Big Data-Driven Explainable Agentic AI Decision Frameworks for Enterprise Innovation in FinTech Ecosystems

FinTech ecosystems are growing at a rapid pace, creating large-scale, heterogeneous, and highly interconnected data environments that pose challenges to traditional frameworks for innovation management and decision support. Even while artificial intelligence (AI) is being used more and more to make use of this data, the majority of current methods are still opaque, reactive, and not well-suited to the needs of human-centered decision-making. In order to facilitate enterprise innovation in intricate FinTech ecosystems, this study suggests an explainable agentic AI-driven big data decision framework. The platform combines explainable big data analytics and visual analytics pipelines with autonomous AI agents that are capable of goal-directed reasoning, adaptive collaboration, and continuous learning. The suggested method permits transparent investigation of extensive financial, transactional, and behavioral data by fusing network-aware data modeling, agent-based decision orchestration, and interpretable machine learning processes. By converting agent recommendations into clear, traceable insights for strategic innovation planning, visual analytics interfaces further support human-AI co-decision-making. When compared with black-box AI models, the framework's capacity to improve decision accuracy, adaptability, and trust is demonstrated through a case-driven evaluation inside real FinTech scenarios. The findings show that by coordinating AI with organizational, ethical, and legal restrictions, explainable agentic AI can greatly enhance company innovation outcomes. By providing a scalable and comprehensible decision framework for next-generation FinTech innovation ecosystems, this work advances the developing field of agentic AI for explainable large data exploration and visual analytics.