FraudX SimS: A Synthetic Dataset for Anomaly Detection in Payment-Card Transactions
Baisholan N. Eric Dietz J. Gnatyuk S. Turdalyuly M. Matson E.T. Baisholanova K.
2025Institute of Electrical and Electronics Engineers Inc.
IEEE Access
2025#13208549 - 208562 pp.
Progress in detecting payment fraud is challenged by the limited variety of publicly available datasets. Relying on one or two datasets makes it hard to compare fairly, disguises sensitivity to data changes, and limits the ability to evaluate explainable methods in depth. This article introduces FraudX SimS, a scenario-labeled synthetic dataset designed to expand the benchmark set for anomaly detection in payment transactions, particularly for fraud detection. The dataset preserves the class imbalance between legitimate and fraudulent activity and includes openly specified spatial, temporal, and behavioral features, allowing direct application of explainable artificial intelligence techniques. We establish baselines with standard machine learning models and report accuracy, precision, recall, F1-score, confusion-matrix results, and the area under the receiver operating characteristic curve (AUC-ROC) and the area under the precision–recall curve (AUC-PR), with a primary emphasis on recall given the cost of missed fraud. We further employ Shapley additive explanations to quantify feature contributions, enabling transparent error analysis and model refinement. Although synthetic, the dataset is constructed to support reproducible experimentation and cross-study comparisons, advancing the development of reliable and interpretable fraud-detection methods.
Anomaly detection , credit card fraud , explainable artificial intelligence , fraud detection , fraud scenarios , fraud simulation , imbalanced datasets , machine learning , SHAP analysis , synthetic data generation , synthetic datasets
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Al-Farabi Kazakh National University, Faculty of Information Technology and Artificial Intelligence, Almaty, 050040, Kazakhstan
International Engineering and Technological University, Software Engineering Department, Almaty, 050060, Kazakhstan
Purdue University, School of Applied and Creative Computing, West Lafayette, 47907, IN, United States
Faculty of Computer Science and Technology, State University, “Kyiv Aviation Institute”, Kyiv, 03058, Ukraine
State Scientific and Research Institute of Cybersecurity Technologies and Information Protection, Kyiv, 03142, Ukraine
Eurasian Technological University, School of Engineering and Information Technologies, Almaty, 050012, Kazakhstan
Al-Farabi Kazakh National University
International Engineering and Technological University
Purdue University
Faculty of Computer Science and Technology
State Scientific and Research Institute of Cybersecurity Technologies and Information Protection
Eurasian Technological University
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