AI-Enhanced Cloud Security Architecture Machine Learning–Driven Fraud Detection for Preventing Business Financial Losses

Abstract

Cloud-hosted financial services and business platforms face an expanding attack surface as enterprises migrate critical systems and payment processing to cloud environments. Machine learning (ML)–driven fraud detection offers scalable, adaptive defenses to identify anomalous transactions, account takeover attempts, and emerging attack patterns in near real time. This paper proposes an AI-enhanced cloud security architecture that integrates native cloud telemetry, feature engineering pipelines, streaming analytics, and hybrid supervised–unsupervised ML models to detect and prevent financial losses. The proposed architecture emphasizes data privacy, model explainability, and operational resilience: it leverages federated feature aggregation where necessary, anomaly scoring with unsupervised models for zero-day behaviors, ensemble classifiers for known fraud patterns, and an automated response layer to quarantine or throttle suspicious activity. We describe a research methodology combining offline model training on historical labeled data, online evaluation with shadow deployments, and continuous learning via human-in-the-loop feedback. Evaluation metrics include detection rate, false-positive rate, time-to-detect, and business loss reduction. Results demonstrate that the combined ensemble—tuned for low false positives and augmented with explainability modules—reduces financial loss in simulations while preserving customer experience. The design balances detection efficacy, privacy constraints, deployment cost, and operational complexity, and we close with recommended future work including real-world pilot deployment and integration with threat intelligence feeds.