Catch Me If You Can: A Multi-Agent Synthetic Fraud Detection Framework for Complex Networks

Detecting fraudulent behavior across diverse domains presents a significant challenge due to the adaptive and elusive activities of fraud agents. Furthermore, imbalanced data distributions and limited labeled examples increase the difficulty of detecting fraud agents. To address these challenges, we propose Catch Me If You Can—a Multi-Agent Framework to generate synthetic datasets and simulate various types of fraudulent behavior, including but not limited to anti-money laundering (AML), credit card fraud, bot attacks, and malicious traffic. Our framework comprises two core agent types: (1) Detectors, trained to identify suspicious patterns in scenarios, and (2) Transaction Agents, including both legitimate participants and adversarial fraud agents employing strategies to evade detection. In this framework, detectors iteratively refine their detection strategies while fraud agents evolve adaptive tactics to disguise illicit activities, creating an adversarial coevolutionary environment. This dynamic fosters the generation of high-dimensional and realistic datasets for training and testing. By integrating synthetic pre-training with transfer learning, the framework leverages a variety of real-world datasets—including IEEE-CIS Fraud Detection, Credit Card Fraud Detection, and Elliptic++—demonstrating its broad applicability across multiple fraud domains. Our approach significantly improves detection performance, bridging the gap between simulation and real-world applications. It enables robust training across heterogeneous fraud behaviors, contributing to the development of resilient, generalizable solutions for financial security and fraud prevention.