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

Authors: Soufiane Aouichaty; Abdelmajid Hajami; Hakim Allali

Addresses: Hassan First University of Settat, Faculty of Sciences and Techniques, VETE Laboratory, Morocco ' Hassan First University of Settat, Faculty of Sciences and Techniques, VETE Laboratory, Morocco ' Hassan First University of Settat, Faculty of Sciences and Techniques, VETE Laboratory, Morocco

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.

DOI: 10.1504/IJMP.2024.140864

International Journal of Management Practice, 2024 Vol.17 No.5, pp.539 - 553

Received: 03 Dec 2022
Accepted: 11 May 2023
Published online: 03 Sep 2024 *

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