A Quantitative Assessment of Generative AI Applications in Public-Sector Financial Reporting and Audit Preparation

Abstract

This study quantitatively assessed the influence of generative artificial intelligence applications on public-sector financial reporting and audit preparation systems within governmental institutions. The research examined the extent to which AI adoption, technological readiness, AI utilization frequency, and organizational support affected financial reporting accuracy, audit preparation efficiency, fraud detection capability, compliance verification effectiveness, administrative productivity, and transparency outcomes across public-sector accounting environments. A quantitative cross-sectional research design was employed using structured survey questionnaires distributed electronically to accountants, auditors, finance officers, procurement personnel, compliance specialists, and financial administrators working within ministries, municipalities, taxation agencies, healthcare institutions, and public audit authorities. A total of 420 questionnaires were distributed, and 389 valid responses were retained after data screening, producing a response rate of 92.6%. Statistical analyses were conducted using SPSS and SmartPLS software packages through descriptive statistics, correlation analysis, regression analysis, and structural equation modeling procedures. The findings demonstrated strong positive relationships between generative artificial intelligence adoption and operational performance indicators within governmental accounting systems. Financial reporting accuracy recorded the strongest correlation with AI adoption (r = 0.768), followed by administrative productivity (r = 0.754), audit preparation efficiency (r = 0.741), and compliance verification effectiveness (r = 0.719). Regression analysis further revealed that AI adoption significantly predicted financial reporting accuracy (β = 0.482, p = 0.000), while AI utilization frequency significantly influenced audit preparation efficiency (β = 0.451, p = 0.000) and administrative productivity (β = 0.463, p = 0.000). The coefficient of determination values showed that the statistical models explained 62.1% of the variance in financial reporting accuracy, 60.4% in administrative productivity, 58.7% in audit preparation efficiency, and 55.3% in fraud detection capability. Subgroup analysis additionally revealed that large institutions achieved a mean reporting accuracy score of 4.51 compared to 3.26 among smaller organizations. Institutions possessing advanced digital infrastructure demonstrated significantly higher transparency performance (mean = 4.51) than institutions operating with basic infrastructure systems (mean = 3.17). The study concluded that generative artificial intelligence substantially improved public-sector financial reporting quality, audit preparation effectiveness, fraud detection capability, compliance oversight, and administrative efficiency within governmental accounting environments.