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Clustering in Risk Management Systems: Features that will help the Portuguese Tax Authority to identify and classify taxpayers
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Resumo(s)
The payment of tax obligations by taxpayers is crucial to ensure that the State and Government Agencies have the financial resources to meet the public expenditure inherent in a state governed by the rule of law. And in the case of the Tax Authority (TA), it is also important to know its taxpayers and what characterizes them.
The focus of this work is to classify taxpayers in a way that allows the Tax Authority to act differently within various groups, aiming to prevent non-compliance situations. The Portuguese Tax Authority already detected 23251 million euros in uncollectible tax debts in the debt portfolio from 2011 to 2021.
Through this work, it was possible to characterize the taxpayer, not only the so-called “demographic” characteristics, but also characteristics based on his debts, both debts on consumption and income, and also tax debts. This allowed us to understand the behavior of the taxpayers who will have bad debts in order to anticipate them.
The metrics were identified and later the data extraction. A cluster analysis was conducted using the extracted variables in order to understand how taxpayers’ group together based on their characteristics.
With this thesis, we found that there were some differences between clusters created in the 2017 fiscal year and those created in the 2021 fiscal year (individuals with individuals and business with business), which may contain important information for the TA.
Descrição
Internship Report presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence
Palavras-chave
Risk Management Machine Learning model Clustering Unsupervised Learning Portuguese Fiscal System Taxpayers Features SDG 8 - Decent work and economic growth SDG 10 - Reduced inequalities SDG 16 - Peace, justice and strong institutions SDG 17 - Partnerships for the goals