Fraud Detection in Online Transactions Using Machine Learning

In the evolving landscape of online transactions, fraud detection remains a critical challenge due to the increasing sophistication of fraudulent activities. This paper explores the application of machine learning techniques to enhance fraud detection in online transactions. Various algorithms, including decision trees, random forests, support vector machines, and neural networks, are investigated to identify and mitigate fraudulent behavior effectively. The study utilizes a large dataset of transaction records, employing feature engineering to extract relevant attributes and enhance model accuracy. Through comparative analysis, it is demonstrated that ensemble methods, particularly random forests and gradient boosting, outperform traditional techniques in detecting fraudulent patterns. Additionally, the implementation of real-time detection systems using these models shows promising results in minimizing false positives and reducing financial losses. The findings highlight the potential of machine learning to offer robust, scalable solutions for fraud detection, contributing to more secure online transaction environments.