Machine Learning Meets Tax Fraud: Insights from Slovakia
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14560%2F25%3A00143624" target="_blank" >RIV/00216224:14560/25:00143624 - isvavai.cz</a>
Result on the web
<a href="https://journals.savba.sk/index.php/ekonomickycasopis/article/view/3902" target="_blank" >https://journals.savba.sk/index.php/ekonomickycasopis/article/view/3902</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.31577/ekoncas.2025.05-06.01" target="_blank" >10.31577/ekoncas.2025.05-06.01</a>
Alternative languages
Result language
angličtina
Original language name
Machine Learning Meets Tax Fraud: Insights from Slovakia
Original language description
One of the most intriguing topics in the field of corporate finance is the detection of tax fraud. We consider a unique dataset of outcomes from Slovak tax authority audits, obtaining valuable insights into verified instances of tax manipulation and avoiding the misclassification problem that is common in this stream of literature. We apply artificial neural networks, random forests, XGBoost, and support vector machines to verify the extent to which we can classify tax manipulators on the basis of publicly available financial statement indicators. Our results show that the XGBoost model demonstrated the highest effectiveness, achieving an F1 score of 0.75 in the full sample, slightly lower scores within the industry groups, and excellent results in sector A - Agriculture, with an F1 score of 0.85. Our results indicate that the use of nowadays commonly known machine learning methods along with standard financial variables can provide a useful tool for tax fraud detection and, as such, can contribute to higher efficiency of tax audits.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
50200 - Economics and Business
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
EKONOMICKY CASOPIS
ISSN
0013-3035
e-ISSN
0013-3035
Volume of the periodical
73
Issue of the periodical within the volume
5-6
Country of publishing house
SK - SLOVAKIA
Number of pages
29
Pages from-to
181-209
UT code for WoS article
001632546300001
EID of the result in the Scopus database
2-s2.0-105018923991