Shining a Light on Credit Risk: A Firefly Algorithm-Enhanced SVM for Supply Chain Finance

This chapter explores how a firefly algorithm–modified support vector machine (FA-SVM) can enhance credit risk assessment within the rapidly evolving context of supply chain finance. Traditional credit evaluation methods, which rely on static indicators and fragmented data, often fail to capture the multifaceted nature of SCF— particularly for SMEs operating in volatile sectors like Agriculture 4.0 . By contrast, FA-SVM integrates swarm-based parameter tuning with SVM’s robust classification capabilities, enabling more accurate and adaptive risk modelling. This approach proves especially useful in addressing imbalanced datasets, where distressed borrowers constitute a minority class. Through fine-tuned hyperparameters, FA-SVM reduces false negatives, thereby mitigating default risks for lenders. In parallel, digital supply chain finance innovations are redefining how credit is extended and managed. Agriculture 4.0 technologies, such as precision farming and real-time monitoring, generate detailed operational data that can significantly improve risk profiling but also introduce complexities related to data privacy and integration costs. The chapter demonstrates that these digital transformations can boost speed and accuracy of lending decisions when integrated with advanced machine learning models. However, it also underscores the importance of robust governance frameworks to address algorithmic bias, data security, and regulatory compliance, ensuring that automated credit decisions remain transparent and equitable.