Fraud Detection Pipeline Using Machine Learning: Methods, Applications, and Future Directions

The prevalence of fraudulent activities in various sectors such as finance, healthcare, and e-commerce has necessitated the development of robust fraud detection systems. This review article presents a comprehensive examination of the current state-of-the-art approaches in fraud detection pipeline architectures employing machine learning techniques. Key methodologies including supervised learning, unsupervised learning, and hybrid methods are discussed in detail, highlighting their application contexts, strengths, and limitations. Additionally, real-world applications of these machine learning solutions across diverse domains are explored, illustrating their practical relevance and impact. We also provide a forward-looking analysis of emerging trends and future directions in fraud detection, such as the integration of deep learning, ensemble methods, and real-time detection capabilities. This review aims to serve as a valuable resource for researchers and practitioners aiming to advance the field of fraud detection through innovative machine learning solutions.