This article develops a comprehensive and practically oriented research contribution that synthesizes streaming analytics, deep learning, and pragmatic systems engineering to create an integrated framework for real-time detection and response to financial transaction fraud. The work builds on contemporary technical reports and peer-reviewed research on streaming platforms (Kafka, Spark, Flink), real-time fraud detection models, anti-money-laundering (AML) machine learning, and operational best practices in financial services (Rajeshwari & Babu, 2016; Abakarim et al., 2018; Nicholls et al., 2021; Saxena & Gupta, 2017). The proposed Integrated Streaming Fraud Response Framework (ISFRF) is defined by four tightly coupled layers—Event Ingestion & Stream Processing, Low-Latency Triage & Authorization, Contextual Enrichment & Deep Analysis, and Forensic Evidence & Governance—and is accompanied by a suite of engineering patterns for feature computation in streaming contexts, drift-adaptive learning, adversarial resilience, privacy-preserving forensic commitments, and human-in-the-loop adjudication. Methodologically, the article employs a structured conceptual synthesis of the literature and derives implementation prescriptions, evaluation metrics adapted to streaming contexts, and a multi-stage empirical agenda including realistic sandbox pilots, adversarial red-team testing, and cross-institutional studies for network-level detection. The analysis emphasizes trade-offs—latency vs. complexity, explainability vs. predictive power, and privacy vs. auditability—and offers concrete design decisions (such as hybrid on-chain/off-chain commitments for audit integrity and tiered model pipelines) to reconcile these tensions. Finally, the work identifies critical gaps in extant knowledge—particularly the scarcity of longitudinal, production-scale evaluations and adversarial field studies—and proposes a prioritized research roadmap for practitioners and scholars engaged in building resilient, trustworthy transaction monitoring systems.