International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Fraud Detection in Online Shopping Using Xgboost

Author(s) Prof. Dr. Miruna Joe Amali, Ms. Dharshini K, Ms. Iswarya A
Country India
Abstract The Dynamic Fraud Detection System presents an intelligent and adaptive approach to securing online shopping platforms by identifying fraudulent transactions in real time. Unlike traditional rule-based systems, this framework continuously monitors user behavior and transactional features—including device type, transaction speed, account age, and location anomalies—to detect potential fraud automatically. Leveraging an optimized XGBoost machine learning model, the system assigns a fraud score (0–100) representing the likelihood of fraudulent activity, with higher scores triggering automated alerts to administrators. The integration of SHAP-based explainability provides transparent insights into the factors contributing to each flagged transaction, enhancing interpretability and trust. Furthermore, a comprehensive risk trend dashboard developed using Flask and Chart.js enables visual analysis of detection patterns, common risk factors, and evolving fraud trends over time. By combining automation, adaptability, and interpretability, this system improves the accuracy of fraud detection, reduces false alerts, strengthens e-commerce security, and fosters greater confidence in online transactions.
Keywords Fraud Detection, Xgboost, SHAP, Online Shopping, Machine Learning
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 7, Issue 5, September-October 2025
Published On 2025-10-25
DOI https://doi.org/10.36948/ijfmr.2025.v07i05.58724
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E-ISSN 2582-2160
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