An Explainable AI Framework for Business and Healthcare Analytics with Financial Risk and Decision Support
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Abstract
The growing availability of complex, heterogeneous data in business, finance, and healthcare presents both significant opportunities and critical challenges for data-driven decision-making. This paper proposes an Explainable AI (XAI) framework for Business and Healthcare Analytics with Financial Risk and Decision Support that integrates multi-modal data sources, privacy-aware learning, and interpretable modeling techniques. The framework enables transparent risk assessment, predictive analytics, and decision support across interconnected domains such as financial performance, operational efficiency, and healthcare outcomes. By incorporating explainability mechanisms, the proposed approach enhances trust, regulatory compliance, and human understanding of AI-driven insights. Additionally, the framework supports responsible data sharing and robust analysis in sensitive environments, making it suitable for real-world deployment in finance- and healthcare-oriented organizations. Experimental evaluations demonstrate the framework’s ability to deliver accurate predictions while maintaining interpretability and privacy, thereby supporting informed and ethical decision-making.
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