Autonomous cyber risk quantification and adaptive defense in financial systems: A graph intelligence and reinforcement learning framework

Financial institutions increasingly operate within hyper-connected digital ecosystems exposed to sophisticated, multi-vector adversarial campaigns that propagate nonlinearly across interdependent assets, third-party vendors, cloud resources, and payment gateways. Existing risk management paradigms - anchored in static CVSS scoring, periodic NIST assessments, and rule-based monitoring - lack the temporal resolution and predictive capacity required to counter modern threat actors operating at machine speed. This article introduces the Autonomous Cyber Risk Quantification and Adaptive Defense Framework (ACRQ-ADF), a unified architecture integrating a Financial Asset Knowledge Graph (FAKG), Graph Attention Network (GAT) risk propagation engine, Transformer-based Threat Intelligence Fusion, Multi-Agent Reinforcement Learning (MARL) defense optimization, and a SHAP-driven Explainable AI compliance layer. Evaluated against enterprise-scale financial infrastructure simulations, ACRQ-ADF achieves 97.3% risk prediction accuracy, reduces Mean Time to Detect (MTTD) from 48 minutes to 7 minutes, compresses Mean Time to Respond (MTTR) from 6.5 hours to 32 minutes, and elevates NIST CSF 2.0 compliance coverage from 78% to 96%. These results demonstrate statistically significant operational superiority over both conventional governance frameworks and modern AI-augmented baselines, establishing ACRQ-ADF as a publishable contribution to next-generation financial cybersecurity architecture.