This article develops a comprehensive, publication-ready synthesis and original framework for near real-time credit card fraud detection grounded in streaming analytics, deep learning, and pragmatic system design. Drawing from empirical and methodological literature on real-time fraud detection, streaming platforms (Kafka, Spark, Flink), deep learning architectures, large-scale anomaly detection, and operational constraints in financial systems, the paper articulates a resilient architectural pattern that balances latency, detection accuracy, explainability, and data governance (Abakarim et al., 2018; Rajeshwari & Babu, 2016; Martín Hernández, 2015; Hebbar, 2025). The proposed Convergent Streaming Detection Framework emphasizes a tiered detection pipeline: ultrafast rule-based triage in the streaming path, lightweight explainable models for immediate scoring, and contextual deep models (including sequence and graph-based learners) operating on enriched windows for elevated scrutiny (Nicholls et al., 2021; Zhou et al., 2019). Practical considerations include feature engineering for streaming contexts, approaches to class imbalance and concept drift, strategies for low-latency model serving, and hybrid provenance and logging to preserve forensic trails without violating privacy or incurring prohibitive storage and throughput costs (Saxena & Gupta, 2017; Nguyen et al., 2020). The article also details rigorous evaluation metrics appropriate to streaming fraud contexts, an experimental design for realistic pilot deployments, adversarial threat modeling, and a multi-year research agenda emphasizing red-team testing and socio-technical evaluation. The synthesis stresses that engineering trade-offs—between latency and model complexity, explainability and predictive performance, and on-chain/off-chain evidence storage—must be made transparently and governed by regulatory and user-centric considerations (The Business Research Company, 2025; Udeh et al., 2024). The contribution is a practically actionable blueprint for researchers and practitioners seeking to deploy deep-learning-driven fraud detection in production-grade streaming environments.