CLOUD-BASED AI IN ACCOUNTING: INTEGRATING SCALABLE SOLUTIONS FOR SMES IN RWANDA

This study investigates the transformative impact of machine learning (ML) on auditing practices in Rwanda, focusing on fraud detection and compliance automation. Using a mixed-methods approach, quantitative data were collected via surveys of auditing professionals, while qualitative insights were obtained through interviews with stakeholders. The study found that ML adoption increased from 15% in 2020 to 75% in 2024, resulting in a significant improvement in fraud detection accuracy from 65% to 93%. Regression analysis confirmed that a 1% increase in ML adoption correlated with a 1.25% rise in fraud detection accuracy (r=0.96, p<0.01). Compliance issue resolution times dropped from 15 days to 5 days, and audit costs were reduced by 40%. Neural networks emerged as the most effective algorithm, used by 35% of firms. Despite challenges such as technical skill gaps (55%) and integration barriers (50%), the study concludes that ML has revolutionized auditing by enhancing efficiency and accuracy. Recommendations include technical training, financial incentives, tailored algorithms, updated regulations, and cross-sector collaboration.