An Accurate Tax Fraud Classifier with Feature Selection based on Complex Network Node Centrality Measure

Tales Matos, José Antonio F. de Macedo, José Maria Monteiro, Francesco Lettich

Abstract

Fiscal evasion represents a very serious issue in many developing countries. In this context, tax fraud detection constitutes a challenging problem, since fraudsters change frequently their behaviors to circumvent existing laws and devise new kinds of frauds. Detecting such changes proves to be challenging, since traditional classifiers fail to select features that exhibit frequent changes. In this paper we provide two contributions that try to tackle effectively the tax fraud detection problem: first, we introduce a novel feature selection algorithm, based on complex network techniques, that is able to capture determinant fraud indicators -- over time, this kind of indicators turn out to be more stable than new fraud indicators. Secondly, we propose a classifier that leverages the aforementioned algorithm to accurately detect tax frauds. In order to prove the validity of our contributions we provide an experimental evaluation, where we use real-world datasets, obtained from the State Treasury Office of Cear{\'a} (SEFAZ-CE), Brazil, to show how our method is able to outperform, in terms of F1 scores achieved, state-of-the-art approaches available in the literature.

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Paper Citation


in Harvard Style

Matos T., Macedo J., Monteiro J. and Lettich F. (2017). An Accurate Tax Fraud Classifier with Feature Selection based on Complex Network Node Centrality Measure . In Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-247-9, pages 145-151. DOI: 10.5220/0006335501450151


in Bibtex Style

@conference{iceis17,
author={Tales Matos and José Antonio F. de Macedo and José Maria Monteiro and Francesco Lettich},
title={An Accurate Tax Fraud Classifier with Feature Selection based on Complex Network Node Centrality Measure},
booktitle={Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2017},
pages={145-151},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006335501450151},
isbn={978-989-758-247-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - An Accurate Tax Fraud Classifier with Feature Selection based on Complex Network Node Centrality Measure
SN - 978-989-758-247-9
AU - Matos T.
AU - Macedo J.
AU - Monteiro J.
AU - Lettich F.
PY - 2017
SP - 145
EP - 151
DO - 10.5220/0006335501450151