Fraud Detection of Credit Cards Using Supervised Machine Learning
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Abstract
Credit card fraud encompasses illicit activities aimed at unlawfully obtaining confidential information to enable unauthorized individuals to engage in illegal transactions. As technology advances, fraudsters have honed their skills in evading security measures, presenting a formidable challenge in fraud detection. To address this issue, an array of algorithms and analytical techniques has emerged to identify and mitigate instances of fraud. This research aimedto identify the most appropriate supervised machine learning algorithm for credit card fraud detection. Logistic Regression, Random Forest, Support Vector Machine, and Decision Trees were implemented and compared. Due to the imbalanced nature of the dataset, the SMOTE (Synthetic Minority Oversampling Technique) technique was employed to rectify the data imbalance by oversampling the minority class. The performance of the trained models was evaluated using various metrics, including the confusion matrix, accuracy, precision, recall, f1-score, Matthews Correlation Coefficient (MCC), and Area Under the Curve (AUC). The results of the analysis revealed that Random Forests exhibited exceptional performance, achieving an impressive recall score of 84% and surpassing other algorithms. This researchprovides the groundwork for future investigations involving diverse deep-learning techniques applied to real-time and dynamic datasets, enabling continuous enhancements in fraud detection and prevention mechanisms.
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Pakistan Journal Emerging Science and Technologies (PJEST) in collaboration with Govt. Islamia Graduate College Civil Lines Lahore, Pakistan is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
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