Title: A data mining model to predict the debts with risk of non-payment in tax administration

Authors: José Ordóñez-Placencia; María Hallo; Sergio Luján-Mora

Addresses: Departamento de Informática y Ciencias de Computación, Escuela Politécnica Nacional, Ecuador ' Departamento de Informática y Ciencias de Computación, Escuela Politécnica Nacional, Ecuador ' Departamento de Lenguajes y Sistemas Informáticos, Universidad de Alicante, Spain

Abstract: One of the main tasks in tax administration is debt management. The main goal of this function is tax due collection. Statements are processed in order to select strategies to use in the debt management process to optimise the debt collection process. This work proposes to carry out a data mining process to predict debts of taxpayers with high probability of non-payment. The data mining process identifies high-risk debts using a survival analysis on a dataset from a tax administration. Three groups of tax debtors with similar payment behaviour were identified and a success rate of up to 90% was reached in estimating the payment time of taxpayers. The concordance index (C-index) was used to determine the performance of the constructed model. The highest prediction rate reached was 90.37% corresponding to the third group.

Keywords: data mining; debt management analysis; machine learning; taxpayer behaviour patterns; survival analysis.

DOI: 10.1504/IJIDS.2024.140186

International Journal of Information and Decision Sciences, 2024 Vol.16 No.3, pp.319 - 339

Received: 06 May 2021
Accepted: 17 Oct 2021
Published online: 29 Jul 2024 *

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