Tax Crime Prediction with Machine Learning: A Case Study in the Municipality of São Paulo

André Ippolito, Augusto Lozano

2020

Abstract

With the advent of Big Data, several industries utilize data for analytical and competitive purposes. The government sector is following this trend, aiming to accelerate the decision-making process and improve the efficiency of operations. The predictive capabilities of Machine Learning strengthen the decision-making process. The main motivation of this work is to use Machine Learning to aid decision-making in fiscal audit plans related to service taxes of the municipality of São Paulo. In this work, we applied Machine Learning to predict crimes against the service tax system of São Paulo. In our methods, we structured a process comprised of the following steps: feature selection; data extraction from our databases; data partitioning; model training and testing; model evaluation; model validation. Our results demonstrated that Random Forests prevailed over other learning algorithms in terms of tax crime prediction performance. Our results also showed Random Forests’ capability to generalize to new data. We believe that the supremacy of Random Forests is due to the synergy of its ensemble of trees, which contributed to improve tax crime prediction performance. With better predictions, our audit plans became more assertive. Consequently, this rises taxpayers’ compliance with tax laws and increases tax revenue.

Paper Citation

in Harvard Style

Ippolito A. and Lozano A. (2020). Tax Crime Prediction with Machine Learning: A Case Study in the Municipality of São Paulo.In Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-423-7, pages 452-459. DOI: 10.5220/0009564704520459

in Bibtex Style

@conference{iceis20,
author={André Ippolito and Augusto Lozano},
title={Tax Crime Prediction with Machine Learning: A Case Study in the Municipality of São Paulo},
booktitle={Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2020},
pages={452-459},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009564704520459},
isbn={978-989-758-423-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - Tax Crime Prediction with Machine Learning: A Case Study in the Municipality of São Paulo
SN - 978-989-758-423-7
AU - Ippolito A.
AU - Lozano A.
PY - 2020
SP - 452
EP - 459
DO - 10.5220/0009564704520459