The Impact of Machine Learning on Auditing: Automating Fraud Detection and Compliance (a Study in Rwanda)
Year: 2024
Type: article
Abstract: 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.
Author: Mbonigaba Celestin
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6931160171
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