CogAgent: Self-Evolving Cognitive Agents for Multi-Source Fraud Detection in Heterogeneous Financial Networks

The growing sophistication of fraudulent activities across financial services, e-commerce marketplaces, and digital ecosystems has outpaced the detection capabilities of traditional rule-based and static machine learning systems. Agentic Artificial Intelligence (AI), a paradigm where autonomous AI agents can perceive, reason, plan, and act in dynamic environments, has emerged as a promising solution for real-time and adaptive fraud detection. This survey explores the role of agentic AI in combating various fraud types, including financial fraud, fraudulent sellers in online marketplaces, identity theft, and insider threats. We analyze underlying architectures, learning strategies, decisionmaking loops, and collaborative multi-agent systems that enhance detection accuracy and scalability. The paper also contrasts agentic AI with conventional approaches, reviews state-of-the-art implementations, highlights critical challenges such as adversarial robustness and interpretability, and discusses future research directions in federated learning, explainable AI, and compliance with evolving regulatory frameworks.