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Integrated Enterprise AI Framework for Secure Financial Healthcare and Socio Digital Intelligence Ecosystems

Abstract

The rapid expansion of digital technologies has significantly transformed enterprise environments across financial, healthcare, and socio-digital sectors. Organizations increasingly rely on artificial intelligence (AI) to analyze large volumes of heterogeneous data generated through digital transactions, healthcare systems, and social platforms. However, the integration of these diverse data ecosystems presents critical challenges related to security, privacy, scalability, and intelligent decision-making. This research proposes an Integrated Enterprise Artificial Intelligence Framework designed to support secure analytics and intelligent insights across financial, healthcare, and socio-digital intelligence ecosystems. The proposed framework combines advanced AI analytics, cloud-based infrastructure, secure data management mechanisms, and multi-domain data integration techniques. The framework enables enterprises to process large-scale datasets while maintaining strong data governance, security controls, and privacy protection. AI models within the framework provide predictive analytics, anomaly detection, and decision support capabilities that assist organizations in risk management, healthcare diagnostics, and socio-economic analysis. The research methodology involves designing a layered enterprise architecture, implementing machine learning models for cross-domain analytics, and evaluating system performance using simulated datasets from financial, healthcare, and social digital platforms. Experimental results demonstrate improved data processing efficiency, enhanced security mechanisms, and better predictive intelligence across integrated enterprise systems. The proposed framework contributes to the development of intelligent and secure enterprise ecosystems capable of supporting modern data-driven decision-making.

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