[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118376-en":3,"doc-seo-118376-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118376,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Towards a new approach to maximize tax collection using machine learning algorithms","Efficient tax debt collection remains a pressing challenge for Moroccan local tax authorities, where rising outstanding liabilities reduce revenue and strain public service delivery. This article evaluates machine learning techniques combined with novel intervention strategies to improve operational efficiency. A practical case study applies taxpayer segmentation from tax payment behavior data to identify high-risk debtors with higher accuracy. The research further integrates behavioral economics to support targeted enforcement and proactive engagement, helping maximize revenue while minimizing legal disputes and wasted resources.","Towards a new approach to maximize tax collection using machine learning algorithms  \nNabil Ourdani, Mohamed Chrayah, Noura Aknin  \nTMS Research Unit, Abdelmalek Essaadi University, UAE, Tetuan, Morocco  \nArticle history:  \nReceived Sep 25, 2023 Revised Oct 17, 2023 Accepted Nov 7, 2023  \nKeywords:  \nClustering  \nMachine learning Novel strategies Tax debt collection Taxpayer segmentation  \nCorresponding Author:  \nEfficient 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 noncompliance 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.  \nThis is an open access article under the CC BY-SA license.  \nNabil Ourdani  \nTMS Research Unit, Abdelmalek Essaadi University Tetuan, Morocco [Email: nabil.ourdani@etu.uae.ac.ma](Email: nabil.ourdani@etu.uae.ac.ma)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nIn recent years, local tax authorities in Morocco have faced significant challenges in collecting tax debts, resulting in substantial revenue loss and hindering the provision of essential public services. According to a report by the Court of Accounts of Morocco [1], there was a 29% deterioration in revenue collection as outstanding amounts tobe recovered increased from 13 billion Moroccan Dirhams (MAD) to 16.8 billion MAD between 2009 and 2013. This increase, with an average annual growth rate of 7.3%, indicates significant challenges in the collection process. Tax debt collection involves complex processes, including taxpayer segmentation, enforcement actions, and resource allocation (human, financial, and technological), particularly tailored to the unique context of African countries [2] . However, traditional approaches often suffer from inefficiencies, relying on manual methods and generic strategies that do not effectively target high-risk debtors. To address these challenges and achieve a reduction in processing time, an increase in taxes collected, and a decrease in the number of legal disputes, there is a growing interest in harnessing machine learning techniques and innovative strategies aimed at optimizing efficiency in local tax debt collection [3] .  \nMachine learning has revolutionized various domains by enabling the automatic extraction of insightsand patterns from large datasets especially in finance [4]. In the context of tax debt collection, machine learning techniques offer immense potential for improving segmentation accuracy, predicting debt default probabilities, and optimizing resource allocation [5] . By utilizing historical data and sophisticated algorithms, local tax authorities can identify taxpayers with the highest likelihood of non-compliance or debt accumulation, enabling targeted interventions and enforcem","cbCaiuQXf0lMrBHG","https://ap.wps.com/l/cbCaiuQXf0lMrBHG","pdf",467480,1,10,"English","en",105,"# Introduction\n# Machine learning for tax debt collection\n## Segmentation, risk prediction, and resource allocation\n# Novel strategies for targeted interventions\n## Behavioral economics and analytics\n# Importance of improving tax collection methods","[{\"question\":\"What problem does the article address in Moroccan local tax debt collection?\",\"answer\":\"Local authorities face inefficiencies that lead to substantial revenue loss as tax debts increase, making it harder to allocate resources and target high-risk debtors effectively.\"},{\"question\":\"How does machine learning help in the proposed approach?\",\"answer\":\"The method uses taxpayer segmentation on historical tax payment behavior to improve the accuracy of identifying high-risk debtors and support more effective enforcement decisions.\"},{\"question\":\"What are the “novel strategies” mentioned, and what role does behavioral economics play?\",\"answer\":\"Novel strategies incorporate behavioral economics principles, alongside other analytics, to better understand taxpayer behavior and enable more proactive, tailored interventions that encourage compliance.\"}]","Towards a new approach to maximize tax collection using machine learning algorithms | 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problem does the article address in Moroccan local tax debt collection?","Question",{"text":75,"@type":76},"Local authorities face inefficiencies that lead to substantial revenue loss as tax debts increase, making it harder to allocate resources and target high-risk debtors effectively.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does machine learning help in the proposed approach?",{"text":80,"@type":76},"The method uses taxpayer segmentation on historical tax payment behavior to improve the accuracy of identifying high-risk debtors and support more effective enforcement decisions.",{"name":82,"@type":73,"acceptedAnswer":83},"What are the “novel strategies” mentioned, and what role does behavioral economics play?",{"text":84,"@type":76},"Novel strategies incorporate behavioral economics principles, alongside other analytics, to better understand taxpayer behavior and enable more proactive, tailored interventions that encourage 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