Jul 2026· IPTEK: The Journal for Technology and Science· Vol 34, pp. 175-185· 0 citations
TL;DR
The combined oversampling of GAN and SMOTE with the CNN classifier model produces the highest evaluation score, and shows that augmented data quality does affect prediction performance, and Ensemble Oversampling technique could be considered to improve classifier performance in financial fraud data.
Abstract
Financial fraud classification cases such as credit card fraud and bitcoin fraud have highly imbalanced data problems that the oversampling data of fraud class is necessary. Financial transactions could have different attributes. In a credit card transaction, the attributes could represent a nominal amount, transaction period infor- mation, the status of deposits or other types like withdrawals or refunds, and more detailed information. In the financial transaction of bitcoin, the attributes could rep- resent the number of nodes, transaction fee, output volume, and aggregated figures. The various characteristics of attributes in financial fraud data require an adaptable oversampling method so that the classification model can perform well. An Ensemble Oversampling method is proposed as a general context approach to handling finan- cial fraud classification in credit cards and bitcoin. The proposed method combines generative with traditional approaches such as GAN, SMOTE, and ADASYN. In the classification step, Deep Learning algorithms such as CNN and LSTM are applied to provide better performance. The genetic algorithm is used to optimize Deep Learn- ing hyperparameters. The evaluation was carried out by comparing four scenarios, i.e., without oversampling, using oversampling with GAN, SMOTE, ADASYN, orig- inal data, and Ensemble Oversampling. The combined oversampling of GAN and SMOTE with the CNN classifier model produces the highest evaluation score of all scenarios with an average F1-Score value of 0.995 and Kappa Statistics of 0.990. It shows that augmented data quality does affect prediction performance, and Ensem- ble Oversampling technique could be considered to improve classifier performance in financial fraud data.
These findings validate that deep learning techniques can be used to detect fraudulent credit card transactions and deployed in real time systems of fraud detection.
Deepika Tiwari, Meenakshi Nawal, N. Neeraj et al.· Journal of Dynamics and Cont...· 1 citation
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
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
Financial transaction fraud is an ongoing threat with significant economic loss and on customers' trust. This paper discusses Fraud Detection in detail with machine learning technique on a given data set of a transaction. We investigate the patterns revealed from the users and the transactions in the database when the user is performing fraudulent transactions, and test several classification models that can be used to detect fraud, which includes logistic regression model, random forests, support vector machines, gradient boosting, and neural networks. The performance of the models is investigated in terms of accuracy, precision, recall, F1 score and ROC-AUC metrics. Based on our experiments, the best detection overall performances are obtained for the tree-based ensemble models (Random Forest and XGBoost) with XGBoost getting the most optimum fraud Recall and F1-Score. Through the data analysis results (such as account age, transaction frequency etc.) and the model comparison, we expound an improved method which is based on combining the ensemble of best models with data imbalance countermeasures to increase the recall of fraudulent cases. We also have an end-to-end machine learning pipeline on Python to detect frauds from preprocessing the data, training the models, evaluating them, and deploying for fraud prediction. Also, a literature review of twenty-five recent studies on fraud detection is given, and the algorithms used, datasets and major contributions of these studies were summarized. The textbook ensemble technique, as proposed gives better fraud detection performance as it gains on the order of ~3-5% improvement against the best single model performance on F1-score, with acceptable precision, thereby underscoring the usefulness of hybrid modeling with specialized techniques for this field. The results emphasize that utilizing various models and domain-specific feature engineering can be of great benefit in fraudulent transaction detection, while also neg
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
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
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
