Increased volatility in the global financial system necessitates highly desirable risk management tools that are both nimble and interpretable, capable of achieving quantifiable outcomes in real-world banking settings. This paper introduces the AI-Powered Cognitive Adaptive Risk Intelligence Framework (CARIF), a practical, multi-layered automation framework designed to operate as a high-accuracy, low-latency system for detecting, assessing, and mitigating financial risks. CARIF has combined AI capabilities, such as neuro-symbolic and adaptive learning engines, that incorporate the advantages of federated and simulation possibilities to allow predictive and prescriptive risk-based solutions. CARIF has been applied to extensive financial data artificially generated, mirroring realistic banking transactions, credit risk assets, and market volatile proxies, against relatively straightforward theoretical methodologies. The federated learning unit and neuro-symbolic approaches would be able to provide explainable decision paths for regulatory compliance and insurance of inter-bank collaboration, without violating customer privacy. The digital twin simulation environment will repeatedly test the risk models against changing macroeconomic conditions, providing advance notice of potential liquidity, credit, and operational risks. Experimental trials that acknowledge control levels have shown that CARIF has experienced a 17.8% enhancement in uncovering anomalies at an early stage, a 12.4% reduction in reverses, and an average reaction period that is 22% faster than existing machine operations-based risk apparatuses. The outcome of these findings suggests that agility and reliability in managing financial risks can be significantly enhanced when AI cognitive reasoning is complemented with adaptive learning and simulation-guided foresight. CARIF therefore provides a modular, regulation-invested, and result-validated strategy for next-generation automated decision support in the banking and financial industry.