تأثیر هوش مصنوعی بر بهینه‌سازی بودجه‌ریزی و دقت پیش‌بینی مالی در حسابداری مدیریت: مطالعه موردی شرکت‌های تولیدی استان تهران

نوع مقاله : پژوهشی

نویسندگان
1 استاد تمام و عضو هیات علمی، گروه حسابداری، دانشگاه آزاد اسلامی، واحد تهران جنوب، تهران، ایران.
2 دانشجوی دکتری حسابداری، دانشگاه آزاد اسلامی، واحد تهران جنوب، تهران، ایران.
چکیده
هدف اصلی این پژوهش بررسی تأثیر استفاده از هوش مصنوعی بر بهینه‌سازی بودجه‌ریزی و دقت پیش‌بینی مالی در حسابداری مدیریت است. روش‌های سنتی پیش‌بینی مالی و بودجه‌ریزی غالباً با محدودیت‌هایی چون خطاهای انسانی، ناکارآمدی در مدیریت منابع، و ناتوانی در تحلیل داده‌های پیچیده مواجه هستند. در این پژوهش، نقش هوش مصنوعی از طریق الگوریتم‌های یادگیری ماشین و شبکه‌های عصبی به کمک نرم‌افزار متلب مورد بررسی قرار گرفت. جامعه آماری شامل شرکت‌های تولیدی استان تهران بوده و داده‌ها از طریق پرسشنامه‌های بسته، مصاحبه‌های نیمه‌ساختاریافته، و تحلیل گزارش‌های مالی جمع‌آوری شدند. نتایج آماری نشان داد که استفاده از هوش مصنوعی باعث کاهش خطاهای پیش‌بینی مالی شده و فرآیند بودجه‌ریزی را بهینه می‌کند. همچنین، فناوری‌های هوش مصنوعی توانسته‌اند شناسایی الگوهای مالی پیچیده، مدیریت بهتر ریسک‌ها، افزایش سرعت پردازش داده‌ها، و تحلیل دقیق‌تری از روندهای مالی شرکت‌ها فراهم کنند. یافته‌ها نشان دادند که دقت پیش‌بینی در شرکت‌های استفاده‌کننده از هوش مصنوعی تا 88 درصد افزایش یافته و عملکرد بودجه‌ریزی نیز با میانگین امتیاز 4.5 از 5 بهتر از شرکت‌های دیگر ارزیابی شده است. این پژوهش نتیجه‌گیری می‌کند که سرمایه‌گذاری در فناوری‌های هوش مصنوعی می‌تواند تحولی اساسی در مدیریت مالی و عملیاتی سازمان‌ها ایجاد کند. به‌ویژه در صنایع تولیدی، این فناوری علاوه بر افزایش دقت و بهره‌وری، به مدیران مالی امکان ارائه پیش‌بینی‌های مبتنی بر اطلاعات دقیق‌تر و تصمیم‌گیری با ریسک کمتر را می‌دهد. پیشنهاد می‌شود تحقیقات آینده به بررسی چالش‌ها و موانع اجرایی این فناوری در صنایع مختلف بپردازد.

کلیدواژه‌ها

موضوعات


عنوان مقاله English

The Effect of Artificial Intelligence on Budgeting Optimization and Financial Forecasting Accuracy in Management Accounting: A Case Study of Manufacturing Companies in Tehran Province

نویسندگان English

Roya Darabi 1
Mehdi Dolatshahi 2
1 Full Professor and Faculty Member, Department of Accounting, Islamic Azad University, South Tehran Branch, Tehran, Iran.
2 PhD Student in Accounting, Islamic Azad University, South Tehran Branch, Tehran, Iran.
چکیده English

The primary aim of this study is to explore the impact of artificial intelligence (AI) on budgeting optimization and financial forecasting accuracy in management accounting. Traditional forecasting and budgeting methods often face limitations such as human errors, inefficiencies in resource management, and an inability to analyze complex financial data. This study examines the role of AI, using machine learning algorithms and neural networks implemented via MATLAB software. The statistical population includes manufacturing companies in Tehran Province. Data was collected through closed questionnaires, semi-structured interviews, and analysis of financial reports. The results reveal that AI reduces financial forecasting errors, optimizes budgeting processes, and significantly improves the speed of data processing, identification of complex financial patterns, and risk management. Findings further show that companies using AI achieved up to an 88% increase in forecasting accuracy and demonstrated superior performance in budgeting, with an average score of 4.5 out of 5 compared to companies not employing AI. This study concludes that investing in AI technologies can create a fundamental transformation in financial and operational management. Particularly in manufacturing industries, AI not only raises forecasting accuracy and overall efficiency but also empowers financial managers to make data-driven decisions with lower risk. It is recommended that future research explore the challenges and barriers to implementing AI technologies across various industries to better understand how these innovations can effectively integrate into financial management systems.

