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The Impact of Artificial Intelligence on Improving the Accuracy of Accounting Estimates | ||
| AL-Qadisiyah Journal For Administrative and Economic sciences | ||
| Volume 28, Issue 2, July 2026, Pages 149-162 PDF (1.75 M) | ||
| Document Type: Research Paper | ||
| DOI: 10.33916/qjae.2026.02149162 | ||
| Author | ||
| OMAR ADEEB QASSIM | ||
| College of Administration and Economics, Wasit University | ||
| Abstract | ||
| The accounting profession is undergoing a profound transformation due to recent advancements in Artificial Intelligence (AI) technologies, which are reshaping how data is processed and analyzed within accounting systems. This transformation has directly impacted the quality and accuracy of accounting estimates, such as fair value measurement, allowance for doubtful debts, depreciation estimation, and financial risk assessment. This study aims to analyze the effect of applying AI technologies on improving the accuracy of accounting estimates compared to traditional methods, with a focus on reducing human errors and professional biases and enhancing the objectivity of financial reporting. The study employs statistical analysis using the Autoregressive Distributed Lag (ARDL) model to examine both short-and long-term relationships between variables. The analysis investigates the impact of AI, machine learning, big data, and smart system governance on the accuracy of accounting estimates. The results indicate that the application of AI technologies has a significant positive effect on improving the accuracy and objectivity of accounting estimates, both in the short and long term. Additionally, AI facilitates the efficient analysis of large volumes of historical and financial data, enhancing the quality and reliability of financial reporting. The study provides practical recommendations to promote the adoption of AI in financial institutions and businesses to achieve greater accuracy and transparency in accounting estimates. | ||
| Keywords | ||
| Artificial Intelligence; Accounting Estimates; Financial Information Quality | ||
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