IMPACT OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING ON ACCOUNTING PRACTICES AND FINANCIAL REPORTING ACCURACY
Machine learning and artificial intelligence technology injection into accounting processes is a paradigm shift and not an add-on technology upgrade. The review below considers how AI-based solutions are revolutionizing the quality of financial reporting, process automation, and professional judgment in accounting. After a review of eighteen studies and business deployments, the study discovers that machine learning activities have a potent influence on financial information accuracy and erasure of human mistake in repetitive functions. The transition, however, extends beyond the efficiency gain to include strategic influences on organizational performance and competitive advantage. The review identifies several important conclusions: AI systems are particularly well-suited to detection of anomalies and pattern recognition, and better fraud detection and quality financial reporting. But there are still implementation challenges, notably those of regulatory compliance, data quality expectation, and the "black box" challenge in which algorithmic decision-making is opaque. The findings are that effective adoption of AI is dependent on thoughtful change management practices and synergistic collaboration that augments rather than replaces human intelligence. Companies employing AI accounting systems benefit from enhanced real-time financial insight, accelerated decision-making capabilities, and diminished operational costs. Yet the practice still faces challenges integrating education models, professional standards, and regulatory framework implications with these emerging technologies. In its view, AI can transform accountancy professionals' work, shifting more emphasis from data processing to strategic analysis and advisory services.
Institutions
- Pacific University Oregon (US)
- Pacific Dental College and Hospital (IN)
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2025-10-13
- DOI
- https://doi.org/10.5281/zenodo.17340447
- Primary Topic
- Financial Reporting and XBRL
- Type
- article
- Field-Weighted Citation Impact
- 0.00