DAMPAK ARTIFICIAL INTELLIGENCE TERHADAP KUALITAS LAPORAN KEUANGAN : ANALISIS SYSTEMATIC LITERATURE REVIEW PADA PERUSAHAAN MANUFAKTUR
DOI:
https://doi.org/10.61722/jaem.v2i4.7400Keywords:
Artificial Intelligence, Financial Reporting Quality, Systematic Literature Review, Manufacturing Companies, Digital AccountingAbstract
The development of digital technology has triggered a significant transformation in the field of accounting, particularly through the integration of Artificial Intelligence (AI). This innovation facilitates the automation of transaction recording, financial data analysis, and anomaly identification, thereby having the potential to improve the quality of business entities' financial reports. This research aims to evaluate the influence of Artificial Intelligence implementation on the quality of financial reports in manufacturing companies by applying the Systematic Literature Review (SLR) method. Data was collected from leading scientific publications indexed in Scopus, Web of Science, ScienceDirect, as well as national journals, with a research timeframe from 2015 to 2025. The literature selection process adopted the PRISMA guidelines, resulting in 16 articles meeting the inclusion criteria. The research findings indicate that AI implementation has a positive impact on the aspects of relevance, reliability, comparability, and understandability of financial reports. This technology enhances the efficiency, accuracy, timeliness, and transparency of reporting, while also supporting the early detection of errors or fraud. However, challenges such as data security, algorithmic bias, limitations in human resource competency, and high implementation costs remain obstacles to its adoption. This research also identifies research gaps related to domestic regulations, AI usage ethics, and long-term empirical measurement of financial reporting quality in Indonesia. Overall, the results confirm that Artificial Intelligence plays a strategic role in improving the quality and credibility of financial reports in manufacturing companies, provided it is supported by solid data governance and regulatory frameworks.
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