International Research Journal of Education and Technology

Artificial Intelligence (AI) is transforming fraud detection by enabling real-time monitoring, pattern recognition, and adaptive learning. Traditional rule-based fraud detection systems struggle with the increasing complexity of fraudulent activities, leading to high false-positive rates and inefficiencies. AI-driven models, leveraging machine learning (ML) and deep learning (DL), enhance fraud prevention through intelligent analytics and predictive capabilities.

The adoption of AI in fraud detection has led to improved accuracy, faster identification of fraudulent activities, and significant reductions in financial losses. This paper explores the methodologies employed in AI-driven fraud detection, including supervised and unsupervised learning, anomaly detection, and neural networks. We also discuss challenges such as data privacy, model interpretability, and real-time processing constraints while evaluating potential solutions for overcoming these obstacles.