Authors:
André Ippolito
and
Augusto Cezar Garcia Lozano
Affiliation:
Tax Intelligence Office, Under-secretariat of Municipal Revenue, Secretariat of Finance, São Paulo City Hall, 190 Libero Badaró Street, São Paulo, Brazil
Keyword(s):
Data, Government, Decision-making, Machine Learning, Fiscal, Audit, Tax, Crime, Random Forests, Ensemble, Compliance, Revenue.
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 gen
eralize 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.
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