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Inteligencia artificial, compliance risk management y conflictividad tributaria

Authors: Martín López, Jorge; Pérez Bernabeu, Begoña;

Inteligencia artificial, compliance risk management y conflictividad tributaria

Abstract

La presente comunicación se basa en la memoria científico-técnica del Proyecto "Data Science y Machine Learning en el ámbito tributario", elaborada por Jorge Martín López y Begoña Pérez Bernabeu en calidad de investigadores principales y presentada en octubre de 2019, en el marco de la convocatoria de Proyectos de I+D+i del Ministerio de Ciencia, Innovación y Universidades, correspondiente al año 2019 (modalidad "Retos Investigación"), habiéndose notificado en septiembre de 2020 la oportuna resolución definitiva de concesión (PID2019-108963RB-I00), Además, ambos son también miembros investigadores del Grupo de Investigación que ha recibido las Ayudas para Grupos de Investigación de Excelencia del Programa PROMETEO 2020, "El Derecho Financiero y Tributario ante el reto de la innovación Científica y Tecnológica", (Resolución de concesión de 8 de julio de 2020, Expediente PROMETEO/2020/092), financiado por la Conselleria de Educación, Investigación, Cultura y Deporte de la Generalitat Valenciana y cuya investigadora principal es Amparo Navarro Faure.

Country
Spain
Related Organizations
Keywords

Compliance risk management, Conflictividad tributaria, Selección de contribuyentes, Inteligencia artificial

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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
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