Jul 2026· International Conference Computing Methodologies and Communication· pp. 2008-2016· 0 citations· 26 references
Abstract
Credit card fraud detection is widely recognized as a critical issue in digital financial systems due to the rapid growth of online transactions. However, existing approaches often fail to provide a comprehensive comparative framework and show limited effectiveness under highly imbalanced conditions. In this context, this study proposes a systematic machine learning pipeline to analyze fraudulent transactions using the IEEE-CIS dataset (590,000 records, 3–4% fraud). The approach integrates data preprocessing, feature encoding, and stratified sampling, followed by the evaluation of multiple supervised models including Logistic Regression, Decision Tree, Random Forest, SVM, Naive Bayes, and XGBoost. Performance is assessed using Accuracy, Precision, Recall, F1-score, and ROC-AUC. The results indicate that ensemble-based methods outperform traditional classifiers. In particular, XGBoost achieves the highest performance (accuracy 98.22%, ROC-AUC 0.9545, F1-score 0.6759), suggesting a strong ability to handle class imbalance. Nevertheless, no model fully optimizes both precision and recall, highlighting a key limitation in current fraud detection systems. Taken together, these findings suggest that ensemble learning provides a robust baseline, while further improvements through hybrid and cost-sensitive approaches are required for effective real-world deployment.
Credit card fraud remains a major challenge for financial institutions, both financially and operationally, as digital transactions continue to grow and fraud datasets remain highly imbalanced. This study compares the performance of several supervised machine learning models for fraud detection, using a unified data preprocessing pipeline. The approach includes removing duplicates, applying RobustScaler normalization, engineering features and using the Synthetic Minority Oversampling Technique (SMOTE) to balance classes before training. Four models were developed and tested Logistic Regression, Decision Tree, Random Forest and Artificial Neural Network (ANN) using the publicly available Kaggle Credit Card Fraud Detection dataset. Their performance was measured with Accuracy, Precision, Recall, F1-score and ROC-AUC metrics. Results showed that thorough preprocessing combined with SMOTE significantly improved the models ability to detect fraudulent transactions. Among them, the Random Forest model delivered the strongest overall performance, proving especially effective at handling highly imbalanced financial data. The comparative analysis also highlighted that ensemble learning methods generally outperform single classifiers in both accuracy and minority-class recognition. These findings emphasize the importance of pairing robust preprocessing strategies with machine learning techniques to boost fraud detection in real-world financial systems. The proposed system offers institutions a scalable and practical solution for building intelligent fraud detection systems, while laying the groundwork for future integration of Explainable AI (XAI) and real-time detection tools.
Nafiu Yahuza, Ahmad Baita Garko, Abubakar Atiku Muslim et al.· Lead Sci Journal of Manageme...· 0 citations
Now the days world become digital, credit card customers have become common; it makes the payment hassle-free. With the ease of use of credit cards; Fraudulent use of credit cards is growing as a significant affair for financial institutions and consumers on an international level. Traditional rule-based detection algorithms are ineffective in determining a transaction's fraudulent nature. First and foremost, it is imperative to comprehend the pattern of fraudulent activities. The current study explores various supervised machine-learning algorithms to analyze patterns and predict the fraudulent nature of transactions in a large dataset used for training the model. The effectiveness of different methods is assessed by comparing their accuracy, precision, F1score, and recall. In the present paper, we discuss the techniques named KNN, SVM(Support Vector Machine), Logistic Regression, Gradient Boosting, Neural Network, XG Boost, Naïve Bayes, Ada Boost, Decision Forest, and Random Forest.
S. Bansal, Reena Hooda, Rohit Yadav· Journal of Commerce, Economi...· 0 citations
This study presents a comparative evaluation of multiple machine learning models for credit card fraud detection, including Logistic Regression, Support Vector Machine (SVM), Random Forest, XGBoost, Multi-Layer Perceptron (MLP) Neural Network, and a Neural Network built using TensorFlow/Keras. Each model underwent a comprehensive methodology involving data preprocessing, feature scaling, and undersampling to address class imbalance. Model performance was evaluated using a variety of metrics accuracy, precision, recall, F1 score, and AUC-ROC to capture their effectiveness in distinguishing fraudulent transactions. Results indicated that XGBoost achieved the highest accuracy at 96.6% with a perfect precision score, while MLP and TensorFlow/Keras neural networks demonstrated excellent AUC scores (0.982 and 0.977, respectively), reflecting strong discrimination between classes. The SVM, Logistic Regression, and Random Forest models also showed competitive performance, with high precision and recall values. This analysis highlights the effectiveness of ensemble and neural network models for fraud detection in imbalanced datasets and emphasizes the importance of metric selection based on application-specific priorities, particularly for balancing false positive and false negative outcomes.
The study shows that machine learning can be useful for fraud detection when it is combined with suitable preprocessing, class-imbalance techniques, and careful evaluation.
Jabulani Khumalo, Min Joon Kim· Global Knowledge Academy· 0 citations
Experimental results demonstrate that TabNet outperforms traditional neural networks and popular machine learning algorithms by achieving high fraud detection accuracy with significantly reduced false alarms while maintaining excellent interpretability suitable for financial regulatory requirements.
G Srividhya, Dr. S Siva Sankara Rao· International Journal of Eng...· 0 citations
Credit card fraud detection is complicated by the severe class imbalance typical of transaction data, because fraudulent cases represent only a small proportion of observations. This study develops a neural-network-based model for classifying transactions as legitimate or fraudulent and compares combinations of two oversampling techniques and two feature-selection approaches. The dataset contains 20,000 observations and 26 variables, with 339 fraudulent transactions (1.7%). After removing the transaction identifier and transforming categorical variables, class imbalance was addressed using the Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN). Relevant features were then selected using Pearson’s correlation coefficient or XGBoost, producing four configurations: SMOTE + Pearson, SMOTE + XGBoost, ADASYN + Pearson, and ADASYN + XGBoost. The models were evaluated using accuracy, precision, recall, specificity, F1 score, loss, mean squared error, training and validation curves, and receiver operating characteristic analysis. Although the ADASYN + Pearson configuration produced high nominal accuracy and recall, its zero specificity and lower F1 score indicated poor identification of legitimate transactions. In contrast, the ADASYN + XGBoost configuration showed a more balanced performance across the evaluation criteria, with accuracy, precision, recall, specificity, and F1 score each reported at 79%. These results support ADASYN + XGBoost as the best-performing configuration among the four models evaluated in this study.
Kalala Kanyinda Norbert, Mukala Patrick, Kafunda Katalayi Pierre· Asian Journal of Research in...· 0 citations
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