The rapid migration of financial services to cloud platforms has increased transaction scalability, accessibility, and processing efficiency while simultaneously creating a complex environment for detecting fraudulent activities in real time. Conventional rule-based mechanisms and isolated machine learning models often face limitations in identifying evolving fraud patterns, handling high-dimensional transaction data, and generating timely alerts without excessive false positives. This paper proposes an efficient Deep Belief Network (DBN)-based approach for real-time financial fraud identification and automated alert generation in cloud computing environments. The proposed architecture integrates cloud-based transaction ingestion, preprocessing, unsupervised representation learning, supervised fraud classification, risk scoring, and automated alert management into a unified analytical pipeline. The theoretical foundation is informed by machine learning studies demonstrating the value of neural, ensemble, and hybrid learning approaches for complex classification problems, while the financial fraud context is specifically positioned using the DBN-based cloud fraud detection framework reported by Lankala et al. (2025). The methodology emphasizes layered feature learning, dimensionality reduction, adaptive classification, and risk-aware alert prioritization. Analytical findings indicate that DBNs can provide an appropriate representation-learning mechanism for discovering nonlinear relationships among transactional variables, while cloud deployment supports elastic processing and continuous monitoring. The study further identifies latency, class imbalance, model interpretability, concept drift, and alert fatigue as major implementation challenges. The proposed approach provides a research-oriented architecture for combining deep representation learning with automated cloud-based fraud response and establishes a foundation for future empirical validation using real-world financial transaction datasets.