ISEP - DM – Engenharia de Inteligência Artificial
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- Transfer learning applied to government auditing: A focused approach on financial statements in Maranhão, BrazilPublication . Coelho, Heloisa Guimarães; Marreiros, Maria Goreti CarvalhoSince Brazil’s return to democracy, dozens of laws, decrees and normative instructions have been drafted with the purpose of regulating and improving the mechanisms for controlling and monitoring municipal public resources. These regulations are specifically aimed at the process of accountability by elected officials, who currently rely on the help of accountants responsible for preparing and submitting financial statements to the Courts of Auditors. However, according to data from the TCU (Federal Court of Accounts), in 2023, Maranhão was the Brazilian State with the highest number of rejected accounts. There are several reasons that can lead to these processes being challenged, including incorrect application of resources, flaws in documentation, human errors, among others. In practice, the routine of accountants includes repetitive and mechanical activities that requires considerable time to prepare and review documents, hence often leading to errors in classification and issuing of documentation. In this context, this dissertation investigates the use of Transfer Learning (TL) to improve automation and accuracy in the classification of financial commitment notes, an initial document in the public expenditure cycle, with a specific focus on the context of the state of Maranhão. To this end, BERTimbau, a pre-trained language model for Brazilian Portuguese, was fine-tuned to assist government accountants in reducing classification errors and ensuring compliance with local and national financial regulations. The CRISP-DM methodology, widely used in data science, was adopted to structure the development of the project. The dataset used, consisting of several classifications of commitment notes for the year 2023, was thoroughly analyzed and pre-processed. For the fine-tuning process of the model, two samples with a similar number of data were selected, varying only the number of possible classifications, due to the high degree of imbalance between the classes. Even in a multiclass context with datasets with a reduced number of classes, the results obtained indicate that the BERTimbau model presents strong performance in the classification task, achieving 98% accuracy with an error rate of 0.10 in the test set, highlighting the effectiveness of BERTimbau in public financial auditing applications. These results highlight the effectiveness of BERTimbau for public financial auditing applications. It is therefore concluded that TL models have great potential to optimize and improve financial auditing processes, with positive implications for wider adoption in Brazil.