Explainable AI with Scalable Deep Learning for Secure Data Exchange in Financial and Healthcare Cloud Environments
DOI:
https://doi.org/10.15680/IJCTECE.2023.0606017Keywords:
Explainable AI, deep learning, cloud computing, financial fraud detection, healthcare data security, anomaly detection, network security, AI-driven analytics, interpretability, complianceAbstract
The increasing digitization of healthcare systems and financial markets has driven unprecedented growth in cloud-based data management and real-time analytics. While cloud infrastructures provide scalability, high availability, and global accessibility, they also introduce significant security, privacy, scalable and fraud challenges. Financial transactions, trading activities, electronic health records (EHRs), and payment systems are highly sensitive and attractive targets for cyberattacks and fraudulent activities. Traditional security mechanisms and rule-based fraud detection approaches are often insufficient to address the complexity, volume, and dynamic nature of modern threats. This paper explores the application of explainable artificial intelligence (XAI) and deep learning techniques for securing financial markets and healthcare data exchanges in cloud environments. The proposed framework integrates deep learning models for anomaly detection, fraud intelligence, and network intrusion detection with XAI techniques to ensure transparency, interpretability, and compliance with regulatory standards. A comprehensive methodology covering secure data ingestion, preprocessing, feature engineering, model training, deployment, and explainable analytics is presented. Experimental results and literature-based evaluations demonstrate that XAI-enhanced deep learning systems improve detection accuracy, reduce false positives, and provide actionable insights while maintaining data privacy and regulatory compliance. The study concludes with future directions for federated learning, privacy-preserving AI, and adaptive cloud-based security architectures.
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