ArticleScientific reports2026
Blockchain-enabled supply chain finance risk intelligent assessment and trust mechanism construction.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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2 authors.
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Abstract
Supply Chain Finance (SCF) enhances financial liquidity, optimizes operational efficiency, and strengthens collaboration among supply chain stakeholders. Traditional SCF systems are vulnerable to economic risks, fraud, and a lack of transparency due to centralized data control and limited real-time monitoring capabilities. Current systems rely on slow central financial bodies and static credit evaluations, lacking dynamic risk assessment and mechanisms to build stakeholder trust. This research proposes Bi-MATRA, a Blockchain-Integrated Multi-Agent Trust and Risk Assessment system. The goal is to create a decentralized, intelligent risk evaluation system for real-time trust computation and automated decision-making in SCF contexts. Bi-MATRA combines blockchain's immutability and transparency with a multi-agent system that automatically monitors and assesses risk. Bi-MATRA uses an adapted Eigen Trust algorithm on the blockchain to calculate trust. Agents rate each other based on interaction history, and transitive trust linkages determine global trust ratings. Smart contracts for dynamic trust-based decision-making update these scores. Smart contracts enforce predefined financial agreements, automating processes and reducing the need for intermediaries. Agent-based modelling was employed to develop a blockchain-based simulation testbed for assessing Bi-MATRA's responsiveness, accuracy, and resilience. The framework was more efficient at trust validation and risk identification than typical SCF systems. Key studies reveal that Bi-MATRA saves transaction clearance time by 35% and improves early risk detection by 28%. EigenTrust-based trust computation builds agent trust and accountability, enhancing financial interactions. In conclusion, the Bi-MATRA framework offers a scalable, decentralized approach to intelligent risk assessment and trust-building in blockchain-enabled supply chain finance systems.
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