面向银行业的自主 AI 安全智能体：跨零售与企业账户的多向量欺诈与 AML 检测

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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 architecture across two parallel event streams: transactions (card fraud, ACH/wire fraud, AML) and sessions (account takeover, hijacking, SIM-swap, insider abuse). Each stream combines an LSTM sequence model of per-account behaviour, a statistical velocity/threshold monitor, and a graph module capturing account-counterparty patterns (fan-in, fan-out, pass-through ratio) for laundering detection. Experiments on a synthetic log of 237,669 transactions and 113,508 sessions across 13 threat categories and 3,470 accounts show that the agent achieves an overall F1 of 0.787 (transaction) and 0.867 (session), versus 0.562/0.733 for a rule-based baseline and 0.655/0.713 for an LSTM-only baseline. The agent also incorporates a customer-facing verification chatbot (96.6% identity accuracy, 86.8% mass-reset detection) and an analyst case-summary assistant (99.3% action recommendation F1), with critical-tier response latency under 0.43 ms at the 95th percentile.

Submission history

From: Joseph Walusimbi [view email]
[v1] Tue, 16 Jun 2026 05:58:40 UTC (47 KB)
[v2] Sun, 28 Jun 2026 14:10:36 UTC (49 KB)
[v3] Tue, 6 Oct 2026 22:36:17 UTC (46 KB)
[v4] Thu, 8 Oct 2026 05:40:53 UTC (46 KB)