An Autonomous AI Security Agent for Banking: Multi-Vector Fraud and AML Detection Across Retail and Corporate Accounts
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 b...
| Authors | Joseph Walusimbi, Joshua Benjamin Ssentongo |
|---|---|
| Primary category | cs.CR |
| Published | 16 June 2026 |
| Last updated at source | 8 October 2026 |
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