Latest research in Financial Crime Detection focuses on leveraging machine learning and artificial intelligence to enhance the identification, prevention, and analysis of fraudulent activities across financial systems. Studies highlight the use of deep learning models, including Recurrent Neural Networks, Transformer encoders, and Autoencoders, often combined in hybrid frameworks, to capture complex patterns, sequential behaviors, and high-order feature interactions in transaction data. Systematic reviews and practical implementations by major financial institutions demonstrate the effectiveness of AI-driven approaches over traditional rule-based systems, enabling real-time anomaly detection, predictive risk assessment, and improved fraud mitigation. These advancements underscore the growing role of intelligent, data-driven systems in ensuring the integrity, security, and reliability of financial operations.