Indicators for smart cities: tax illicit analysis through data mining

The anomalies in the data coexist in the databases and in the non-traditional data that can be accessed and produced by a tax administration, whether these data are of internal or external origin. The analysis of certain anomalies in the data could lead to the discovery of patterns that respond to different causes, being able to evidence these causes certain illicit by taxpayers or acts of corruption when there is the connivance of the taxpayer with the public employee or public official. The purpose of this research is the theoretical development of the causal analysis of certain anomalies of tax data, demonstrating that the data mining methodology contributes to evidence of illicit and corrupt acts, through the application of certain algorithms.

• Autores: Silva, Jesús; Solano, Darwin; Fernández, Claudia; Nieto Ramos, Lainet; Urdanegui, Rosella; Herz, Jeannette; Mercado, Alberto; Ovallos-Gazabon, David
• Tipo de recurso: Article of journal
• Fecha de publicación: 2021
• Institución: Corporación Universidad de la Costa
• Repositorio: REDICUC - Repositorio CUC
• Idioma: eng
• OAI Identifier: oai:repositorio.cuc.edu.co:11323/7707
• Acceso en línea: https://hdl.handle.net/11323/7707; https://doi.org/10.1007/978-981-15-7234-0_88; https://repositorio.cuc.edu.co/
• Palabra clave: Data mining; Anomalous data; Algorithms; Automatic learning; Big data; Noise
• Rights: openAccess
• License: Attribution-NonCommercial-NoDerivatives 4.0 International