Explainable Multi Agent Architectures Using AI and Cloud for Cross Jurisdictional Healthcare Fraud Detection

Abstract

The rapid digitization of healthcare systems across global jurisdictions has introduced unprecedented challenges in detecting and preventing fraudulent activities. Traditional rule-based fraud detection systems are increasingly ineffective due to evolving fraud patterns, cross-border regulatory differences, and large-scale heterogeneous data environments. This paper proposes an Explainable Multi-Agent Artificial Intelligence (XAI-MAS) architecture deployed on cloud infrastructure for cross-jurisdictional healthcare fraud detection. The system integrates multiple intelligent agents responsible for data ingestion, anomaly detection, risk scoring, compliance validation, and decision explanation. Leveraging cloud-native scalability, the architecture processes structured and unstructured healthcare data, including insurance claims, electronic health records, and transactional logs.
Explainability mechanisms such as SHAP, LIME, and causal inference modules are embedded within agent workflows to ensure transparency, regulatory compliance, and trustworthiness. Multi-agent collaboration enables parallel processing and adaptive learning, significantly improving fraud detection accuracy while reducing false positives. The framework also incorporates federated learning to maintain data privacy across jurisdictions while enabling collaborative intelligence. Experimental insights from recent studies indicate that multi-agent AI systems outperform traditional models in adaptability and real-time detection capabilities.
This architecture provides a robust, scalable, and interpretable solution for modern healthcare ecosystems, ensuring secure, compliant, and efficient fraud detection across diverse regulatory landscapes.