The analysed studies together highlight the progression of fraud detection techniques from conventional machine learning methods to hybrid and deep learning frameworks. Although models like Random Forest, Adaboost-LGBM, and deep neural networks attain great accuracy, they frequently encounter drawbacks such as computational inefficiency, insufficient interpretability, and suboptimal performance on imbalanced FinTech datasets.