AI-Driven Cyber Fraud Detection in Financial and Healthcare Ecosystems Using Cloud Deep Learning
DOI:
https://doi.org/10.15662/zyk99r25Keywords:
AI-driven fraud detection, cloud computing, deep learning, cybersecurity, financial markets, healthcare data security, network security, DevSecOpsAbstract
The rapid digital transformation of financial services and healthcare ecosystems has significantly increased exposure to sophisticated cyber fraud and security threats in cloud computing environments. Traditional rule-based and signature-driven security mechanisms are inadequate to detect complex, high-velocity fraud patterns across heterogeneous data sources. This paper proposes an AI-driven cyber fraud detection framework that leverages cloud-based deep learning architectures to provide scalable, real-time, and adaptive fraud intelligence for both financial and healthcare domains. The proposed model integrates transactional, network, and healthcare data streams using secure cloud data pipelines, enabling cross-domain correlation and anomaly detection. Deep learning techniques, including deep neural networks and recurrent architectures, are employed to learn non-linear fraud patterns and evolving attack behaviors, while network security analytics enhance threat contextualization and response accuracy. The framework supports cloud-native deployment with DevSecOps automation, ensuring continuous monitoring, rapid model updates, and regulatory-aware security controls suitable for financial compliance and healthcare data protection. Experimental evaluation demonstrates improved detection accuracy, reduced false positives, and enhanced resilience against advanced persistent threats compared to conventional machine learning approaches. The results highlight the effectiveness of AI-enabled cloud deep learning in delivering robust cyber fraud intelligence across interconnected financial and healthcare ecosystems.References
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