Indicators for Smart Cities: Tax Illicit Analysis Through Data Mining

Jesús Silva; Darwin Solano; Claudia Fernández; Lainet Nieto Ramos; Rosella Urdanegui; Jeannette Herz; Alberto Mercado; David Ovallos-Gazabon

doi:10.1007/978-981-15-7234-0_88

Indicators for Smart Cities: Tax Illicit Analysis Through Data Mining

Jesús Silva
, Darwin Solano
, Claudia Fernández
, Lainet Nieto Ramos
, Rosella Urdanegui
, Jeannette Herz
, Alberto Mercado
, David Ovallos-Gazabon

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

1 Cita (Scopus)

Resumen

The anomalies in the data coexist in the databases and in the non-traditional data that can be accessed and produced by a tax administration, whether these data are of internal or external origin. The analysis of certain anomalies in the data could lead to the discovery of patterns that respond to different causes, being able to evidence these causes certain illicit by taxpayers or acts of corruption when there is the connivance of the taxpayer with the public employee or public official. The purpose of this research is the theoretical development of the causal analysis of certain anomalies of tax data, demonstrating that the data mining methodology contributes to evidence of illicit and corrupt acts, through the application of certain algorithms.

Idioma original	Inglés
Título de la publicación alojada	Proceedings of International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications, ICMISC 2020
Editores	Vinit Kumar Gunjan, Jacek M. Zurada
Editorial	Springer Science and Business Media Deutschland GmbH
Páginas	929-937
Número de páginas	9
ISBN (versión impresa)	9789811572333
DOI	https://doi.org/10.1007/978-981-15-7234-0_88
Estado	Publicada - 2021
Publicado de forma externa	Sí
Evento	International Conference on Recent Trends in Machine Learning, IOT, Smart Cities and Applications, ICMISC 2020 - Hyderabad, India Duración: 29 mar. 2020 → 30 mar. 2020

Serie de la publicación

Nombre	Advances in Intelligent Systems and Computing
Volumen	1245
ISSN (versión impresa)	2194-5357
ISSN (versión digital)	2194-5365

Conferencia

Conferencia	International Conference on Recent Trends in Machine Learning, IOT, Smart Cities and Applications, ICMISC 2020
País/Territorio	India
Ciudad	Hyderabad
Período	29/03/20 → 30/03/20

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

ODS 11: Ciudades y comunidades sostenibles
ODS 16: Paz, justicia e instituciones sólidas

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10.1007/978-981-15-7234-0_88

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Silva, J., Solano, D., Fernández, C., Ramos, L. N., Urdanegui, R., Herz, J., Mercado, A., & Ovallos-Gazabon, D. (2021). Indicators for Smart Cities: Tax Illicit Analysis Through Data Mining. En V. K. Gunjan, & J. M. Zurada (Eds.), Proceedings of International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications, ICMISC 2020 (pp. 929-937). (Advances in Intelligent Systems and Computing; Vol. 1245). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-15-7234-0_88

Silva, Jesús ; Solano, Darwin ; Fernández, Claudia et al. / Indicators for Smart Cities : Tax Illicit Analysis Through Data Mining. Proceedings of International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications, ICMISC 2020. editor / Vinit Kumar Gunjan ; Jacek M. Zurada. Springer Science and Business Media Deutschland GmbH, 2021. pp. 929-937 (Advances in Intelligent Systems and Computing).

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title = "Indicators for Smart Cities: Tax Illicit Analysis Through Data Mining",

abstract = "The anomalies in the data coexist in the databases and in the non-traditional data that can be accessed and produced by a tax administration, whether these data are of internal or external origin. The analysis of certain anomalies in the data could lead to the discovery of patterns that respond to different causes, being able to evidence these causes certain illicit by taxpayers or acts of corruption when there is the connivance of the taxpayer with the public employee or public official. The purpose of this research is the theoretical development of the causal analysis of certain anomalies of tax data, demonstrating that the data mining methodology contributes to evidence of illicit and corrupt acts, through the application of certain algorithms.",

keywords = "Algorithms, Anomalous data, Automatic learning, Big data, Data mining, Noise",

author = "Jes{\'u}s Silva and Darwin Solano and Claudia Fern{\'a}ndez and Ramos, \{Lainet Nieto\} and Rosella Urdanegui and Jeannette Herz and Alberto Mercado and David Ovallos-Gazabon",

note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Singapore Pte Ltd.; International Conference on Recent Trends in Machine Learning, IOT, Smart Cities and Applications, ICMISC 2020 ; Conference date: 29-03-2020 Through 30-03-2020",

