Artificial intelligence (AI) is quickly changing the financial sector, making the operational processes efficient, the decision-making process more accurate, and customer relationship more engaging. This review explores the key finance-related uses of AI, such as fraud detection, credit scoring, algorithmic trading, automation of customer care, and the new use of generative AI. The use of AI-based systems helps financial institutions to operate large amounts of structured and unstructured data, identify intricate patterns, and automate fast transactions to enhance risk management and service provision. Machine learning models are used in credit markets to improve the accuracy of prediction and financial inclusion by analyzing alternative data. In capital markets, AI is used to optimize portfolios and provide sentiment-based predictions, and conversational AI applications are used to tailor their customer experience. Although these developments have been made, there are enormous obstacles to the adoption of AI. Such matters as transparency, algorithm bias, data privacy, cybersecurity, and regulatory complexity bring about accountability and systemic risk concerns. Market volatility can also be increased in times of financial stress by intensifying dependence on automated models. This review suggests that explainable AI systems, fairness auditing systems, and sound governance systems should be implemented to make AI accountable. In general, despite all the substantial opportunities of AI as a source of innovation and competitiveness, the sustainable integration of AI necessitates a balance between the technological advancement and ethical, regulatory, and financial stability