Institutionalizing Artificial Intelligence within Indonesia’s Zakat Architecture: Navigating Regulatory Challenges and Sharia Compliant Implementation

Abstract

This study examines how zakat management, supported by artificial intelligence and big data, can be optimized within the context of Islamic finance in Indonesia to address persistent poverty and the gap between zakat potential and actual collection (Purpose). Using a qualitative approach, this research is based on a literature review and case analysis focusing on the application of artificial intelligence, big data analytics, and digital governance mechanisms in zakat management practices in Indonesia’s Islamic finance institutions (Methodology). The findings indicate that artificial intelligence and big data enhance zakat management by enabling data-driven decision making, predictive analytics, automated beneficiary verification, and real-time reporting, which improve collection performance, targeting accuracy, operational efficiency, and transparency in Islamic finance–based zakat institutions (Results). From a theoretical perspective, this study contributes to zakat management and Islamic finance literature by integrating artificial intelligence, big data, and sharia compliance into governance, accountability, and ethical decision-making frameworks (Theoretical implication). Practically, the study shows that zakat institutions can leverage artificial intelligence and big data to strengthen public trust and governance in zakat management, while addressing challenges related to regulation, data privacy, cybersecurity, algorithmic bias, implementation costs, and sharia compliance through instruments such as blockchain (Practical implication).