Detecting financial frauds is crucial for maintaining the integrity and security of financial systems. This paper aims to compare the effectiveness of traditional machine learning models and advanced deep learning models, including large language models, in detecting financial fraud. Traditional models such as Random Forest and KNN were evaluated alongside deep learning models like Generative Adversarial Network (GAN), Artificial Neural Networks (ANNs), and large language models including GPT-2 and FinRoberta. Using financial filings from the U.S. Securities and Exchange Commission (SEC) and transaction statements, we assessed each model’s performance based on accuracy, precision, recall, and F1-score. The study highlights the strengths and limitations of each approach in terms of accuracy and reliability in detecting fraudulent activities. Results indicate that while traditional models offer solid baseline performance, deep learning models, and large language models significantly enhance detection accuracy, with FinBERT and GPT-2 showing promise due to their advanced natural language processing capabilities. The insights from this research provide valuable guidance for researchers, practitioners, and policymakers on leveraging advanced machine learning techniques to combat financial fraud more effectively. This study underscores the potential of integrating cutting-edge deep learning and language models into financial fraud detection systems to improve their efficiency and robustness. Moreover, the practical implications of these findings suggest that adopting these advanced models could lead to more proactive and precise detection of fraudulent activities, thereby enhancing financial security.