An Autonomous AI Security Agent for Banking: Multi-Vector Fraud and AML Detection Across Retail and Corporate Accounts

תקציר מקורי באנגליתarXiv:2606.17555v4 Announce Type: replace-cross Abstract: Banks face two threat families with fundamentally different detection requirements: signature-based fraud (card-not-present attacks, account takeover, ATM cloning) and behavioural financial crime (structuring, layering, mule networks, business email compromise). Static rule engines catch high-velocity events but remain blind to BEC payment redirection, session hijacking, and laundering layering, which are engineered to resemble legitimate activity at the individual level. This paper presents an autonomous AI security agent acting independently at low- and medium-risk tiers and escalating to a human analyst or compliance officer for high-risk and critical actions for retail and corporate banking using a three-component fusion archite