The combination of Artificial Intelligence (AI) and predictive analytics can revolutionise financial risk management, especially in the banking and fintech domains. The needs of risk management, compliance are broad in the corporation and related academic papers are deep and strong on the matter the first 50 years only are discussed at a high level of these methodologies making solutions possible in identification, prediction risk mitigation for a large variety of risks credit default, fraud, liquidity crises, regulatory violations etc. The emphasis will be on affordably improving real- time analytics and adaptive algorithms to accommodate vast, disparate data streams of transaction logs, market feeds, and customer behavior. The proposed work introduce a new hybrid prediction framework combining neural network approaches with probabilistic modeling and ensemble learning for better accuracy and scalability. It also offers a glimpse into real-world legacy financial worlds and the upstart fintech players as this concept is proven empirically. These practical deployments illustrate the fact that AI systems challenge classical models in terms of accuracy, agility, risk transparency, and automated decisions. Next then discuss implementation challenges, ethical considerations, and potential paths forward for AI to be deployed within financial ecosystem.