loading
Documents

Research.Publish.Connect.

Paper

Authors: Tales Matos ; José Antonio F. de Macedo ; José Maria Monteiro and Francesco Lettich

Affiliation: Federal University of Ceará, Brazil

ISBN: 978-989-758-247-9

Keyword(s): Fraud Detection, Data Mining, Tax Evasion, Feature Selection.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Data Mining ; Databases and Information Systems Integration ; Enterprise Information Systems ; Sensor Networks ; Signal Processing ; Soft Computing

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. (More)

PDF ImageFull Text

Download
Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 54.85.162.213

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
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

@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},
}

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

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.