Abstract
In this paper we propose a methodology that combines Bayesian and “big data” tools designed to optimize the investigation of fraud that we call “Bayesian dialysis”. Its use improves the results of any statistical selection technique previously used, capitalizing on the evidence obtained by decision makers. Its effectiveness is shown using as an exemplary case the selection for VAT control in the “Agencia Estatal de Administración Tributaria” (AEAT), that is the State Tax Agency. Although in Spain the existing systems for the selection of taxpayers in the control of fraud in VAT, in a context in which the impact of the fraud is estimated at 2%2, get excellent results, the proposed methodology, which has the characteristics of novelty and generality, improves accuracy in the detection of fraudsters in a 12.29%, from an average of 82.28% fraudsters detected in the collectives inspected to 94.36%. In addition, the confidence interval in the detection of fraud, at the 0.95 level, improves by 13.72%, which represents a qualitative leap in the effectiveness of the control reaching the threshold of 95.51%
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
This research has used data from the AEAT, but since it is an academic research, the data has not been reviewed by the AEAT and the research conclusions are the full responsibility of the authors.
References
CASE: study and reports on the VAT gap in the EU-28 member states: 2018 final report TAXUD/2015/CC/131 (2018)
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
Bolton, R., Hand, D.: Statistical fraud detection: a review. Stat. Sci. 17, 235–255 (2002)
Baesen, B., Van Vlasselaer, V., Verbecke, W.: Fraud Analytics Using Predictive, and Social Network Techniques. A Guide to Data Science for Fraud Detection. Wiley, Hoboken (2015)
Castellón, P., Velásquez, J.: Characterization and detection of taxpayers with false invoices using data mining techniques (2013). https://repositorio.uchile.cl/handle/2250/12637
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.: Control of the collaborative economy. IOTA Papers, December 2017
Gonzalez, 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)
Breyfogle III, F.W.: Implementing Six Sigma: Smarter Solutions Using Statistical Methods. Wiley, Hoboken (1999)
Acknowledgements
This project has been supported by the Ministry of Economy and Competitivity. Project MTM2017-86875-C3-3-R.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
García, I.G., Caballero, A.M. (2021). Bayesian Dialysis of the Evidence in Fraud Detection. In: Bucciarelli, E., Chen, SH., Corchado, J.M., Parra D., J. (eds) Decision Economics: Minds, Machines, and their Society. DECON 2020. Studies in Computational Intelligence, vol 990. Springer, Cham. https://doi.org/10.1007/978-3-030-75583-6_17
Download citation
DOI: https://doi.org/10.1007/978-3-030-75583-6_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-75582-9
Online ISBN: 978-3-030-75583-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)