Proceedings of International Conference on Applied Innovation in IT
2025/08/29, Volume 13, Issue 4, pp.323-331
Using Machine Learning Algorithms for Advanced Credit Card Fraud Detection
Fadya Abdulfattah Habeeb Abstract: Credit card fraud (CCF (is a serious problem that impacts a number of industries, including banking, e-commerce, insurance, finance, commercial entities, and private citizens. More sophisticated technologies are needed for effective detection as fraudulent activities become more complex. Machine learning (ML) approaches have demonstrated high performance in CCF identification. In this study, we explore six ML techniques: CatBoost, Hist Gradient Boosting, Decision Tree (DT), LightGBM (LGBM), ANN, and XGBoost (XGBC). To represent the data, identify its key characteristics, and compare their performance to determine the most successful fraud detection algorithm. Among these algorithms, the CatBoost algorithm, known for its high accuracy and superior performance, stands out. The proposed approach was based on the Kaggle dataset, and the results showed that CatBoost achieved the highest performance with a lower error rate compared to other models. This study also discusses the challenges related to implementing these advanced techniques, including the requirement for sizable, high-quality datasets and significant computational resources for model training and deployment. Key performance measures were used to evaluate the techniques' effectiveness, and CatBoost achieved a test accuracy of 0.9966, outperforming Hist Gradient Boosting, DT, LGBM, XGBC and ANN.
Keywords: Credit Card (CC), Fraud Detection, Predictive Modeling, Machine Learning(ML).
DOI: 10.25673/122135
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