A Comprehensive Comparative Study of Different Machine Learning Models for Fraud Detection Techniques

In recent years, the proliferation of online financial transactions, e-commerce platforms, and digital banking services has resulted in a concomitant surge in fraudulent activity. Identifying and mitigating fraud has emerged as a critical concern for enterprises, especially within the financial and banking industries. Conventional rule-based systems, although previously effective, frequently struggle to adjust to the always evolving and complex nature of fraudulent schemes. In this context, machine learning (ML) has emerged as a powerful and adaptable technology capable of analyzing extensive volumes of transactional data to reveal concealed patterns and abnormalities suggestive of fraud. This review study intends to deliver a comprehensive overview of several machine learning models employed in fraud detection, including supervised and unsupervised learning techniques, alongside ensemble and hybrid models. Each model is evaluated based on its precision, interpretability, computing efficiency, and effectiveness in identifying unusual and evolving fraudulent activities. The document delineates the assessment criteria prevalent in this field, addresses significant issues such data imbalance, feature selection, and real-time prediction demand, and underscores current innovations and practical implementations across diverse industries. The evaluation ultimately highlights current research deficiencies and proposes avenues for further investigation that may enhance the creation of more adaptable, transparent, and efficient fraud detection systems.