Conventional Machine Learning Methods for Detecting Tax Evasion among Inactive or Insolvent Companies
By: Alexandru SIMION and Adrian COSTEA
References
- Abedin, M. Z., Chi, G., Uddin, M. M., Satu, Md. S., Khan, Md. I., & Hajek, P. (2021). Tax Default Prediction Using Feature Transformation-Based Machine Learning. IEEE Access, 9, 19864–19881. https://doi.org/10.1109/ACCESS.2020.3048018
- Barbu, D.-C., Bâra, A., & Oprea, S.-V. (2024). Impact of Electronic Cash Registers on Tax Collection. In Proceedings of 22nd International Conference on Informatics in Economy (IE 2023) (pp. 25–37). Springer. https://doi.org/10.1007/978-981-99-6529-8
- Belkin, M., Hsu, D., Ma, S., & Mandal, S. (2019). Reconciling modern machine-learning practice and the classical bias–variance trade-off. Proceedings of the National Academy of Sciences, 116(32), 15849–15854. https://doi.org/10.1073/pnas.1903070116
- Carvalho, M., Pinho, A. J., & Brás, S. (2025). Resampling approaches to handle class imbalance: a review from a data perspective. Journal of Big Data, 12(1). https://doi.org/10.1186/s40537-025-01119-4
- Charizanos, G., Demirhan, H. and İçen, D. (2025) “Binary classification with Fuzzy-Bayesian logistic regression using Gaussian fuzzy numbers,” Intelligent Systems with Applications, 26. Available at: https://doi.org/10.1016/j.iswa.2025.200494
- Coita, I. F., & Mare, C. (2021). The Utility of Neural Model in Predicting Tax Avoidance Behavior. In Smart Innovation, Systems and Technologies (Vol. 238, pp. 71–81). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-16-2765-1_6
- Costea, A. and Eklund, T. (2003) “A two-level approach to making class predictions,” in 36th Annual Hawaii International Conference on System Sciences, 2003. Proceedings of the. IEEE, p. 9 pp. Available at: https://doi.org/10.1109/HICSS.2003.1174207
- Demirhan, H. (2025) “Mixed fuzzy C-means clustering,” Information Sciences, 690, p. 121528. Available at: https://doi.org/10.1016/j.ins.2024.121528
- Höglund, H. (2017). Tax payment default prediction using genetic algorithm-based variable selection. Expert Systems with Applications, 88, 368–375. https://doi.org/10.1016/j.eswa.2017.07.027
- Junqué De Fortuny, E., Stankova, M., Moeyersoms, J., Minnaert, B., Provost, F., & Martens, D. (2014). Corporate residence fraud detection. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1650–1659. https://doi.org/10.1145/2623330.2623333
- Palacio-Niño, J.-O., & Berzal, F. (2019). Evaluation Metrics for Unsupervised Learning Algorithms. arXiv. https://doi.org/10.48550/arXiv.1905.05667
- Pop, I. D., & Coroiu, A. M. (2022). Predicting bankruptcy in Romania using artificial neural network. International Journal of Modern Manufacturing Technologies, 14(3), 211–218. https://doi.org/10.54684/ijmmt.2022.14.3.211
- Kleinberg, J. (2019). An Impossibility Theorem for Clustering. Neural Information Processing Systems: Proceedings of the 16th International Conference on Neural Information Processing Systems. https://dl.acm.org/doi/10.5555/2968618.2968676
- Surugiu, M.-R., Vasile, V., Surugiu, C., Mazilescu, C. R., Panait, M.-C., & Bunduchi, E. (2025). Tax Compliance Pattern Analysis: A Survey-Based Approach. International Journal of Financial Studies, 13(1), 14. https://doi.org/10.3390/ijfs13010014
DOI: https://doi.org/10.2478/picbe-2026-0053 | Journal eISSN: 2558-9652
Language: English
Page range: 661 - 673
Published on: Jul 16, 2026
Published by: Bucharest University of Economic Studies
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
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© 2026 Alexandru SIMION, Adrian COSTEA, published by Bucharest University of Economic Studies
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.