arXiv:2606. 16663v1 Announce Type: new Abstract: Money laundering through insurance claims poses a threat to insurers both through fraudulent payouts and reputational and regulatory risk.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.
arXiv:2607. 13469v1 Announce Type: cross Abstract: The banking sector increasingly relies on automated systems to monitor electronic transactions for signs of fraud, yet conventional rule-based approaches struggle with high false-positive rates and offer no justification for their outputs, limiting their utility for compliance teams.
arXiv:2607. 19266v1 Announce Type: cross Abstract: Fraud detection systems must scale with rising transaction volume while remaining explainable and reviewable.
arXiv:2608. 07471v1 Announce Type: cross Abstract: This study considers the task of applying artificial intelligence to recognize bank fraud.
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. 05101v1 Announce Type: new Abstract: The application of machine learning-based predictive algorithms to Anti-Money Laundering (AML) has grown rapidly, driven by the vast volume of financial transaction data available to banks.
arXiv:2607. 19259v1 Announce Type: cross Abstract: Financial statement fraud detection (FSFD) is crucial for market integrity but faces challenges from increasingly sophisticated schemes and under-utilized textual data in financial reports.