Article
Detection of Fraudulent Online Transactions Using Machine Learning Techniques
With the rapid growth of digital payments and online banking, detecting fraudulent transactions has become an important concern for financial organizations. Traditional detection methods often fail to identify fraud accurately due to the complex and evolving nature of fraudulent activities. This project presents an improved fraud detection system using the XGBoost machine learning algorithm, combined with Principal Component Analysis (PCA) to enhance feature selection and reduce data complexity. The model is developed and tested using a publicly available fraud transaction dataset from Kaggle. During implementation, various stages such as data preprocessing, feature extraction, model training, and evaluation are carried out systematically. The performance of the proposed model is assessed using key evaluation metrics including accuracy, precision, recall, and F1-score. To validate its effectiveness, the results are compared with the Naïve Bayes algorithm. The comparison shows that the XGBoost model performs significantly better in identifying fraudulent transactions, achieving accuracy above 99% along with improved precision and recall values. Additionally, the system is capable of predicting fraudulent activities in real-time using unseen test data. Overall, the results demonstrate that the proposed approach is efficient and reliable for detecting online transaction fraud, making it suitable for real-world financial applications.
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