FROM DISTRIBUTION TO TRANSFORMATION: A CONCEPTUAL PREDICTIVE ANALYTICS FRAMEWORK FOR SUSTAINABLE POVERTY ALLEVIATION THROUGH ZAKAT

Abstract

Zakat represents one of the most institutionalized methods of Islamic social finance aimed at redistributing wealth and alleviating poverty. Despite increasing zakat collections across Muslim majority countries, evidence of sustainable poverty exit among recipients remains limited, as most zakat governance models emphasize distribution efficiency rather than long term socioeconomic transformation. This paper proposes a conceptual Poverty-to-Prosperity (P2P) Predictive Zakat Governance Framework that integrates predictive analytics with Islamic social finance principles to enhance targeting accuracy and intervention effectiveness. Using a conceptual development approach, the study synthesizes insights from poverty dynamics, capability theory, Maqasid al-Shariah, and data-driven governance to construct a multi-layered framework that classifies zakat recipients based on poverty risk trajectories and aligns assistance with differentiated intervention strategies. The framework introduces a probability-based stratification model supported by dashboard monitoring to strengthen institutional transparency and outcome-oriented decision making. The proposed framework contributes to the emerging literature on digital transformation in Islamic social finance by offering a structured approach for integrating predictive technologies into zakat management systems. Policy implications for data-driven zakat governance and future empirical validation are discussed.