Fraud detection using machine learning in e-commerce

Ang, Su Huan (2025) Fraud detection using machine learning in e-commerce. Final Year Project, UTAR.

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Abstract

The fast growth of e-commerce has resulted in a rise in fraudulent activities, posing significant challenges to the security and trust of online transactions. Traditional fraud detection methods often fall short in effectively identifying complex fraud patterns due to issues like data imbalance, misclassification of costly errors, and the evolving nature of fraud tactics. This research proposes a machine learning-based approach to improve fraud detection performance in e-commerce platforms. Resampling techniques like SMOTE, oversampling and under-sampling are applied to address class imbalance issue. The study aims to reduce false negatives and enhance the detection of rare fraudulent transactions. Ensemble models such as Random Forest, AdaBoost, and XGBoost, will be employed to capture complex patterns and improve model performance. A systematic model evaluation was conducted using metrics such as accuracy, F1-score, MCC, precision, recall and AUC to ensure robust performance. Experimental results showed that Random Forest combined with oversampling achieved the best trade-off between precision and recall, reducing false negatives while maintaining high overall accuracy. Robustness was further validated through testing on both synthetic datasets and the Kaggle dataset, confirming the model’s adaptability and reliability. Finally, the best-performing model was integrated into a Power BI dashboard, enabling real-time monitoring of fraud detection results and visualization of emerging fraud trends. This integration supports decision-making by providing stakeholders with timely insights. The study contributes to the development of adaptive fraud detection systems capable of mitigating financial risks and maintaining customer trust in the e-commerce sector.

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