AI-Based Cybersecurity and Fraud Analytics for Healthcare Data Integration in Cloud Banking Ecosystems
DOI:
https://doi.org/10.15662/IJEETR.2025.0706020Keywords:
AI-based cybersecurity, fraud analytics, healthcare data integration, cloud banking, anomaly detection, deep learning, data privacy, regulatory complianceAbstract
The convergence of healthcare data platforms and cloud-based banking ecosystems has created unprecedented opportunities for digital payments, insurance claims automation, patient-centric financial services, and real-time risk assessment. However, this integration also introduces significant cybersecurity and fraud risks due to the sensitive nature of healthcare data, regulatory constraints, and the expanding cloud attack surface. This paper proposes an AI-driven cybersecurity and fraud analytics framework tailored for healthcare data integration within cloud banking ecosystems. The framework leverages machine learning, deep learning, graph analytics, and anomaly detection to secure data pipelines, detect financial and identity fraud, and ensure regulatory compliance. By integrating healthcare information systems, cloud-native banking platforms, and AI-powered security intelligence, the proposed approach enables proactive threat detection, adaptive fraud prevention, and resilient data governance. Experimental evaluations using simulated healthcare–banking transaction scenarios demonstrate improved detection accuracy, reduced false positives, and enhanced response time compared to traditional rule-based systems. The study highlights the importance of explainability, privacy-preserving learning, and federated analytics to meet healthcare and financial regulatory requirements. The findings suggest that AI-based cybersecurity and fraud analytics can play a critical role in securing cross-domain digital ecosystems while enabling innovation in healthcare-financial services integration
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