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doi = "10.1007/978-981-15-7234-0\_88",

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Silva, J, Solano, D, Fernández, C, Ramos, LN, Urdanegui, R, Herz, J, Mercado, A & Ovallos-Gazabon, D 2021, Indicators for Smart Cities: Tax Illicit Analysis Through Data Mining. En VK Gunjan & JM Zurada (eds.), Proceedings of International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications, ICMISC 2020. Advances in Intelligent Systems and Computing, vol. 1245, Springer Science and Business Media Deutschland GmbH, pp. 929-937, International Conference on Recent Trends in Machine Learning, IOT, Smart Cities and Applications, ICMISC 2020, Hyderabad, India, 29/03/20. https://doi.org/10.1007/978-981-15-7234-0_88

Indicators for Smart Cities: Tax Illicit Analysis Through Data Mining. / Silva, Jesús; Solano, Darwin; Fernández, Claudia et al.
Proceedings of International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications, ICMISC 2020. ed. / Vinit Kumar Gunjan; Jacek M. Zurada. Springer Science and Business Media Deutschland GmbH, 2021. p. 929-937 (Advances in Intelligent Systems and Computing; Vol. 1245).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

TY - GEN

T1 - Indicators for Smart Cities

T2 - International Conference on Recent Trends in Machine Learning, IOT, Smart Cities and Applications, ICMISC 2020

AU - Silva, Jesús

AU - Solano, Darwin

AU - Fernández, Claudia

AU - Ramos, Lainet Nieto

AU - Urdanegui, Rosella

AU - Herz, Jeannette

AU - Mercado, Alberto

AU - Ovallos-Gazabon, David

PY - 2021

Y1 - 2021

N2 - The anomalies in the data coexist in the databases and in the non-traditional data that can be accessed and produced by a tax administration, whether these data are of internal or external origin. The analysis of certain anomalies in the data could lead to the discovery of patterns that respond to different causes, being able to evidence these causes certain illicit by taxpayers or acts of corruption when there is the connivance of the taxpayer with the public employee or public official. The purpose of this research is the theoretical development of the causal analysis of certain anomalies of tax data, demonstrating that the data mining methodology contributes to evidence of illicit and corrupt acts, through the application of certain algorithms.

AB - The anomalies in the data coexist in the databases and in the non-traditional data that can be accessed and produced by a tax administration, whether these data are of internal or external origin. The analysis of certain anomalies in the data could lead to the discovery of patterns that respond to different causes, being able to evidence these causes certain illicit by taxpayers or acts of corruption when there is the connivance of the taxpayer with the public employee or public official. The purpose of this research is the theoretical development of the causal analysis of certain anomalies of tax data, demonstrating that the data mining methodology contributes to evidence of illicit and corrupt acts, through the application of certain algorithms.

KW - Algorithms

KW - Anomalous data

KW - Automatic learning

KW - Big data

KW - Data mining

KW - Noise

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DO - 10.1007/978-981-15-7234-0_88

M3 - Contribución a la conferencia

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SN - 9789811572333

T3 - Advances in Intelligent Systems and Computing

SP - 929

EP - 937

BT - Proceedings of International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications, ICMISC 2020

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PB - Springer Science and Business Media Deutschland GmbH

Y2 - 29 March 2020 through 30 March 2020

ER -

Silva J, Solano D, Fernández C, Ramos LN, Urdanegui R, Herz J et al. Indicators for Smart Cities: Tax Illicit Analysis Through Data Mining. En Gunjan VK, Zurada JM, editores, Proceedings of International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications, ICMISC 2020. Springer Science and Business Media Deutschland GmbH. 2021. p. 929-937. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-981-15-7234-0_88

Indicators for Smart Cities: Tax Illicit Analysis Through Data Mining

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