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ered baselines and presents a robust, flexible, scalable solution for FinTech fraud detection for researchers and practitioners alike. This report examines the expansion of FinTech, its foundational technology, and the rising concerns related to security and privacy (1). Although it recognises problems and possible solutions, it
alance of datasets, or the evolving characteristics of fraudulent actions. In terms of detection, machine learning methodologies have proven effective but face limitations with redundant features and insufficient hyperparameter settings. The proposed study is a hybrid GA–RF architecture that optimizes feature selection and model adjustments to achieve higher detection accuracy with greater interpretability. The proposed s