Towards a new approach to maximize tax collection using machine learning algorithms

Nabil Ourdani, Mohamed Chrayah, Noura Aknin


Efficient tax debt collection is a challenge for Moroccan local tax authorities. This article explores the potential of machine learning techniques and novel strategies to enhance efficiency in this process. We present a practical use case demonstrating the application of machine learning for taxpayer segmentation, improving accuracy in identifying high-risk debtors. Using a comprehensive dataset of tax payment behavior, we showcase the effectiveness of machine learning algorithms in segmenting taxpayers based on their likelihood of non-compliance or debt accumulation. We also investigate innovative strategies that integrate behavioral economics principles to enable better targeted interventions. Real-world case studies in local tax debt collection highlight the impact of these strategies. The findings underscore the transformative potential of machine learning techniques and novel strategies in improving the efficiency of local tax debt collection. Accurate identification of high-risk debtors and tailored enforcement actions help maximize revenue while minimizing resource waste. This research contributes to the existing knowledge by providing insights into the implementation of machine learning techniques and novel strategies in tax debt collection. It emphasizes the importance of data-driven approaches and highlights how local tax authorities can drive efficiency and optimize revenue collection by embracing these advancements.


Clustering; Machine learning; Novel strategies; Tax debt collection; Taxpayer segmentation;

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IAES International Journal of Artificial Intelligence (IJ-AI)
ISSN/e-ISSN 2089-4872/2252-8938 
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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