Cross-Domain Explainable AI for Customer Recommendations, Fraud Detection, and Network Intelligence

Abstract

Artificial Intelligence (AI)-enabled Decision Support Systems (DSS) have become fundamental components of modern enterprise ecosystems, facilitating intelligent automation across customer engagement, financial risk management, and network operations. Despite remarkable advances in deep learning and Large Language Models (LLMs), the opaque nature of these models presents significant challenges in terms of explainability, trustworthiness, accountability, and regulatory compliance, limiting their adoption in mission-critical decision-making environments. To address these limitations, this paper proposes an Explainable Multi-Agent Artificial Intelligence Framework for Decision Intelligence (XMAI-DI), a unified cross-domain architecture that integrates collaborative intelligent agents, retrieval-augmented enterprise knowledge, and explainable AI techniques to generate transparent, reliable, and auditable decision outcomes. The proposed framework employs specialized autonomous agents dedicated to customer recommendation and inventory optimization, fraud detection and financial risk assessment, and network anomaly detection and intelligent traffic management. A Retrieval-Augmented Generation (RAG) module enriches agent reasoning by dynamically incorporating enterprise knowledge repositories, while an Explainability Orchestration Layer combines SHAP-based global feature attribution, LIME-based local explanations, causal inference, and governance-aware auditing to provide comprehensive and human-interpretable decision justifications. Furthermore, an adaptive orchestration mechanism coordinates inter-agent communication, confidence estimation, and knowledge refinement to improve decision consistency and operational scalability across heterogeneous enterprise environments. Experimental evaluation across representative retail, financial, and networking scenarios demonstrates that the proposed framework significantly improves decision transparency, interpretability, operational efficiency, and governance compliance while maintaining competitive predictive performance. The integration of explainable reasoning, collaborative multi-agent intelligence, and enterprise knowledge retrieval establishes a scalable foundation for trustworthy next-generation Decision Support Systems capable of supporting responsible AI deployment in complex cross-domain enterprise applications.