Predictive Halal Assurance: Utilizing Machine Learning for Real-Time Fraud Detection and Risk Mitigation in Global Food Supply Chains

Abstract

The traditional method of halal auditing is mostly retrospective, focusing on past documentation and frequent physical checks. That renders it exposed to the risk of transient contamination or intentional deception in an era of rapid worldwide trade and difficult sourcing of ingredients. In this research we suggest a conceptual change from static certification to predictive halal assurance using Machine Learning (ML) approaches. The proposed ML system evaluates past supply chain data, border refusal records, ingredient pricing anomalies and geopolitical upheavals to foresee and detect highrisk regions in the halal logistics chain before contamination happens. The study is based on a qualitative conceptual framework that integrates the existing literature on predictive food safety systems and modifies it to fit within rigorous shariah compliant frameworks. The findings reveal that with predictive AI, authorities may move from reactive enforcement to proactive prevention in halal governance. This tech-driven monitoring greatly improves the durability of the global halal ecosystem, and protects the sanctity of the ummah’s consumption in a digitalised civilization.

Keywords: Predictive Halal Assurance, Machine Learning (ML), Supply Chain Fraud, Halal Governance, Risk Mitigation, Shariah Compliance, Digitalized Civilization