The rapid proliferation of data, the ubiquity of distributed computing, and the maturation of artificial intelligence (AI) have created both unprecedented opportunities and acute challenges for financial risk management. This article synthesizes theoretical foundations, engineering architectures, regulatory considerations, and applied techniques for building resilient real-time risk management systems that integrate event-sourced data architectures with advanced AI and continuous monitoring frameworks. Drawing on foundational texts in quantitative risk management and risk modeling, as well as contemporary case studies, practitioner reports, and domain-specific research on event sourcing, real-time monitoring, and backtesting protocols, the paper constructs a comprehensive conceptual and methodological blueprint. It introduces a layered methodology that maps business objectives to data governance, stream processing, model lifecycle management, and regulatory compliance, and it explicates how event sourcing and stream processing (e.g., Kafka-style architectures) provide the immutability, auditability, and temporal granularity necessary for robust backtesting, counterfactual analysis, and stress scenarios. The analysis situates real-time AI risk systems within the broader landscape of disaster-risk modeling, supply-chain risk, ESG integration, and adverse outcome pathways to show cross-disciplinary applicability. The results section offers a descriptive synthesis of expected system behaviors, failure modes, and governance levers rather than empirical numerical outputs, reflecting the textual and prescriptive nature of the study. The discussion provides detailed interpretations of trade-offs between latency, accuracy, interpretability, and regulatory traceability, and it outlines a research agenda and practical roadmap for implementation in financial institutions, fintechs, and regulatory sandboxes. The conclusion summarizes actionable principles and stresses the imperative for multidisciplinary governance combining technical, organizational, and regulatory controls.

Real-time risk management, event sourcing, AI for finance

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