Elementos de ingenier\'ia de explotaci\'on de la informaci\'on aplicados a la investigaci\'on tributaria fiscal
Rodrigo Lopez-Pablos (Universidad Nacional de La Matanza y Universidad, Tecnol\'ogica Nacional)

TL;DR
This paper explores how data mining and AI techniques like neural and Bayesian networks can assist tax authorities in detecting tax evasion, considering their limited computational resources.
Contribution
It introduces the application of data mining and AI algorithms to tax analysis, demonstrating their feasibility for public tax administrators through an empirical case study.
Findings
Induction algorithms, neural networks, and Bayesian networks are effective in tax evasion detection.
Strategies tailored for local and regional tax inspectors improve heuristic application.
Computationally limited methods can still significantly aid tax research and enforcement.
Abstract
By introducing elements of information mining to tax analysis, by means of data mining software and advanced computational concepts of artificial intelligence, the problem of tax evader's crime against public property has been addressed. Through an empirical approach from a hypothetical case of use, induction algorithms, neural networks and bayesian networks are applied to determine the feasibility of its heuristic application by the tax public administrator. Different strategies are explored to facilitate the work of local and regional federal tax inspectors, considering their limited computational capabilities, but equally effective for those social scientist committed to handcrafting tax research. ----- Apresentando a introdu\c{c}\~ao de elementos de explora\c{c}\~ao de informa\c{c}\~oes para an\'alise fiscal, por meio de software de minera\c{c}\~ao de dados e conceitos…
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Taxonomy
TopicsBusiness, Innovation, and Economy · Taxation and Compliance Studies · Scientific Research and Technology
