EXPLORING ARTIFICIAL INTELLIGENCE APPLICATIONS IN ACCOUNTING PRACTICES AND THEIR IMPLICATIONS FOR FINANCIAL REPORTING QUALITY AND DECISION-MAKING RELIABILITY
DOI:
https://doi.org/10.36526/sosioedukasi.v15i4.9702Keywords:
Artificial Intelligence, Accounting Practices, Financial Reporting QualityAbstract
Artificial Intelligence (AI) is increasingly being incorporated into accounting and financial reporting, but the literature does not support treating adoption as an automatic guarantee of better outcomes. This study uses a qualitative narrative literature review to synthesize peer-reviewed studies and authoritative professional reports on AI applications in accounting, financial reporting quality, accounting information quality, internal control, and decision support. The review prioritizes literature published from 2018 to 2026 and retains earlier foundational studies where they are necessary to explain the theoretical framework. Literature was identified through targeted searches using combinations of the terms ‘artificial intelligence’, ‘machine learning’, ‘generative AI’, ‘accounting’, ‘financial reporting’, ‘information quality’, ‘fraud detection’, ‘internal control’, and ‘decision making’, and was selected for direct relevance, academic or institutional credibility, and substantive discussion of accounting outcomes. The synthesis indicates that AI is most consistently discussed as a means of expanding processing capacity, automating selected tasks, detecting anomalies, supporting predictive analysis, and assisting the interpretation of financial information. Evidence concerning financial reporting quality is more conditional: automation may be associated with fewer internal-control weaknesses, while recent studies also identify risks related to monitoring, data quality, explainability, bias, cybersecurity, and governance. The review therefore conceptualizes the relationship as AI-enabled accounting processes → accounting information quality → financial reporting and decision-support outcomes, moderated by organizational readiness and human oversight. The study contributes by integrating these three outcome domains rather than treating AI adoption, reporting quality, and decision support as separate topics.
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