An Analytical Study on AI and Machine Learning Techniques for Financial Fraud Detection

Abstract This research analyzes the application of Artificial Intelligence (AI) and Machine Learning (ML) models in detecting financial fraud in digital transactions. Traditional rule-based systems often fail to adapt to evolving fraud patterns. The study focuses on comparing supervised, unsupervised, and hybrid models to identify those that provide higher detection accuracy and adaptability. Algorithms such as Logistic Regression, Random Forest, XGBoost, Autoencoders, and LSTM are evaluated using performance metrics like accuracy, precision, recall, and F1-score. Findings indicate that ensemble and deep learning approaches significantly enhance detection performance while maintaining real-time scalability.