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Suspicious Transaction Detection In Bank Transactions Using Agentic AI.

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Titel: Suspicious Transaction Detection In Bank Transactions Using Agentic AI.
Autoren: Wali, Girish, Sivathapandi, Praveen
Quelle: Cuestiones de Fisioterapia; 2025, Vol. 54 Issue 2, p4827-4836, 10p
Schlagwörter: MACHINE learning, FRAUD investigation, BANK fraud, ARTIFICIAL intelligence, FRAUD
Abstract: Banking fraud has become a serious issue, with financial institutions struggling to detect suspicious transactions effectively. Traditional fraud detection methods often fail due to evolving fraudulent techniques. This paper explores the use of Agentic AI to identify suspicious bank transactions with greater accuracy and efficiency. Agentic AI, which operates with more autonomy and adaptability than traditional AI models, can analyze transaction patterns, detect anomalies, and make intelligent decisions in real time. The study implements an AI-driven detection model using machine learning techniques and evaluates its performance on a bank transaction dataset. The results show that Agentic AI improves fraud detection accuracy while reducing false positives. This research highlights the potential of intelligent AI agents in securing financial transactions. Future work can focus on improving real-time detection and integrating advanced AI techniques to handle emerging fraud patterns more effectively. [ABSTRACT FROM AUTHOR]
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Datenbank: Biomedical Index
Beschreibung
Abstract:Banking fraud has become a serious issue, with financial institutions struggling to detect suspicious transactions effectively. Traditional fraud detection methods often fail due to evolving fraudulent techniques. This paper explores the use of Agentic AI to identify suspicious bank transactions with greater accuracy and efficiency. Agentic AI, which operates with more autonomy and adaptability than traditional AI models, can analyze transaction patterns, detect anomalies, and make intelligent decisions in real time. The study implements an AI-driven detection model using machine learning techniques and evaluates its performance on a bank transaction dataset. The results show that Agentic AI improves fraud detection accuracy while reducing false positives. This research highlights the potential of intelligent AI agents in securing financial transactions. Future work can focus on improving real-time detection and integrating advanced AI techniques to handle emerging fraud patterns more effectively. [ABSTRACT FROM AUTHOR]
ISSN:11358599