Application of Artificial Intelligence in Detecting Creative Accounting Tendencies Among Corporations in Kenya

Abstract

The rapid advancement of Artificial Intelligence (AI) has transformed auditing and financial reporting, offering novel approaches to detecting creative accounting practices. This study investigates the effectiveness of AI in identifying financial irregularities among selected Kenyan corporations, comparing AI-driven techniques with traditional accrual-based models, including the Modified Jones Model and the Beneish M-Score. Using a mixed-methods design, secondary financial data spanning five years from four road transport companies were analyzed alongside qualitative insights from finance managers and internal auditors. AI techniques, including anomaly detection models and explainable AI tools, effectively identified complex, multi-dimensional patterns indicative of earnings manipulation, corroborating findings from traditional models in severe cases. The results demonstrate that AI complements classical detection methods by capturing non-linear relationships and emergent manipulative practices that conventional models may overlook. The study further highlights the importance of organizational readiness, auditor training, and regulatory frameworks to ensure the ethical and effective deployment of AI in financial reporting. Findings suggest that integrating AI into auditing practices can enhance accuracy, efficiency, and transparency, thereby strengthening corporate governance and stakeholder trust.