Financial fraud is now a major threat for both consumers and institutions due to the growth of digital money. The complex and dynamic nature of contemporary fraud schemes makes it difficult for traditional detection methods, which are mostly focused on static rules and human checks, to stay up. This chapter explores how fraud detection procedures in financial ecosystems are being transformed by artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL). AI models, trained on large-scale transactional datasets, can now identify, forecast, and respond to fraudulent behaviors in real time with heightened precision. We explore a variety of AI methodologies, including both supervised and unsupervised learning, and delve into neural network applications for pattern recognition. Practical implementations from major financial organizations such as PayPal, Mastercard, and American Express are presented to illustrate the operational benefits of AI-driven fraud prevention systems. Difficulties like such as data security, algorithmic bias, and false positive management are critically examined. The chapter concludes by highlighting future directions, including the blockchain integration and federated learning to improve privacy and transparency, and the continued importance of human oversight in AI-powered decision-making.