Agentic AI for Real-Time Fraud Prevention in Digital Finance
Article Original Website Link: https://www.academicpublishers.org/journals/index.php/ijns/article/view/13112Keywords:
AI, Fraud, Finance, Prevention, DetectionAbstract
The exponential growth of digital financial transactions has created unprecedented opportunities for fraudulent activities, necessitating advanced technological interventions beyond traditional rule-based systems. This paper examines the emergence and application of agentic artificial intelligence (AI) systems for real-time fraud prevention in digital finance. Agentic AI represents a paradigm shift from passive detection mechanisms to autonomous, adaptive systems capable of independent decision-making, continuous learning, and proactive threat mitigation. Through comprehensive analysis of contemporary research and implementations, this study explores the methodological foundations, architectural frameworks, performance outcomes, and operational challenges of agentic AI in financial fraud prevention. The findings reveal that multi-agent reinforcement learning, deep neural networks, and autonomous decision-making architectures significantly outperform conventional approaches in detection accuracy, response latency, and adaptability to novel fraud patterns. However, implementation challenges including explainability requirements, data quality constraints, and regulatory compliance considerations remain critical barriers. This paper synthesizes current knowledge to provide insights for financial institutions, technology developers, and policymakers navigating the integration of agentic AI into fraud prevention infrastructures.
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