Detecting Atypical Behaviors of Taxpayers with Risk of Non-Payment in Tax Administration, A Data Mining Framework

• Ordoñez, José ; Hallo, María ^[1]
  1. [1] Escuela Politécnica Nacional, Facultad de Ingeniería de Sistemas, Quito, Ecuador
• Localización: Revista Politécnica, ISSN-e 2477-8990, Vol. 52, Nº. 1, 2023 (Ejemplar dedicado a: Revista Politecnica), págs. 35-44
• Idioma: inglés
• DOI: 10.33333/rp.vol52n1.04
• Títulos paralelos:
  • Detección de Comportamientos Atípicos de Contribuyentes con Riesgo de no Pago en una Administración Tributaria, Un Marco de Trabajo de Minería de Datos
• Enlaces
  • Texto completo
• Resumen
  • español

    Uno de los principales procesos en la administración tributarias es la gestión de cobranza. El objetivo de este proceso, entre otros, es la recuperación de los recursos económicos que han sido declarados por los contribuyentes. Debido a las limitaciones de las administraciones tributarias, tales como: personal, herramientas, tiempo, etc., las administraciones tributarias buscan la recuperación de las deudas en las etapas tempranas de control, donde el costo de recaudación es menor que en las etapas posteriores. Para optimizar el proceso de gestión de cobranza y contribuir a la toma de decisiones, este trabajo propone un marco de trabajo basado en aprendizaje profundo para detectar comportamientos atípicos de contribuyentes con alta probabilidad de no pago. Grupos de comportamiento normal y atípico fueron también analizados para encontrar eventos de interés usando reglas de asociación.

  • English

    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.

     

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