Digital commerce's growth has driven the financial sector, particularly the banks, to seek intelligent, adaptable, and resilient financial ecosystems. While the existing AI applications in the sector can identify and manage certain processes, such as fraud and credit scoring, and are efficient, they do not possess an integrated architecture to support comprehensive financial intelligence across a range of digital banking activities. To discard this limitation, this research proposes a new approach, the Unified Cognitive Financial Intelligence Model (U-CFIM). This Model consists of three integrated layers: (1) a Cognitive Data Fusion Layer that in real-time integrates and synchronizes transactional, behavioral, and contextual data; (2) an Adaptive Intelligence Layer that utilizes various forms of machine learning, deep learning, and risk pattern-based analyses; and (3) a Hybrid Orchestration Layer that autonomously performs digital payment execution, anomaly notification, tailored compliance recommendations, and validation of legislative compliance. Through thematic analysis supported by multi-institution case studies, this Model produced value in the form of enhanced predictive accuracy and speed of performing decisions, as well as improved flows of customer-personalized digital commerce. This elucidates that the U-CFIM provides enhanced adaptability and a pathway to many banks with the vision to improve digital commerce and design, implement, and refine custom AI-powered financial ecosystems