Multi-Agent Systems for Coordinated Fraud Detection in Tax Networks

With the increasing complexity and digitalization of global tax systems, traditional fraud detection mechanisms struggle to address emerging threats such as shell companies, under-invoicing, and cross-border evasion. Rule-based and isolated systems lack the adaptability, scalability, and contextual awareness needed to combat coordinated fraud schemes effectively. This paper proposes a Multi-Agent System (MAS) architecture for intelligent, decentralized tax fraud detection. The MAS framework comprises autonomous agents that observe taxpayer behavior, detect anomalies, and collaborate in real-time for joint analysis. The architecture is layered into Detection Agents (for identifying irregularities using statistical and machine learning models), Coordination Agents (to enable inter-agent communication and fraud consensus), and Learning Agents (to adapt models based on confirmed fraud cases). A semantic ontology layer standardizes tax data formats across departments, enabling interoperability, while intra-agent negotiation resolves disputes on suspicious transactions. The architecture was tested using synthetic yet realistic data in a simulated national tax network. Results demonstrate improved fraud detection accuracy, scalability, and reduced response time compared to traditional systems. The MAS-driven approach aligns with the goals of modern revenue authorities by promoting efficiency, transparency, and collaboration. Future enhancements could include blockchain integration, federated learning, and human-in-the-loop frameworks for improved auditability and privacy.