Explaining the Role of Artificial Intelligence in Enhancing Financial Reporting Quality with an Emphasis on Detecting Accounting Misstatements and Reducing Information Asymmetry
Keywords:
Artificial intelligence, financial reporting, accounting misstatements, information asymmetry, machine learning, intelligent auditing, disclosure qualityAbstract
This study aimed to explain the role of artificial intelligence in improving financial reporting quality, with particular emphasis on detecting accounting misstatements and reducing information asymmetry in capital market settings. This study adopted a qualitative research design using thematic analysis. The participants consisted of accounting experts, auditors, financial analysts, financial technology specialists, and chief financial officers from Tehran. Purposeful sampling was applied based on professional experience and direct familiarity with financial reporting, auditing, capital market disclosure, or accounting information systems. Data collection continued until theoretical saturation was achieved. A total of 24 participants were interviewed; theoretical saturation was reached after the twenty-first interview, and three additional interviews were conducted to confirm the stability of the extracted categories. Data were collected only through semi-structured interviews. All interviews were transcribed verbatim and analyzed using NVivo software through open coding, concept grouping, subtheme development, and main category extraction. The findings revealed five main categories explaining the role of artificial intelligence in financial reporting quality: “intelligent detection of accounting misstatements and abnormal patterns,” “enhancement of transparency and reliability of financial reporting,” “reduction of information asymmetry in the capital market,” “redefinition of the professional role of accountants and auditors,” and “requirements of data governance, internal control, and algorithmic accountability.” Participants emphasized that machine learning, big data analytics, natural language processing, and continuous transaction monitoring can identify red flags related to earnings manipulation, abnormal accruals, inconsistent disclosures, unusual journal entries, and opaque financial narratives earlier and more comprehensively than conventional procedures. The study concludes that artificial intelligence should not be viewed merely as a technical tool for automating accounting routines. Rather, it can serve as a strategic mechanism for enhancing financial information quality, improving comparability, strengthening transparency, reducing misstatement risk, and narrowing information gaps among managers, investors, auditors, and regulators. However, its effective implementation depends on data quality, digital infrastructure, professional competence, human oversight, ethical algorithm design, and transparent data governance frameworks.
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