Abstract
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 set of benchmarks for anomaly detection in payment transactions, particularly in the context of 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.