The increasing reliance on Artificial Intelligence (AI) in financial services has accelerated the demand for transparency and accountability in automated decision-making systems. However, the opaque nature of many high-performing Artificial Intelligence models present a significant research gap—how to maintain predictive accuracy while ensuring interpretability, especially in high-stakes domains such as credit assessment, fraud detection, and risk management. This chapter explores the emerging field of Explainable Artificial Intelligence (XAI) and its critical role in aligning algorithmic outputs with stakeholder expectations across regulatory, institutional, and consumer levels. By comparison of different Artificial Intelligence models like SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) and interpretable neural networks the study evaluates how Explainable Artificial Intelligence tools improve understanding without significantly compromising performance. The findings demonstrate that explainability enhances user trust, supports regulatory compliance, and increases customer acceptance—especially when decisions affect credibility. The significance of this study lies in its actionable insights for diverse stakeholders: for regulators, it supports enforceable standards; for financial institutions, it enables responsible innovation; and for customers, it fosters transparency and fairness. By establishing a performance-transparency equilibrium, this research advocates for strategic deployment of Explainable Artificial Intelligence in finance, balancing transparency, trust and performance.