Research on Fraud Detection focuses on developing advanced methodologies to identify and prevent fraudulent activities across financial, e-commerce, and digital service sectors. Recent approaches leverage deep learning techniques, including hybrid models combining Recurrent Neural Networks, Transformer encoders, and Autoencoders, to achieve high detection accuracy. Heterogeneous graph-based models capture complex relationships in transaction data, while explainable AI methods such as SHAP and LIME improve transparency and regulatory compliance. Additionally, incorporating human-in-the-loop feedback allows systems to adapt to evolving fraud patterns, enhancing overall performance. These innovations collectively aim to create robust, interpretable, and adaptive fraud detection frameworks capable of mitigating modern threats effectively.