Abstract

JOSE, Ordóñez,  and  MARIA, Hallo,. Detecting Atypical Behaviors of Taxpayers with Risk of Non-Payment in Tax Administration, A Data Mining Framework. Rev Politéc. (Quito) [online]. 2023, vol.52, n.1, pp.35-44. ISSN 2477-8990.  https://doi.org/10.33333/rp.vol52n1.04.

One of the primary processes in tax administration is debt collection management. The objective of this process, among others, is to recover economic resources that have been declared by taxpayers. Due to limitations in tax administration such as staffing, tools, time, and others, tax administrations seek to recover debts in the early stages of control where collection costs are lower than in subsequent stages. To optimize the debt collection management process and contribute to decision-making, this study proposes a deep learning-based framework to detect atypical behaviors of taxpayers with a high probability of non-payment. Normal and atypical behavior groups were also analyzed to identify interesting events using association rules.

Keywords : Data mining; debt management analysis; machine learning; patterns of taxpayer behaviors.

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