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

Universidad de la Costa
Corporación Universitaria Minuto de Dios
Universidad Simón Bolívar

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

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.

Original language	English
Title of host publication	Proceedings of International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications, ICMISC 2020
Editors	Vinit Kumar Gunjan, Jacek M. Zurada
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	929-937
Number of pages	9
ISBN (Print)	9789811572333
DOIs	https://doi.org/10.1007/978-981-15-7234-0_88
State	Published - 2021
Externally published	Yes
Event	International Conference on Recent Trends in Machine Learning, IOT, Smart Cities and Applications, ICMISC 2020 - Hyderabad, India Duration: 29 Mar 2020 → 30 Mar 2020

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	1245
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Conference

Conference	International Conference on Recent Trends in Machine Learning, IOT, Smart Cities and Applications, ICMISC 2020
Country/Territory	India
City	Hyderabad
Period	29/03/20 → 30/03/20

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 11 Sustainable Cities and Communities
SDG 16 Peace, Justice and Strong Institutions

Keywords

Algorithms
Anomalous data
Automatic learning
Big data
Data mining
Noise

Access to Document

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

Cite this

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. In 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",

year = "2021",

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

language = "Ingl{\'e}s",

isbn = "9789811572333",

series = "Advances in Intelligent Systems and Computing",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "929--937",

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booktitle = "Proceedings of International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications, ICMISC 2020",

}

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. in 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).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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

UR - https://www.scopus.com/pages/publications/85096427225

U2 - 10.1007/978-981-15-7234-0_88

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

M3 - Contribución a la conferencia

AN - SCOPUS:85096427225

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

A2 - Gunjan, Vinit Kumar

A2 - Zurada, Jacek M.

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. In Gunjan VK, Zurada JM, editors, 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

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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