The integration of Artificial Intelligence (AI) into the finance sector is reshaping decision-making, risk management, and financial analysis, driving both efficiency and innovation. However, the “black box” nature of many AI models presents critical challenges regarding transparency and interpretability, especially in high-stakes financial decisions. The inability to explain AI-generated results can lead to legal, ethical, and financial risks, including regulatory penalties and loss of stakeholder trust. Explainable AI (XAI) addresses these challenges by offering methodologies that enhance the transparency and interpretability of AI models. This chapter examines the role of XAI in financial decision making, providing a comprehensive review of XAI and its applications in areas such as risk management, portfolio optimization, lending services, fraud detection, and stock market analysis. It also explores the key challenges of implementing XAI, assesses the trade-offs between performance and interpretability, and outlines future directions to advance XAI to meet the ethical, regulatory, and operational needs of the rapidly evolving financial landscape.