Data-Driven Secure APIs for Healthcare Data Security and Financial Fraud Detection Leveraging AI and Deep Learning
DOI:
https://doi.org/10.15662/IJEETR.2023.0505004Keywords:
Data-driven security, Secure APIs, Artificial intelligence, Healthcare data protection, Financial fraud detection, Machine learning, Cloud analytics, Deep LearningAbstract
The increasing volume and sensitivity of healthcare and financial data necessitate robust security mechanisms capable of real-time intelligence and adaptability. This paper proposes a data-driven secure API framework for healthcare data security and financial fraud detection leveraging artificial intelligence (AI) and deep learning techniques. The framework integrates secure API gateways with cloud-based analytics to enable controlled data access, real-time monitoring, and intelligent threat detection across heterogeneous healthcare and financial systems. Deep learning models are employed to learn complex patterns from large-scale transactional and clinical datasets, facilitating accurate anomaly and fraud detection while preserving data integrity and confidentiality. The secure API layer incorporates authentication, encryption, access control, and audit logging to ensure compliance with regulatory and privacy requirements. Experimental analysis demonstrates improved detection accuracy, reduced false positives, and enhanced scalability compared to conventional rule-based and non–AI-driven approaches. The proposed approach highlights the effectiveness of combining data-driven secure APIs with AI and deep learning to strengthen healthcare data protection and mitigate financial fraud in modern cloud-enabled environments.
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