Paper Contents
Abstract
The rapid growth of online transactions has increased the risk of fraudulent activities, posing significant challenges to businesses and consumers. Detecting and preventing online payment fraud is essential to maintain the security and trustworthiness of digital payment systems. This project aims to develop a machine learning-based system capable of identifying fraudulent transactions in real-time with high accuracy. The solution utilizes both supervised and unsupervised learning methods to analyze transaction data and detect irregular patterns indicative of fraud. Machine learning algorithms such as Random-Forest, Gradient Boosting, and Neural Networks are employed to classify transactions as legitimate or suspicious. Techniques like feature extraction, anomaly detection, and ensemble modeling are applied to improve detection performance. Additionally, data pre-processing steps, including balancing techniques like SMOTE, help manage class imbalances in transaction datasets. The system is designed for real-time fraud detection and is flexible for deployment in cloud environments to handle large volumes of transaction data efficiently. By implementing this approach, the project seeks to reduce financial losses, minimize false positives, and enhance the overall security of online payment platforms. This solution contributes to building a more trustworthy digital economy by safeguarding users and businesses from potential fraud
Copyright
Copyright © 2025 V.Sumedh. This is an open access article distributed under the Creative Commons Attribution License.
