Implementation of Random Forest Algorithm for Online Transaction Fraud Detection

Abstract

Online transaction fraud presents a significant challenge to digital commerce, causing financial and reputational risks for businesses, financial institutions, and consumers. Traditional fraud detection systems often struggle to keep up with the evolving nature of fraud, leading to high false positive rates and undetected fraudulent activities. This study explores the use of the Random Forest algorithm as a machine learning solution for online transaction fraud detection. The research focuses on optimizing the Random Forest algorithm to address challenges such as class imbalance, feature selection, and computational efficiency. Techniques like the Synthetic Minority Oversampling Technique (SMOTE) are employed to balance the dataset, and feature engineering is used to identify the most relevant transaction attributes for fraud detection. The model's performance is evaluated using metrics such as accuracy, precision, recall, F1-score, and AUC-ROC, and is compared with other machine learning methods, including Support Vector Machines, Gradient Boosting, and Neural Networks. The results show that the optimized Random Forest model provides high accuracy in detecting fraudulent transactions while minimizing false positives. Key features influencing fraud detection are identified, offering valuable insights into fraud patterns. Additionally, the study demonstrates the model's computational efficiency, making it suitable for real-time implementation in digital financial systems. This research contributes to the development of more robust and scalable fraud detection frameworks, enhancing the security of online transactions. The findings highlight the potential of machine learning algorithms like Random Forest in reducing fraud risks and strengthening trust in digital commerce.