Abstract
In this paper, we propose a methodology combining Bayesian and big data tools designed to optimize the investigation of fraud. This methodology is called Bayesian dialysis. We address three issues: a) Is it possible to capitalize on the evidence provided by data indicating fraud without a parametric model and using an interpretable approach? b) If so, would it be the best solution in any case? c) What is the effect size of all unobservable, even unknown, variables? We prove the viability of a new method using as an exemplary case the selection for VAT control in the Spanish Tax Agency (Agencia Estatal de Administración Tributaria—AEAT). The new method improves fraudster detection precision by 12,29%, which is increased from an average of 82.28% to 94.36%. We also use 2018–2019 corporate tax data to test the scope of this approach. Finally, based on the concept of tetrads, we propose a method to quantify the effect of unknown latent variables on models analysis.
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Onwubiko, C.: Fraud matrix: a morphological and analysis-based classification and taxonomy of fraud. Comput. Secur. 96, 101900 (2020)
CASE: Study and Reports on the VAT Gap in the EU-28 Member States: 2018 Final Report TAXUD/2015/CC/131 (2018). https://ec.europa.eu/taxation_customs/sites/taxation/files/2018_vat_gap_report_en.pdf
REAF- REGAF Asesores fiscales Consejo de Economistas: Reflexiones sobre el fraude fiscal y el problema de las estimaciones:20 propuestas para reducirlo (2017). https://www.reaf-regaf.economistas.es
Stankevicius, E., Leonas, L.: Hybrid approach model for prevention of tax evasion and fraud. Procedia – Soc. Behav. Sci. 213, 383–389 (2015)
Baesen, B., Van Vlasselaer, V., Verbecke, W.: Fraud Analytics Using Predictive, and Social Network Techniques. A Guide to Data Science for Fraud Detection. Wiley (2015)
Matos, T., Macedo, J., Lettich, F., Monteiro, J., Renso, C., Perego, R., Nardini, F.: Leveraging feature selection to detect potential tax fraudsters. Expert Syst. Appl. 145, 113128 (2020)
González, P., Velásquez, J.: Characterization and detection of taxpayers with false invoices using data mining techniques. Expert Syst. Appl. 40(5), 1427–1436 (2013)
Martikainen, J.: Data mining in tax administration- using analytics to enhance tax compliance. Aalto University School Business (2012). https://epub.lib.aalto.fi/en/ethesis/id/13054
González, I.: Analytics and big data: the case of AEAT. Tax Administration Review, no. 44, October 2018
González, I., Mateos, A.: Social network analysis tools in the fight against fiscal fraud and money laundering. In: Proceedings of the 15TH International Conference on Modelling Decisions for Artificial Intelligence (MDAI 2018) (2018)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kauffman, San Francisco (1988)
Glymour, G., Schines, R., Spirtes, R., Kelly, K.: Discovering Causal Structure: Artificial Intelligence, Philosophy of Science, and Statistical Modelling. Academic Press, Orlando (1987)
Fratello, M., Tagliaferri, R.: Decision trees and random forests. In: Ranganathan, S., Gribskov, M., Nakai, K., Schönbach, C. (eds.) Encyclopedia of Bioinformatics and Computational Biology. Academic Press, Orlando (2019)
Spearman, C.: General intelligence determined and measured. Am. J. Psychol. 15(201), 93 (1904)
Bollen, K.A., Ting, K.-F.: A tetrad test for causal indicators. Psychol. Methods 5(1), 3–22 (2000)
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This project has been supported by the Ministry of Economy and Competitiveness. Project MTM2017–86875-C3–3-R.
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González García, I., Mateos, A. (2021). Using Bayesian Dialysis and Tetrads to Detect the Persistent Characteristics of Fraud. In: Rocha, Á., Adeli, H., Dzemyda, G., Moreira, F., Ramalho Correia, A.M. (eds) Trends and Applications in Information Systems and Technologies. WorldCIST 2021. Advances in Intelligent Systems and Computing, vol 1365. Springer, Cham. https://doi.org/10.1007/978-3-030-72657-7_13
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DOI: https://doi.org/10.1007/978-3-030-72657-7_13
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