The rapid integration of Artificial Intelligence (AI) and Machine Learning (ML) into the financial services sector has catalyzed a profound transformation in how consumer behavior is modeled, predicted, and managed. This research article provides an exhaustive investigation into the technical architectures and regulatory challenges defining the modern financial landscape. By synthesizing advanced predictive methodologies-ranging from gradient-boosted decision trees (XGBoost) and Recurrent Neural Networks (RNN) to classical Principal Component Analysis (PCA)-this study elucidates the mechanisms through which "decision engines" forecast consumer purchase propensities and market volatility. Beyond technical execution, the article delves into the critical socio-technical imperatives of the post-crisis paradigm, specifically focusing on the evolution of FinTech as a driver for financial inclusion and sustainability. A central pillar of this research is the rigorous examination of the emerging regulatory landscape, analyzing the three primary challenges of AI regulation as articulated by global governance bodies. Through a bibliometric and content analysis of the "AI Life Cycle," the study explores how global frameworks must balance the drive for innovation with the necessity of resilient policy. The findings suggest that while predictive accuracy in banking is reaching unprecedented heights, the systemic risks associated with black-box trading algorithms and talent transformation necessitate a 360-degree approach to governance. The research concludes that the future of finance lies at the intersection of algorithmic precision, ethical transparency, and regulatory convergence.