AI-Driven Harmonisation of Mudarabah Models:

Operational Efficiency, Risk Management, and Global Integration in Islamic Finance

Aya Nazier Arabiyat

 

Abstract: This article examines the role of Artificial Intelligence (AI) in enhancing the operational viability of the Mudarabah model within Islamic banking. While Mudarabah is theoretically positioned as a cornerstone of profit-and-loss sharing in Islamic finance, its practical implementation has been constrained by persistent challenges, including information asymmetry, weak monitoring mechanisms, limited risk assessment tools, and the reliance of institutions on conventional safeguards. Adopting a qualitative analytical literature review approach, this article synthesises key contributions from Islamic finance and financial technology scholarship to develop an integrated analytical framework for evaluating AI-driven interventions in Mudarabah operations. The analysis demonstrates that specific AI applications, such as blockchain-based recordkeeping, machine learning–driven risk assessment, predictive analytics for project monitoring, and smart contracts for governance enforcement, can address structural weaknesses inherent in traditional Mudarabah practices. Rather than presenting AI as a generic technological solution, the article analytically maps distinct AI tools to identified operational, governance, and risk-related challenges in Mudarabah-based banking. The findings suggest that AI-enabled harmonisation of Mudarabah models enhances institutional efficiency, reduces agency risks, and supports scalable implementation across jurisdictions, thereby strengthening Islamic banking’s contribution to sustainable economic development.