laundering threatens financial stability and exposes institutions to penalties, motivating automated detection.
arXiv:2607. 09528v1 Announce Type: new Abstract: The emergence of metaverse platforms has created virtual economies that introduce new challenges related to fraud, bot activity, and illicit financial behavior.