Tax Fraud Detection for Under-Reporting Declarations Using an Unsupervised Machine Learning Approach-Reference-Cited by-同舟云学术

Tax Fraud Detection for Under-Reporting Declarations Using an Unsupervised Machine Learning Approach

Published:2018-07-19 Issue: Volume: Page:
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Container-title:Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
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Author:

de Roux Daniel¹,Perez Boris²,Moreno Andrés¹,Villamil Maria del Pilar¹,Figueroa César³

Affiliation:

1. Universidad de los Andes, Bogotá, Colombia

2. Universidad de los Andes, Bogotá & Univ. Francisco de Paula Santander, Cúcuta, Colombia

3. Secretaria de Hacienda Distrital, Bogotá, Colombia

Funder

COLCIENCIAS

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3219819.3219878

Reference30 articles.

1. Investing Answers. 2017. Tax Fraud. http://www.investinganswers.com/financial-dictionary/laws-regulations/tax-fraud-4116. (2017). Investing Answers. 2017. Tax Fraud. http://www.investinganswers.com/financial-dictionary/laws-regulations/tax-fraud-4116. (2017).

2. Maria Elisabete Neves António Dias Carlos Pinto João Batista. 2016. Signaling Tax Evasion Financial Rations and Cluster Analysis. (2016). http://www.gestaodefraude.eu/wordpress/wp-content/uploads/2016/01/wp051.pdf Maria Elisabete Neves António Dias Carlos Pinto João Batista. 2016. Signaling Tax Evasion Financial Rations and Cluster Analysis. (2016). http://www.gestaodefraude.eu/wordpress/wp-content/uploads/2016/01/wp051.pdf

3. Characterization and detection of taxpayers with false invoices using data mining techniques

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