Harnessing Artificial Intelligence in Detecting Accounting Fraud: A Case Study of Emerging Markets and Regulatory Challenges

Abstract

Financial fraud remains a critical challenge in emerging markets, undermining investor confidence and regulatory effectiveness. This study examines the application of artificial intelligence (AI) in detecting accounting fraud, focusing on its effectiveness, regulatory challenges, and strategic integration. Using a mixed-methods approach, the research combines qualitative content analysis with quantitative regression models to evaluate AI-driven fraud detection mechanisms. The findings reveal a strong correlation (r = 1.0, p < 0.0001) between AI adoption and fraud detection accuracy, with regression analysis confirming that every 1% increase in AI adoption improves fraud detection accuracy by 1%. AI-based fraud detection significantly outperforms traditional methods, reducing fraud detection time from six months to 1.5 months and enhancing accuracy to 85%. Despite these advantages, barriers such as weak regulatory frameworks, data scarcity, and high implementation costs hinder AI adoption in emerging markets. The study recommends strengthening regulatory policies, fostering AI literacy among financial professionals, and leveraging blockchain integration to enhance fraud detection efficiency. Future research should explore cost-effective AI solutions tailored for resource-constrained environments.