Credit Risk Management in Factoring and Supply Chain Finance: A Bibliometric Analysis of Scientific Publications

Factoring and supply chain finance (SCF) allow firms, particularly small and medium-sized enterprises, to obtain liquidity against receivables and other trade assets, but they expose financiers to the credit risk of suppliers, buyers and their relationships. This study maps the scientific literature on credit risk management in factoring and SCF through a bibliometric analysis. A Scopus search conducted on 12 October 2026 and screening following the PRISMA 2020 flow yielded 288 articles and reviews published between 1995 and 2025. Performance analysis was combined with science mapping in VOSviewer, including keyword co-occurrence, country co-authorship and co-citation analyses of references and sources. The results show that the field has grown rapidly since 2019, with 87.2% of the documents published in 2019–2025. China dominates research output, while studies from the United Kingdom, the United States and Italy attain higher normalised impact, and operations management and operational research journals are the main publication venues. Three research streams are identified: analytical operations–finance models of risk allocation in capital-constrained supply chains, conceptual and empirical research on SCF adoption and reverse factoring, and machine learning-based credit risk assessment of SMEs. Research attention has shifted from traditional factoring and receivables financing towards SCF, machine learning, blockchain and, most recently, artificial intelligence and sustainability. The accounting, financial reporting and internal control dimensions of these arrangements, the allocation of risk among multiple financiers, and evidence from under-represented regions emerge as the main gaps.