AI-Based Credit Risk Assessment and Intelligent Matching Mechanism in Supply Chain Finance

Abstract

In the context of increasing complexity and globalization of supply chain finance (SCF), traditional credit risk assessment and loan matching mechanisms face significant challenges, including inefficiency, information asymmetry, and high default risks. These limitations hinder the development of inclusive financing, especially for small and medium-sized enterprises (SMEs) that often lack strong credit histories or tangible collateral. Recent advances in artificial intelligence (AI) and machine learning provide new opportunities to address these challenges through data-driven, adaptive, and scalable solutions. This paper proposes a novel AI-driven framework that integrates credit risk assessment with an intelligent matching mechanism tailored for SCF environments. First, we construct a credit scoring model that leverages structured and unstructured data from multiple sources, including financial statements, transactional behavior, supplier-buyer relationships, and logistics data. Using advanced machine learning techniques such as gradient boosting (e.g., XGBoost, LightGBM) and deep learning architectures (e.g., BiLSTM), we are able to capture nonlinear patterns and dynamic credit signals that traditional statistical models fail to detect. Key features such as payment cycles, cash flow volatility, and upstream/downstream stability are engineered and weighted using explainable AI (XAI) methods to ensure transparency and interpretability. Second, we introduce an intelligent loan matching mechanism based on multi-objective optimization, incorporating credit risk levels, financing costs, enterprise profiles, and lender preferences. By applying techniques such as reinforcement learning and genetic algorithms, the matching engine dynamically aligns borrowers with optimal lenders, reducing mismatches and lowering transaction friction in the SCF ecosystem. Experimental validation is conducted using real-world data collected from a digital SCF platform covering over 5,000 SME borrowers and 300 financial institutions. The proposed AI model achieves a high level of predictive performance (AUC > 0.90, F1-score > 0.85), significantly outperforming baseline logistic regression and rule-based methods. Moreover, the intelligent matching mechanism increases the loan approval success rate by 37% and reduces processing time by 42% on average. Our findings demonstrate that AI technologies can significantly improve both the accuracy and efficiency of SCF operations. The integrated system not only enhances credit risk control but also facilitates intelligent, automated, and inclusive financial services. This work offers practical implications for FinTech companies, supply chain platforms, and policy makers aiming to strengthen SME financing infrastructure in complex global supply chains.