THE IMPACT OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN FORENSIC ACCOUNTING FOR FRAUD DETECTION IN RWANDA

This study investigates the impact of Artificial Intelligence (AI) and Machine Learning (ML) in forensic accounting for fraud detection in Rwanda, focusing on their effectiveness, challenges, and contributions to financial transparency and corporate governance. A mixed-methods approach was employed, integrating structured surveys, in-depth interviews, and statistical analysis. Key findings indicate that AI and ML significantly enhance fraud detection accuracy, increasing from 23.33% in 2020 to 66.67% in 2024, while reducing detection time from 6 months to 2.5 months. Statistical tests revealed a strong correlation (r = 0.91, p < 0.001) between AI adoption and fraud detection efficiency, with a chi-square value of 94.13 (p = 1.74e-19) confirming a significant shift from traditional methods. Regression analysis further demonstrated AI’s role in corporate governance, showing a strong positive relationship (R² = 0.78, p < 0.001) between AI adoption and financial transparency improvements. Despite these advancements, challenges such as skill shortages, high implementation costs, and regulatory gaps persist. The study recommends targeted AI training programs, hybrid AI-human fraud detection models, strengthened regulatory frameworks, cost-effective AI adoption strategies, and awareness initiatives to facilitate AI integration in forensic accounting.