FraudSentinel: Federated Multi-Agent Reinforcement Learning for Privacy-Preserving Cross-Marketplace Fraud Detection in Distributed E-Commerce Ecosystems

E-commerce fraud costs the global economy $48 billion annually, with sophisticated fraud rings operating across multiple online marketplaces. While individual platforms deploy fraud detection systems, fraudsters exploit the lack of cross-platform intelligence sharing. Traditional centralized fraud databases violate privacy regulations and create single points of failure. We present FRAUDSENTINEL, a federated multi-agent reinforcement learning framework that enables privacy-preserving fraud pattern sharing across distributed e-commerce marketplaces without exposing sensitive customer data. FRAUDSENTINEL introduces three key innovations: (1) a hierarchical federated multi-agent architecture where each…