Proactive Healthcare Cyber Defense Using a Secure AI-Cloud and Machine Learning Framework for Finance
DOI:
https://doi.org/10.15680/IJCTECE.2024.0706016Keywords:
Cloud security, fraud detection, machine learning, anomaly detection, graph analytics, behavioral biometrics, ensemble models, model explainability, automated response, privacy-preserving analyticsAbstract
Cloud environments host a growing share of enterprise financial services and transaction-processing workloads, attracting sophisticated scam-driven attacks that cause direct monetary losses and long-term reputational harm. This paper presents an integrated AI-powered Cloud Fraud Intelligence (CFI) framework that leverages machine learning (ML), behavioral analytics, graph-based link analysis, and cloud-native telemetry to detect, prioritize, and mitigate scam-driven fraud in near real time. We describe a layered architecture combining: (1) distributed data ingestion from cloud services and financial logs; (2) feature engineering pipelines that produce transactional, behavioral, and network features; (3) hybrid ML models (ensemble of gradient-boosted trees, deep learning for sequence modeling, and unsupervised anomaly detectors) tuned for imbalanced data; and (4) automated response orchestration that integrates with identity, access, and payment controls. Through simulated and retrospective evaluations on mixed synthetic and anonymized enterprise datasets, the CFI framework demonstrates improved detection recall for novel scams, reduced false positives via contextual enrichment, and faster time-to-containment using automated playbooks. We discuss operational considerations for deployment in cloud-first organizations—privacy-preserving data handling, model governance, drift monitoring, explainability, and regulatory compliance. The paper concludes with limitations, directions for improving adversarial robustness, and a research agenda for federated and privacy-preserving fraud detection across multiple institutions.
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