<![CDATA[The rise of sophisticated financial fraud schemes in an increasingly digital economy has underscored the limitations of traditional rule-based detection systems. This study investigates the application of AI-powered systems for real-time financial fraud detection, integrating supervised, unsupervised, and hybrid machine learning approaches. A comparative evaluation of models such as Deep Neural Networks, Random Forests, Gradient Boosting, Autoencoders, and ensemble techniques was conducted using both static and streaming transaction data. Reports on accuracy, precision, recall, F1-score, latency and anomaly detection were reviewed. Deep Neural Networks had the most accurate results and Autoencoders were best at catching new fraud attempts with few false positives. It was established by statistical testing that model performance varied and concept drift detection indicated that retraining should be done continuously. Looking at feature importance confirmed that specific transaction details were explainable and useful in practice. Thanks to this work, we can identify how to make fraud detection systems more accurate, consistent and responsive which supports the growth of reliable and smart financial platforms.]]>
