An AI-Driven Cloud Security Framework for Fraud Detection in Banking and Financial Markets Using Machine Learning and Deep Learning

Abstract

Financial fraud—ranging from credit-card theft and identity misuse to market manipulation—presents a persistent, evolving threat to banks, payment processors, and capital markets. This paper proposes an AI-driven cloud security framework that integrates scalable data ingestion, feature engineering, ensemble machine learning (ML) classifiers, and deep learning (DL) architectures with real-time streaming and cloud native security controls to improve fraud detection accuracy and timeliness. The framework combines supervised (random forests, gradient boosting, and deep neural networks) and unsupervised/anomaly detection (autoencoders, clustering, isolation forests) techniques to address class imbalance and concept drift, and leverages explainability modules for regulatory transparency. A layered cloud security posture—identity and access management (IAM), encryption, logging/monitoring, and compliance checkpoints (PCI-DSS/Risk matrices)—is embedded to meet financial regulatory requirements. Experiments on benchmark and synthetic datasets show significant gains in detection F1 and reduced false positives when hybrid ML+DL ensembles and continuous model re-training are applied in a cloud streaming environment. We discuss operational tradeoffs, privacy-preserving approaches (differential privacy, federated learning), and deployment considerations for low-latency scoring. The framework aims to deliver an adaptable, auditable, and scalable solution for modern banking and market infrastructures.