Online Fraud Transaction Detection using Machine Learning

Abstract

The rapid growth of online transactions has significantly increased the prevalence of fraudulent activities, posing severe financial risks to both individuals and organizations. Traditional rule-based systems for detecting fraudulent transactions have proven insufficient due to their inability to adapt to evolving fraud patterns. In this context, machine learning (ML) offers a dynamic and effective solution for fraud detection by leveraging historical transaction data to identify anomalies and potential fraud in real-time.This paper presents an in-depth analysis of various machine learning techniques used for detecting online fraudulent transactions. We explore supervised learning models such as logistic regression, decision trees, and random forests, as well as advanced approaches like neural networks and ensemble methods. The study emphasizes the importance of feature engineering in enhancing model accuracy, highlighting key features such as transaction amount, time, location, and user behavior patterns. Moreover, we discuss the challenges associated with imbalanced datasets, which are common in fraud detection, and the strategies to overcome them, such as resampling techniques and anomaly detection models.To evaluate the effectiveness of the proposed models, we conduct extensive experiments using real-world transaction datasets. The results demonstrate that machine learning models, particularly ensemble methods, significantly outperform traditional approaches in detecting fraudulent transactions with high accuracy and minimal false positives. Additionally, we examine the deployment of these models in real-time fraud detection systems, addressing concerns related to scalability, latency, and interpretability.