In modern banking, institutions face both signature-based fraud (such as card-not-present attacks, account takeover, ATM cloning) and behavioral financial crimes (like structuring, layering, mule networks, business email compromise), which have fundamentally different detection requirements.
Traditional static rule engines are effective in catching brute-force and high-velocity events but are blind to behaviors like business email compromise (BEC) payment redirection, session hijacking, and money laundering layering, which appear indistinguishable from legitimate activities at the transaction or session level.
This paper presents an AI security agent for retail and corporate banking that addresses this gap through a three-component fusion architecture operating on two parallel event streams: a transaction stream (card fraud, ACH/wire fraud, AML categories) and a session stream (account takeover, session hijacking, SIM swap, insider abuse).
Each stream combines an LSTM sequence model to capture per-account behavioral history, a statistical velocity/threshold monitor, and a graph/network module to capture account-counterparty relationship patterns (fan-in, fan-out, pass-through ratio) for money laundering detection.
Experiments on a synthetic event log of 237,669 transactions and 113,508 sessions demonstrate an overall F1 score of 0.787 (transaction stream) and 0.867 (session stream) for the proposed model, compared to 0.562/0.733 for a rule-based baseline and 0.655/0.713 for an LSTM-only baseline.
The agent also includes a customer-facing transaction-verification chatbot (96.6% identity verification accuracy, 86.8% mass-reset attack detection) and an analyst case-summary assistant (99.3% action-recommendation F1), with Critical-tier automated response latency under 0.43 ms at the 95th percentile.
Blogger's Review: This paper showcases the potential of AI in financial security, significantly enhancing fraud and money laundering detection through multi-stream processing and deep learning techniques. As technology advances, AI security agents are expected to provide more comprehensive real-time monitoring and response capabilities.