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طراحی مدل مبارزه با فساد مالی در نظام مالیاتی ایران با رویکرد مدلسازی ریاضی و یادگیری ماشین | ||
| حسابداری دولتی | ||
| مقاله 4، دوره 12، شماره 1 - شماره پیاپی 23، دی 1404، صفحه 75-108 اصل مقاله (706.75 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.30473/gaa.2026.76515.1843 | ||
| نویسندگان | ||
| امیر جودی سیسی1؛ جمال بحری ثالث* 2؛ سعید جبارزاده کنگرلوئی3؛ علی آشتاب4 | ||
| 1گروه حسابداری، واحد ارومیه، دانشگاه آزاد اسلامی، ارومیه، ایران | ||
| 2گروه حسابداری، واحد ارومیه، دانشگاه آزاد اسلامی ارومیه، ایران | ||
| 3دانشیار، گروه حسابداری، واحد ارومیه، دانشگاه آزاد اسلامی ارومیه، ایران | ||
| 4دانشیار گروه حسابداری، دانشکده اقتصاد و مدیریت، دانشگاه ارومیه، ارومیه، ایران | ||
| چکیده | ||
| موضوع و هدف: این پژوهش با هدف طراحی و اعتباریابی یک مدل جامع مبتنی بر یادگیری ماشین برای مبارزه با فساد مالی در نظام مالیاتی ایران انجام شده است. فساد مالیاتی با ایجاد نابرابری اقتصادی، کاهش درآمدهای عمومی دولت و مانعتراشی در مسیر توسعه پایدار، چالش جدی برای اقتصاد ایران به شمار میرود. روششناسی:پژوهش از نوع کاربردی و با ماهیت توصیفی–تحلیلی است. جامعه آماری شامل کارکنان و کارشناسان سازمان امور مالیاتی کشور بود که با روش نمونهگیری تصادفی طبقهای، ۱۴۰ نفر بهعنوان نمونه انتخاب شدند. دادهها از طریق پرسشنامه محققساخته دارای ۷۱ سؤال در ۹ بُعد جمعآوری گردید. تحلیل دادهها با استفاده از الگوریتمهای جنگل تصادفی، گرادیان تقویتی و شبکه عصبی عمیق انجام شد و برای افزایش تفسیرپذیری نتایج، از روشهای SHAP، PDP، خوشهبندی K-means و AHP فازی بهره گرفته شد. یافتهها: یافتهها نشان داد که پیچیدگی قوانین مالیاتی، عدم شفافیت فرآیندها و ضعف نظارت مهمترین عوامل مؤثر بر فساد مالیاتی هستند. تحلیل خوشهبندی سه خوشه نظام مالیاتی سنتی ، در حال گذار و نظام مالیاتی دیجیتالی را شناسایی کرد. اولویتبندی راهکارها شفافسازی قوانین، آموزش و فرهنگسازی و پاسخگویی را بعنوان مؤثرترین اقدامات معرفی نمود. نتیجهگیری، اصالت و افزوده به دانش:شبیهسازی سناریوها حاکی از آن است که اجرای کامل راهکارهای پیشنهادی میتواند فساد مالیاتی را تا ۴۷.۸ درصد طی پنج سال کاهش دهد اصالت پژوهش در تلفیق پیشرفته یادگیری ماشین با روشهای تفسیرپذیر و تصمیمگیری چندمعیاره و ارائه مدلی بومی، جامع و اولویتبندیشده است که میتواند مبنایی علمی برای سیاستگذاریهای ضدفساد و پژوهشهای آتی فراهم آورد. | ||
| کلیدواژهها | ||
| فساد نظام مالیاتی؛ یادگیری ماشین؛ هوشمندسازی مالیاتی؛ کنوانسیون مریدا؛ دیجیتالیسازی | ||
| عنوان مقاله [English] | ||
| Designing a Model for Combating Financial Corruption in the Iranian Tax System: A Mathematical Modeling and Machine Learning Approach | ||
| نویسندگان [English] | ||
| Amir Judy sisi1؛ Jamal Bahri Sales2؛ Saeid Jabbarzadeh Kangarluei3؛ Ali Ashtab4 | ||
| 1Department of Accounting, Ur. C, Islamic Azad University, Urmia, Iran | ||
| 2: Associate Prof, Department of Accounting, Ur.C., Islamic Azad University, Urmia, Iran | ||
| 3Department of Accounting, Ur.C., Islamic Azad University, Urmia, Iran | ||
| 4Department of Accounting, Faculty of Economics and Management, Urmia University, Urmia, Iran | ||
| چکیده [English] | ||
| Topic and Objective: This research aimed to design and validate a comprehensive machine learning-based model for combating financial corruption in Iran's tax system. Tax corruption poses a significant challenge to the Iranian economy by exacerbating economic inequality, reducing public government revenues, and obstructing sustainable development. Methodology: The study is applied in purpose and descriptive-analytical in nature. The statistical population consisted of employees and experts from the Iranian National Tax Administration, with 140 participants selected through stratified random sampling. Data were collected using a researcher-developed questionnaire comprising 71 items across nine dimensions. Analysis employed advanced machine learning algorithms, including Random Forest, Gradient Boosting, and Deep Neural Networks. To enhance interpretability, SHAP values, Partial Dependence Plots (PDP), K-means clustering, and Fuzzy Analytic Hierarchy Process (AHP) were applied. Findings: Results revealed that complexity of tax laws, lack of process transparency, and weak oversight were the most influential factors in tax corruption. Cluster analysis identified three tax system profiles: traditional, transitional, and digital. Prioritization of solutions highlighted law clarification and simplification, education and cultural promotion, and accountability enhancement as the most effective measures. Conclusion, Originality, and Contribution to Knowledge: Scenario simulations indicate that full implementation of the proposed strategies could reduce tax corruption by up to 47.8% over five years. The study's originality stems from its advanced integration of machine learning with interpretable methods and multi-criteria decision-making, delivering an indigenous, comprehensive, and prioritized model. This framework offers a solid scientific basis for anti-corruption policymaking and future research. | ||
| کلیدواژهها [English] | ||
| Corruption in the tax system, Machine learning, Tax intelligence, UNCAC (Merida Convention), Digitalization | ||
| مراجع | ||
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آمار تعداد مشاهده مقاله: 296 تعداد دریافت فایل اصل مقاله: 4 |
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