Artificial Intelligence, Ambiguity, and Assets: Securing Creditor Rights in Kenyan Insolvency

This article evaluates the impact of procedural ambiguities within Kenya's Insolvency Act, 2015 on the efficient realization of secured assets by creditors, and explores the potential of digital technologies, particularly Artificial Intelligence (AI), to mitigate these challenges. While the Act aimed to establish a clear, efficient, and equitable framework balancing debtor rehabilitation and predictable security realization, practical implementation is hindered by specific legal provisions and procedural ambiguities. This discrepancy creates adverse economic and financial consequences, including delays, diminished returns for secured creditors, increased lending risk, and erosion of confidence in the financial system. Drawing on a review of the Act, relevant case law, scholarly discourse, and comparative analysis with international insolvency frameworks, this study identifies key procedural ambiguities and systemic impediments. It employs the Creditors' Bargain Theory and the Rescue Theory to contextualize the tension between conflicting objectives within the Act. The findings highlight how ambiguities in areas such as moratoria, the need for court approval, and the lack of granular procedural rules, particularly in liquidation, contribute to judicial uncertainty and hinder efficient realization. The article argues that digital solutions and AI, leveraged for data analysis, process automation, and guided workflows, offer promising avenues to standardize procedures, enhance transparency, and reduce delays, drawing lessons from jurisdictions already integrating technology in insolvency. The study provides an empirically grounded understanding of the realities faced by secured creditors and recommends targeted legislative, judicial, and technological interventions to enhance the Act's effectiveness.