Credit card fraud detection is a critical research area due to the significant financial losses and security risks associated with fraudulent activities. This study presents FraudX AI, an ensemble-based framework addressing the challenges in fraud detection, including imbalanced datasets, interpretability, and scalability. FraudX AI combines random forest and XGBoost as baseline models, integrating their results by averaging probabilities and optimizing thresholds to improve detection performance. The framework was evaluated on the European credit card dataset, maintaining its natural imbalance to reflect real-world conditions. FraudX AI achieved a recall value of 95% and an AUC-PR of 97%, effectively detecting rare fraudulent transactions and minimizing false positives. SHAP (Shapley additive explanations) was applied to interpret model predictions, providing insights into the importance of features in driving decisions. This interpretability enhances usability by offering helpful information to domain experts. Comparative evaluations of eight baseline models, including logistic regression and gradient boosting, as well as existing studies, showed that FraudX AI consistently outperformed these approaches on key metrics. By addressing technical and practical challenges, FraudX AI advances fraud detection systems with its robust performance on imbalanced datasets and its focus on interpretability, offering a scalable and trusted solution for real-world financial applications.

Credit card fraud remains a significant concern for financial institutions due to its low prevalence, evolving tactics, and the operational demand for timely, accurate detection. Machine learning (ML) has emerged as a core approach, capable of processing large-scale transactional data and adapting to new fraud patterns. However, much of the literature modifies the natural class distribution through resampling, potentially inflating reported performance and limiting real-world applicability. This systematic literature review examines only studies that preserve the original class imbalance during both training and evaluation. Following PRISMA 2020 guidelines, strict inclusion and exclusion criteria were applied to ensure methodological rigor and relevance. Four research questions guided the analysis, focusing on dataset usage, ML algorithm adoption, evaluation metric selection, and the integration of explainable artificial intelligence (XAI). The synthesis reveals dominant reliance on a small set of benchmark datasets, a preference for tree-based ensemble methods, limited use of AUC-PR despite its suitability for skewed data, and rare implementation of operational explainability, most notably through SHAP. The findings highlight the need for semantics-preserving benchmarks, cost-aware evaluation frameworks, and analyst-oriented interpretability tools, offering a research agenda to improve reproducibility and enable effective, transparent fraud detection under real-world imbalance conditions.

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.

The financial market belongs to the segment of the economic system, which is especially strictly regulated and controlled by state bodies. The special emphasis of the state on the issues of control and supervision of the financial market is due to the fact that the degree of development of its infrastructure has direct impact on the economic situation of the country. The conducted analysis of the financial market development covers the period from 2014 through 2017. The article reflects the features of the financial market of Kazakhstan and its role for the national economy, as well as the mechanism of state regulation of the financial market. The authors reveal the problems of the financial market functioning at the present stage, and draw conclusion about the need to develop the regulatory infrastructure of the financial market to achieve sustainable economic growth.

Gamification elements are used in various fields. Game technologies can also be used in the educational process. How to make lessons interesting with the help of gaming technologies? The article provides an answer to this question. A geometry game was developed and the game mechanics were analyzed. Several solutions have been proposed to combat the destructive strategy. The main constructs of gamification are shown, such as intrinsic and extrinsic motivation, attitude, self-efficacy. On the basis of the developed game, experiments were carried out with students. On the basis of the experiment, the effectiveness of using gamification for the formation of sustainable internal motivation of students to systematically solve problems is shown. Examples are given that illustrate the content aspect of the presented model - fragments of author's developments. The application of the developed game as a material for repeating the topic for the lesson of analytical geometry for traditional education is considered.Based on the results of the study, conclusions were drawn about the benefits and effectiveness of gamification in the classroom. Prospects for the development of the research topic and directions for further activities are outlined. Scientific work can be useful for programmers developing e-Iearning tools.