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Depósito de la Universidad de Murcia
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Derechos y garantías de los obligados tributarios frente a la elaboración de perfiles de riesgo por la Administración Tributaria mediante herramientas de IA

Authors: Marcos Cardona, Marta;

Derechos y garantías de los obligados tributarios frente a la elaboración de perfiles de riesgo por la Administración Tributaria mediante herramientas de IA

Abstract

Nuestra Administración tributaria, como la mayoría de las administraciones de los países de nuestro entorno, ha incorporado herramientas de big data para mejorar la eficacia en la lucha contra el fraude fiscal. Estas técnicas de análisis masivo de datos proporcionan la capacidad de procesar y cruzar grandes volúmenes de información procedente de distintas fuentes, tanto internas como externas. La introducción de herramientas basadas en inteligencia artificial (IA) y machine learning1 supone un paso más en esta evolución tecnológica. Entre sus aplicaciones más relevantes se encuentra el desarrollo de modelos predictivos que identifican patrones de comportamiento asociados a perfiles de riesgo fiscal, que permiten clasificar e identificar a contribuyentes en función de la probabilidad estadística de incumplimiento de sus obligaciones.

Country
Spain
Related Organizations
Keywords

Tributos, Perfiles de riesgo, Discriminación, Inspección, Objetivo 16: Paz y justicia, Algoritimos, Sesgos, Compliance

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green