Presents a three-component fusion AI agent for multi-vector fraud and AML detection in retail/corporate banking using LSTM, statistical, and graph modules on synthetic data, reporting F1 scores of 0.787 (transactions) and 0.867 (sessions).
A survey of network anomaly detection techniques,
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UNVERDICTED 2representative citing papers
An AI agent for ACMIS uses supervised anomaly detection, behavioral analytics, and an NLP chatbot to report 0.966 macro F1 on simulated threat data, outperforming rule-based and LSTM baselines.
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An AI Security Agent for Banking: Multi-Vector Fraud and AML Detection Across Retail and Corporate Accounts
Presents a three-component fusion AI agent for multi-vector fraud and AML detection in retail/corporate banking using LSTM, statistical, and graph modules on synthetic data, reporting F1 scores of 0.787 (transactions) and 0.867 (sessions).
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An AI Security Agent for University ACMIS: Multi-Vector Threat Detection and Automated Response
An AI agent for ACMIS uses supervised anomaly detection, behavioral analytics, and an NLP chatbot to report 0.966 macro F1 on simulated threat data, outperforming rule-based and LSTM baselines.