TAD_BERT: automatic decision classification model for national tax appeals commission in Morocco using BERT

Soufiane Aouichaty, Abdelmajid Hajami and Hakim Allali

International Journal of Management Practice, 2024, vol. 17, issue 5, 539-553

Abstract: The extraction and classification of data from the Moroccan National Tax Appeals Commission are complex and non-existent in the Moroccan legal and tax domain (NTAC). Rulings data extraction relies too heavily on manual labour, is inefficient, time-consuming, and prone to mistakes. Tools for automating the tax rulings task have been suggested to assist the tax appeals decisions (TAD); however, applying a generic natural language processing model to domain-specific items and lacking training text data present difficulties. In this paper, we developed a text extraction system to boost productivity, creating a database for analysis and prediction. Our study aims to automate data extraction and classification using REGEX and the BERT algorithm. Among 562 rulings (1999-2018) on tax irregularities, we extracted 201 corporate tax-related decisions and 550 disputes on corporate tax headings. Our model achieved strong results, with a precision of 99.1% and an accuracy of 98.6%.

Keywords: text classification; automatic decision classification; BERT; REGEX; national tax. (search for similar items in EconPapers)
Date: 2024
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