Artificial Intelligence and Big Data for Tax Audit Performance: Evidence from the Moroccan Tax Administration

Abstract

The increasing integration of digital technologies in tax administrations has become a major driver of transformation in contemporary fiscal systems, as highlighted in recent studies on public sector digitalization. In this context, this article examines the role of Artificial Intelligence and Big Data in optimizing tax control in Morocco, by identifying the opportunities, challenges, and perspectives associated with their implementation.

The central research question is as follows: To what extent can the integration of AI and Big Data enhance the effectiveness of tax control in Morocco while ensuring transparency and protecting taxpayers’ rights?

Methodologically, the study adopts a mixed-methods approach combining a critical literature review and an empirical investigation. The latter is based on a structured questionnaire administered to a sample of 200 respondents, including 80 tax officials and 120 taxpayers, selected using a purposive sampling method. Data were collected using Likert-scale measures and complemented by semi-structured interviews to deepen the qualitative analysis.

The results indicate that the integration of AI and Big Data contributes to improving the detection of tax anomalies, optimizing risk-based targeting, and enhancing the overall efficiency of tax control processes. However, they also highlight important limitations related to data quality, algorithmic bias, cybersecurity risks, and organizational skill requirements.

This study contributes to the existing body of knowledge by addressing a theoretical gap related to the lack of integrated empirical analyses on the combined use of AI and Big Data in tax control in developing countries, particularly Morocco. It thus provides a renewed perspective on the transition toward intelligent taxation, based on the articulation between technological innovation, data governance, and ethical considerations.

Classification JEL : H26, H83, O33, C88

Paper type: Empirical research