Research report: INSIGHT: a scalable cluster-based framework with theoretical guarantees for interpretable fraud detection in mixed-feature data

A new computational framework called INSIGHT improved fraud detection in two financial transaction datasets while providing cluster descriptions and feature-importance analyses. The study reports F1 scores of 0.82 on the European Credit Card dataset and 0.71 on BankSim. INSIGHT also produced the strongest reported results among the compared methods on several performance measures, while offering tools designed to make its classifications more transparent.

Developed as a supervised, cluster-based method, INSIGHT is intended for data that combines continuous and categorical features. Its central technical component is a computationally efficient mixed-distance metric, which allows the framework to compare records containing different types of information. The method also applies cluster-aware oversampling to address the extreme imbalance between fraudulent and non-fraudulent transactions while preserving the distribution structure of the data.

The evaluation used two financial datasets: European Credit Card, containing 284,807 transactions, and BankSim, containing 594,643 transactions. On European Credit Card, INSIGHT achieved the highest F1 score and area under the ROC curve among the compared approaches, including a tuned CatBoost model and several supervised base models. On BankSim, the framework achieved the highest area under the ROC curve and the highest precision among the compared methods.

The paper also reports that INSIGHT outperformed isolation forest and autoencoder-based anomaly detectors on both datasets. Alongside its predictive performance, the study provides theoretical guarantees concerning algorithmic convergence, cluster stability and scalable performance. Ablation studies assessed the individual contributions of the mixed-distance metric, cluster-aware oversampling and the choice of k-medoids variant.

Further experiments linked the framework’s theoretical properties with measured robustness and scalability. The study therefore combines formal analysis with empirical testing, although its evaluation is centered on these two financial datasets and the specified comparison methods. Interpretability is incorporated through cluster characterization and feature-importance analysis.

These outputs show how INSIGHT groups transactions and which variables contribute to its fraud classifications, allowing researchers and financial-security analysts to inspect the model’s behavior rather than rely only on an aggregate prediction score. The framework was published as a regular paper in the International Journal of Data Science and Analytics , Volume 22, as article 347, with a stated publication date of 20 October 2026.

Subject of Research: Scalable and interpretable cluster-based fraud detection in mixed-feature financial transaction data

Article Title: INSIGHT: a scalable cluster-based framework with theoretical guarantees for interpretable fraud detection in mixed-feature data

Article References: Yousefimehr, B., Ghatee, M., Seifi, M. A., & Fazli, J. (2026). INSIGHT: a scalable cluster-based framework with theoretical guarantees for interpretable fraud detection in mixed-feature data. International Journal of Data Science and Analytics, 22(1), Article 347. https://doi.org/10.1007/s41060-026-01313-4

Image Credits: AI Generated

DOI: 10.1007/s41060-026-01313-4

Keywords: fraud detection, financial analytics, mixed-feature data, cluster analysis, oversampling, machine learning, model interpretability, class imbalance, European Credit Card dataset, BankSim dataset, INSIGHT, scalable