The Role of User Behavior Patterns in Enhancing Fraud Detection in Online Banking: A Bibliometric Analysis

The complexity of fraud in online banking has outpaced traditional rule-based detection systems. This paper examines how user behavior patterns enhance fraud detection by integrating behavioral analytics with machine learning models to improve accuracy and reduce false positives. A bibliometric analysis using VOSviewer was conducted on 200 Google Scholar papers (2020–2024) to identify key trends. Terms like “user behavior” and “machine learning algorithms” emerged as central. Machine learning models, including neural networks and deep learning, were evaluated, with Convolutional Neural Networks (CNNs) achieving up to 95% accuracy. Challenges such as computational complexity, data privacy, and overfitting persist. Future research should explore hybrid models that integrate multiple data sources to further improve fraud detection systems. These findings align with IEEE's focus on cybersecurity and financial technology advancements.