کلیدواژه‌ها English

Artificial Intelligence
Budgeting
Forecasting Accuracy
Management Accounting
Matlab
Abbasi, M. , hagh parast, A. A. , Vahedian Ghaffari, A. and salehi, A. H. (2025). Investigating the Impact of Artificial Intelligence on the Quality of Managerial Accounting Information (A Case Study of Companies Listed on the Stock Exchange). (e227141). Strategic Management Accounting, 2(2), e227141. [in Persian] https://doi.org/10.22034/smajournal.2025.512751.1009
Abbasi Meybodi, Z. and Mohebbi, H. (2025). Analyzing Factors Influencing Quality 4.0 Development in the Smart Supply Chain Using a Hybrid DEMATEL and Interpretive Structural Modeling (ISM) Approach. Strategic Management Accounting, 2(1), 24-54. [in Persian] https://doi.org/10.22034/smajournal.2025.519941.1018
Ahmed, A., Albaz, M. M., & Metwaly, A. Z. (2022). The Role of Artificial Intelligence Technologies in Improving the Performance of the Management Accountant considering the Egyptian State's Trend Toward Digital Transformation. World Research of Business Administration Journal, 2(3), 167-182. http://dx.doi.org/10.56830/ZAAF5463
Capone, C., Talgat, S., Hazir, O., Abdrasheva, K., & Kozhakhmetova, A. (2024). Artificial Intelligence Models for Predicting Budget Expenditures. Eurasian Journal of Economic and Business Studies, 68(1), 32-43. https://doi.org/10.47703/ejebs.v68i1.331
Chen, Y. (2021). Framework of the smart finance and accounting management model under the artificial intelligence perspective. Mobile Information Systems, 2021(1), 4295191. http://dx.doi.org/10.1155/2021/4295191
Chukwuma-Eke, E. C., Ogunsola, O. Y., & Isibor, N. J. (2022). A conceptual framework for financial optimization and budget management in large-scale energy projects. International Journal of Multidisciplinary Research and Growth Evaluation, 2(1), 823-834. https://www.allmultidisciplinaryjournal.com/uploads/archives/20250331144653_MGE-2025-2-169.1.pdf
Farzaneh Kalourzi, Mostafa., Sadr, Hossein., & Khodadadhosseini, Seyyed Hamid. (2024). Analyzing the effects of ChatGPT on web-based electronic banking using Grounded Theory (Case Study: Agriculture Bank of Iran). [in Persian] https://civilica.com/doc/2271396  
Grobler-Dębska, K., Mularczyk, R., Gawęda, B., & Kucharska, E. (2024). Time Series Methods and Business Intelligent Tools for Budget Planning—Case Study. Applied Sciences, 15(1), 287. http://dx.doi.org/10.3390/app15010287
 Harandi, A. and Hadizadeh, M. (2024). Efficient Government Budgeting Based on Artificial Intelligence in the Future of Iran: Scenarios, Policies, and Actions. Program and Development Research, 5(1), 115-147. https://www.journaldfrc.ir/article_205536_en.html
Jain, V., & Kulkarni, P. A. (2023). Integrating AI techniques for enhanced financial forecasting and budgeting strategies. International Journal of Economics and Management Studies, 10(09), 9-15. http://dx.doi.org/10.14445/23939125/IJEMS-V10I9P102
Korobeynikova, O. M., Korobeynikov, D. A., Popova, L. V., Chekrygina, T. A., & Shemet, E. S. (2021, March). Artificial intelligence for digitalization of management accounting of agricultural organizations. In IOP Conference Series: Earth and Environmental Science (Vol. 699, No. 1, p. 012049). IOP Publishing. http://dx.doi.org/10.1088/1755-1315/699/1/012049
Lari Dasht-Biyaz, Abbas, Khadem, Hamid, & Hamidehpoor, Kiana. (2015). Comparison of the ability of artificial intelligence algorithms in predicting EPS changes with an emphasis on influential components.  [in Persian] https://www.sid.ir/paper/826445/fa
Maleki, Davood, Ghorbani, Neda, Ahmadi, Mohammadreza, & Arianyan, Ehsan. (2024). Analysis of innovation and future trend prediction of information technology in Iran based on data from the Telecom 2024 exhibition.  [in Persian]
Namifard Tehran, F. , Sotudeh, R. , Haghparast, A. and hirad, A. (2025). Presenting the structural model of the efficiency indicators and components of blockchain technology in the accounting and auditing unit. Journal of Management Accounting and Auditing Knowledge, 14(56), 395-410. [in Persian]  https://www.jmaak.ir/article_23869_en.html?lang=fa
