With the rapid development of digital transactions and electronic banking systems, financial fraud has become one of the most prominent challenges facing financial institutions and e-commerce companies. To address these growing threats, machine learning technologies have emerged as effective tools in building intelligent systems capable of detecting fraudulents with accuracy and speed. The traditional fraud detection system is based on fixed rules (Rule-Based Systems), but these systems are often unable to keep up with renewed fraudulent methods. In contrast, machine learning-based fraud detection systems rely on the analysis of historical transaction data and the extraction of natural and abnormal behavioral patterns, enabling the system to distinguish between legitimate operations and suspicious activities. The mechanism of the system The machine learning-based fraud detection system consists of several main stages: 1. Data collection: includes financial transaction data, user behavior, geographic location, and type of device used. 2. Preprocessing: Cleaning data, processing missing values, and converting variables into analyzable formulas. 3. Feature Selection: Identifying the most influential characteristics in fraud detection. 4. Model training: using appropriate machine learning algorithms. 5. Performance evaluation: through metrics such as accuracy, recall, and false positives. The most prominent algorithms used • Logistic Regression: To classify transactions into fraudulent or non-fraudulent. • Decision Trees: To build a clear and easy classification model to explain. • Random Forest: To improve performance and reduce overfitting. • Supporting Vector Machine (SVM): To deal with high-dimensional data. • Deep Neural Networks: To analyze complex patterns and detect unusual behaviors. Advantages of using machine learning in fraud detection • Ability to adapt to new fraudulent patterns. • Reduce financial losses resulting from illegal operations. • Improve the speed of decision-making in real time (Real-Time Detection). • Reduce dependence on human intervention. Challenges Despite the high efficiency of these systems, they face several challenges, including: • Imbalanced Data (Imbalanced Data) due to the lack of fraudulent operations compared to normal operations. • The need for big and high-quality data. • Protect data privacy and ensure compliance with legislation. • The possibility of high rate of false alarms. Conclusion Analysis of a fraud detection system using machine learning techniques is an advanced step towards enhancing the security of digital financial systems. As algorithms continue to evolve and the volume of data increases, these systems are expected to become more accurate and effective, contributing to building a secure and sustainable digital environment