Fraudulent Transaction Detection in Online Systems Using Random Forest and Gradient Boosting

Abstract

The rapid increase in online transactions has significantly raised the risk of fraudulent activities, leading to substantial financial losses. Traditional fraud detection methods often struggle to address the complexity and scale of modern digital fraud. This paper explores the application of machine learning techniques, specifically Random Forest and Gradient Boosting, to detect fraudulent transactions. Both algorithms are widely recognized for their ability to handle large, complex datasets and improve predictive accuracy. The study examines how these techniques work, with Random Forest focusing on ensemble learning and feature importance, and Gradient Boosting employing an iterative, stage-wise approach to correct errors from previous models. Key challenges in fraud detection, including class imbalance, data scarcity, the evolving nature of fraud, and high-dimensional data, are discussed in depth. The paper reviews relevant studies that have utilized machine learning for fraud detection in various contexts, including e-commerce and credit card fraud, highlighting the strengths and limitations of different approaches. It also examines strategies to mitigate challenges, such as resampling techniques and continuous learning. The findings emphasize that while machine learning offers significant improvements in fraud detection, continuous adaptation is essential to keep pace with evolving fraud tactics. By providing a comprehensive overview of machine learning in fraud detection, this research contributes valuable insights into enhancing security measures for digital transactions and financial systems.