The rapid migration of financial services toward cloud computing has increased the volume, velocity, and heterogeneity of transaction data while simultaneously expanding the attack surface available to fraudulent activities. Conventional rule-based fraud detection mechanisms are increasingly constrained by their dependence on predefined patterns and their limited ability to identify evolving and previously unseen anomalies. This paper proposes a Deep Belief Network (DBN)-based intelligent framework for financial fraud detection and real-time alerting in cloud computing environments. The framework integrates cloud-based transaction ingestion, preprocessing, representation learning, fraud classification, risk scoring, and automated alert generation into a unified architecture. The theoretical foundation is derived from deep learning-based feature representation and anomaly-oriented intelligent detection, while the supplied literature provides supporting perspectives from machine learning, deep neural networks, intelligent detection frameworks, and real-time processing. Particular emphasis is placed on the application of DBN architecture to high-dimensional financial transaction streams, where latent representations can support more adaptive fraud discrimination. The proposed framework separates detection from alert prioritization so that high-risk transactions can trigger immediate responses while uncertain cases can be subjected to additional verification. The analysis indicates that the framework can improve adaptability, scalability, and automation compared with static detection approaches, although challenges remain regarding class imbalance, concept drift, computational overhead, explainability, and false-positive management. The framework extends the direction established by Lankala et al. (2025) by positioning DBN-based fraud intelligence within a broader cloud-native real-time alerting architecture.