<![CDATA[Financial fraud detection is crucial for protecting the integrity of financial markets and institutions globally. Recent advancements in machine learning (ML) and deep learning (DL) have dramatically enhanced the ability to detect and prevent fraudulent activities across various sectors. This review paper examines the implementation of ML and DL in fraud detection, highlighting the evolution from traditional methods to sophisticated models like neural networks, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). We explore different ML techniques such as supervised, unsupervised, and hybrid approaches, their effectiveness in handling large, imbalanced datasets, and their application in real-world scenarios. Special attention is given to the integration of technologies like blockchain and IoT with AI to innovate fraud detection frameworks. Despite the promising advancements, challenges remain, such as the need for large volumes of labeled data, potential model bias, and the black-box nature of many deep learning models. Future directions focus on enhancing model transparency, addressing privacy concerns, and expanding the use of federated learning. This review aims to demonstrate the effectiveness of current technologies and encourage their adoption in enhancing global financial security]]>
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