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Title |
Enhancing E-commerce Security: A Comprehensive Approach to Real-Time Fraud Detection |
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Author |
Sara Alqethami, Badriah Almutanni, Walla Aleidarousr |
| Citation |
Vol. 24 No. 4 pp. 1-10 |
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Abstract |
In the era of big data, the growth of e-commerce transactions brings forth both opportunities and risks, including the threat of data theft and fraud. To address these challenges, an automated real-time fraud detection system leveraging machine learning was developed. Four algorithms (Decision Tree, Na?ve Bayes, XGBoost, and Neural Network) underwent comparison using a dataset from a clothing website that encompassed both legitimate and fraudulent transactions. The dataset exhibited an imbalance, with 9.3% representing fraud and 90.07% legitimate transactions. Performance evaluation metrics, including Recall, Precision, F1 Score, and AUC ROC, were employed to assess the effectiveness of each algorithm. XGBoost emerged as the top-performing model, achieving an impressive accuracy score of 95.85%. The proposed system proves to be a robust defense mechanism against fraudulent activities in e-commerce, thereby enhancing security and instilling trust in online transactions. |
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Keywords |
Fraud detection, E-commerce, Real-time transactions XGBoost, Na?ve Bayes; Neural Network |
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