Secure Healthcare Intelligence using AI-Driven Predictive Systems Integrating Fraud Risk Analytics Cybersecurity and MMS with Cloud Computing and Data Warehousing
DOI:
https://doi.org/10.15662/IJARCST.2025.0806813Keywords:
Healthcare Intelligence, Artificial Intelligence, Predictive Analytics, Fraud Risk Analytics, Cybersecurity, Cloud Computing, Data Warehousing, Mobile Medical Systems (MMS)Abstract
The healthcare sector is increasingly dependent on digital intelligence to enhance clinical decision-making, optimize operational efficiency, and ensure data security. However, the rapid adoption of electronic health records, cloud platforms, and mobile medical systems (MMS) has exposed healthcare infrastructures to sophisticated cyber threats and financial fraud risks. This study proposes a secure healthcare intelligence framework that integrates AI-driven predictive systems with fraud risk analytics, cybersecurity mechanisms, cloud computing, and data warehousing. The framework leverages machine learning algorithms to predict fraudulent activities, detect anomalies, and support proactive risk management while ensuring compliance with healthcare data protection standards. Cloud-based architectures provide scalable storage and computational resources, while centralized data warehouses enable efficient data integration and real-time analytics. Advanced cybersecurity layers, including encryption, access control, and intrusion detection systems, safeguard sensitive medical and financial data. The proposed approach enhances trust, accuracy, and resilience in healthcare intelligence systems, enabling healthcare organizations to deliver secure, data-driven services. The research highlights the potential of combining artificial intelligence and secure cloud technologies to address emerging challenges in healthcare fraud detection, predictive analytics, and information security
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