Halal Trust Score sebagai Inovasi Penilaian Pembiayaan Syariah: Sebuah Prototipe pada Halal Value Chain berbasis Blockchain - Pradananta, Galih; Dana, Badara Shofi (Jurnal Dinamika Ekonomi Pembangunan, 2025)
The global halal industry is undergoing a paradigm shift from voluntary compliance to mandatory state-enforced assurance, particularly in Indonesia, where the halal food sector is a critical pillar of the national economy. However, the current halal certification regime remains predominantly product-centric and relies on static, document-based verification. This creates a significant information asymmetry for Islamic Financial Institutions (IFIs) when assessing the creditworthiness and Sharia compliance of Micro, Small, and Medium Enterprises (MSMEs). This research proposes the "Halal Trust Score," a novel digital artifact and credit risk metric derived from a blockchain-based traceability system. Adopting the Design Science Research Methodology (DSRM), we architected and prototyped a permissioned blockchain network using Hyperledger Fabric. The system digitizes the halal value chain, from the Slaughterhouse (RPH) to the final retailer, capturing real-time behavioral data through QR code interactions. A bespoke smart contract algorithm aggregates this data into a dynamic Trust Score based on four weighted indicators: Certificate Activity (20%), Input Scan Frequency (30%), Output Publication Frequency (30%), and Chain Interaction Velocity (20%). Simulation results demonstrate that this scoring mechanism effectively translates qualitative supply chain behaviors into quantitative financial metrics, serving as a reliable proxy for the "Character" and "Capacity" elements of credit risk. By reducing information opacity, the Halal Trust Score not only facilitates more efficient, data-driven financing decisions for IFIs but also creates a mimetic and normative institutional pressure for MSMEs to maintain high integrity standards. This study contributes to the literature on Islamic Fintech by providing a validated framework for integrating real-sector behavioral data into monetary-sector risk assessment.