The increasing convenience of e-commerce and online payment systems has contributed to a rise in financial fraud incidents. This development has prompted significant research aimed at identifying effective techniques for detecting and preventing such fraud. Conventional approaches, such as rule-based systems or statistical models, face challenges due to imbalances in datasets and the constantly evolving tactics of fraudsters, which they struggle to manage. In contrast, sophisticated AI models, particularly deep learning methods, offer practical solutions to these issues. This paper compares various leading AI models for detecting financial fraud, assessing their advantages, disadvantages, and performance on standard datasets. The evaluation emphasizes critical factors such as accuracy, efficiency, and scalability, demonstrating the potential of these models to significantly impact the field of financial fraud detection. Additionally, this paper addresses the evolving dynamics of fraud and the need for models that can adapt in real time, highlighting future research directions for further advancements.