Authors: Atharv Joshi
Abstract: The convergence of Artificial Intelligence (AI), Cloud Computing, and the Internet of Things (IoT) has enabled intelligent, data-driven transformation across healthcare and financial systems. However, the integration of these technologies presents significant challenges related to security, scalability, interoperability, and real-time decision-making. Healthcare and financial domains demand highly reliable and secure architectures due to the sensitive nature of their data and strict regulatory requirements. Existing solutions often address these technologies in isolation, resulting in fragmented architectures and increased exposure to operational and security risks. This paper proposes a unified artificial intelligence framework that securely integrates cloud and IoT infrastructures to support intelligent healthcare and financial applications. The framework adopts a layered architecture encompassing IoT data acquisition, cloud-based storage and processing, AI-driven analytics, and embedded security mechanisms. Machine learning and deep learning models are employed to enable predictive analytics, anomaly detection, and decision support while ensuring data confidentiality, integrity, and availability. The framework supports both real-time and batch data processing, enabling scalable and low-latency operations. The proposed framework is validated through healthcare and financial use case scenarios, including remote patient monitoring and real-time financial transaction analysis. Performance evaluation demonstrates improved system efficiency, enhanced decision-making accuracy, and robust security compared to traditional siloed systems. The results confirm that the unified framework effectively addresses integration challenges while maintaining compliance and adaptability. This research contributes a comprehensive and scalable solution for next-generation intelligent healthcare and financial ecosystems, offering a foundation for future advancements in AI-enabled cloud and IoT integration.
DOI: http://doi.org/10.5281/zenodo.18169572
