Classificação das despesas com pessoal no contexto dos Tribunais de Contas

• Autor(a) principal: Teixeira, Pedro Henrique
• Data de Publicação: 2023
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Repositório Institucional da UFG
• Texto Completo: http://repositorio.bc.ufg.br/tede/handle/tede/13074
• topic: Auditoria; Despesa pública; Aprendizado de máquina; Semi-supervisionado; Classificação de texto; Audit; Public expense; Machine learning; Semi-supervised; Text classification; CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO

The Court of Accounts of the Municipalities of the State of Goiás (TCMGO) uses the expenditure data received monthly from the municipalities of Goiás to check the expenditure related to personnel expenses, as determined by LRF. However, there are indications that the classification of expenses sent by the municipal manager may contain inconsistencies arising from fiscal tricks, creative accounting or material errors, leading TCMGO to make decisions based on incorrect reports, resulting in serious consequences for the inspection process. As a way of dealing with this problem, this work used text classification techniques to identify, based on the description of the expense and instead of the code provided by the municipality, the class of a personnel expense. For this, a corpus was built with 17,116 expense records labeled by domain experts, using binary and multi-class approaches. Data processing procedures were applied to extract attributes from the textual description, as well as assign numerical values to each instance of the data set with the TF-IDF algorithm. In the modeling stage, the algorithms Multinomial Naïve Bayes, Logistic Regression and Support Vector Machine (SVM) were used in supervised classification. SVM proved to be the best algorithm, with F-Score of 0.92 and 0.97, respectively, on the multi-class and binary corpus. However, it was found that the labeling process carried out by human experts is complex, time-consuming and expensive. Therefore, this work developed a method to classify personnel expenses using only 235 labeled samples, improved by unlabeled instances, based on the adaptation of the Self-Training algorithm, producing very promising results, with an average F-Score between 0.86 and 0.89.