Nguyen, T.T.T. (2024). Toward Financial Optimization: Assessing the Influence of Budget Process on Effective Accounting Management. Management Dynamics in the Knowledge Economy, 12(2), 2024. 116-132. https://doi.org/10.2478/mdke-2024-0008
Okeke, N. I., Bakare, O. A., & Achumie, G. O. (2024). Forecasting financial stability in SMEs: A comprehensive analysis of strategic budgeting and revenue management. Open Access Research Journal of Multidisciplinary Studies, 8(1), 139-149. http://dx.doi.org/10.53022/oarjms.2024.8.1.0055
Osoolian, M. , Nikmaram, A. and Karimi, M. (2025). Predicting Index Trend Using Hybrid Neural Networks with a Focus on Multi-Scale Temporal Feature Extraction in the Tehran Stock Exchange. Financial Research Journal27(1), 85-113. [in Persian] https://doi.org/10.22059/frj.2024.377816.1007611
Panait, M. P. I., Musat, L., & Cosor, M. (2025). Considerations Regarding the Use of Artificial Intelligence in Financial Management and Accounting in State Pre-University Educational Institutions. Acta Universitatis Danubius. Œconomica, 21(1), 74-94. https://dj.univ-danubius.ro/index.php/AUDOE/article/view/3260
Pavlovic, M., Gligoric, C., Zdravkovic, F., & Pavlovic, D. (2024). Revolutionizing management accounting: the role of artificial intelligence in predictive analytics, automated reporting, and decision-making. Business & Management Compass, 68(4), 23-42. http://dx.doi.org/10.56065/nxn2gx53
Saadati, E. , ansari, Z. , farahmandniaa, A. and asadimehr, K. (2025). The Strategy based approach to applying artificial intelligence technology in accounting: with reference to auditing and management accounting trends. Strategic Management Accounting, 2(2), 1-20. [in Persian]  https://doi.org/10.22034/smajournal.2025.526677.1036
Sajadi Rad, Tahere., & Mostajeran, Abdolrasoul. (2024). Developing a framework for managing artificial intelligence risks in the financial services industry. Journal of New Research Approaches in Management and Accounting, 8(31), 1495–1510. [in Persian] https://majournal.ir/index.php/ma/article/view/2952
Shojaei Nasir Abadi, M. , Piri, H. and Sotudeh, R. (2024). Comparative Analysis of Artificial Neural Networks and Linear Regression in Predicting the Continuation of Shareholders' Overreaction Trends. Accounting and Auditing Review, 31(3), 547-572. [in Persian] https://doi.org/10.22059/acctgrev.2024.373333.1008920
Sotudeh, R. , Haghparast, A. and Hirad, A. (2025). Management Accounting and Resilience Economics Model for Sustainable Development of Manufacturing Companies. Strategic Management Accounting, 1(1), 40-64. [in Persian] https://doi.org/10.22034/smajournal.2025.511901.1003
Subrahmanyam, S., Azoury, N., & Sarkis, N. (2024, May). AI and Business Planning: Revolutionizing Forecasting and Resource Allocation. In 2024 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI) (pp. 1-6). IEEE. https://doi.org/10.1109/ACCAI61061.2024.10601728
Valle-Cruz, D., Fernandez-Cortez, V., & Gil-Garcia, J. R. (2022). From E-budgeting to smart budgeting: Exploring the potential of artificial intelligence in government decision-making for resource allocation. Government Information Quarterly, 39(2), 101644. https://doi.org/10.1016/j.giq.2021.101644
Zarei, Rahnamaye Roudposhti, Khanmohammadi, Mohammad-Hamed, Kordlouee, & Hamidreza Kordlouee. (2024). Presenting a model for fraud prediction based on artificial intelligence (Application of the Support Vector Machine (SVM) model). Journal of Accounting and Management Auditing Knowledge, 15(59), 175–186.  [in Persian] https://www.jmaak.ir/article_23936_en.html 
Zeinali Kermani, A. ., Piri, H., Payan, A. ., & Sotudeh, R. (2025). Investigating the Role of Blockchain Technology and Artificial Intelligence in the Enhancement and Improvement of Accounting Information Systems Using an Interpretive Structural Approach. Business, Marketing, and Finance Open, 2(4), 1-17. [in Persian]   https://doi.org/10.61838/bmfopen.2.4.8