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Authors: Vinicius Di Oliveira 1 ; 2 ; Ricardo Chaim 2 ; Li Weigang 2 ; Sergio Neto 1 ; 2 and Geraldo Filho 2

Affiliations: 1 Secretary of Economy, Brasilia, Federal District, Brazil ; 2 University of Brasilia, Federal District, Brazil

Keyword(s): Machine Learning, Data Preparation, Tax Default, Risk Identification.

Abstract: The failure to perceive non-payment of the tax due is the main risk of tax inspection. The complex tax legislation and the volume of information available must be overcome for facing tax evasion. There is a gap in studies investigating the analysis of tax default risk and Machine Learning algorithms. This study proposes the use of ML algorithms ordinarily used on credit risk analysis as a risk analysis tool for tax default. The tax data preparation issue was faced by discretizing qualitative and quantitative variables. This work presents a new approach for the classification of companies regarding tax avoidance using Machine Learning. The developed ANN model achieved an AUC = 0.9568 in the classification task. The study gathers more than 300 thousand companies in the city of Brasilia - Brazil, analyzing their socioeconomic and financial characteristics.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Di Oliveira, V.; Chaim, R.; Weigang, L.; Neto, S. and Filho, G. (2021). Towards a Smart Identification of Tax Default Risk with Machine Learning. In Proceedings of the 17th International Conference on Web Information Systems and Technologies - WEBIST, ISBN 978-989-758-536-4; ISSN 2184-3252, pages 422-429. DOI: 10.5220/0010712200003058

@conference{webist21,
author={Vinicius {Di Oliveira}. and Ricardo Chaim. and Li Weigang. and Sergio Neto. and Geraldo Filho.},
title={Towards a Smart Identification of Tax Default Risk with Machine Learning},
booktitle={Proceedings of the 17th International Conference on Web Information Systems and Technologies - WEBIST,},
year={2021},
pages={422-429},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010712200003058},
isbn={978-989-758-536-4},
issn={2184-3252},
}

TY - CONF

JO - Proceedings of the 17th International Conference on Web Information Systems and Technologies - WEBIST,
TI - Towards a Smart Identification of Tax Default Risk with Machine Learning
SN - 978-989-758-536-4
IS - 2184-3252
AU - Di Oliveira, V.
AU - Chaim, R.
AU - Weigang, L.
AU - Neto, S.
AU - Filho, G.
PY - 2021
SP - 422
EP - 429
DO - 10.5220/0010712200003058