Credit Risk Assessment Model of Supply Chain Finance Factoring for Non-Bank Financial Institutions in Phases of Pre-Financing and Post-Financing

Credit Risk Assessment Model of Supply Chain Finance Factoring for Non-Bank Financial Institutions in Phases of Pre-Financing and Post-Financing | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 17 October 2025 V1 Latest version Share on Credit Risk Assessment Model of Supply Chain Finance Factoring for Non-Bank Financial Institutions in Phases of Pre-Financing and Post-Financing Authors : Patrick Kuok Kun Chu 0000-0002-8395-8534 [email protected] , Mavis Junru Wu , and Philip Kin Fun Law Authors Info & Affiliations https://doi.org/10.22541/au.176070627.78596208/v1 194 views 94 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract This study constructs a credit risk indicator system and develops a dual-stage credit risk assessment framework tailored to the characteristics of factoring, addressing both pre-financing and post-financing phases. Based on transaction-level data from Company X in China, the research employs a systematic variable selection process using Least Absolute Shrinkage and Selection Operator (LASSO) with five-fold cross-validation and Recursive Feature Elimination with Cross-Validation (RFECV) methods to select key credit risk indicators encompassing financial, behavioral, and supply chain-related variables from both suppliers and buyers and their trade interactions. The final models are developed using logistic regression to ensure interpretability and practical usability. The empirical results reveal that in the pre-financing phase, buyer credit score, financial leverage, and supply chain concentration are key risk indicators. In the post-financing phase, overdue behavior and transactional irregularities become dominant risk signals. This study contributes to both academic and practical aspects by providing a simplified yet effective credit risk modeling approach for non-bank financial institutions. Supplementary Material File (credit risk assessment model of supply chain finance factoring - with title page.docx) Download 877.78 KB Information & Authors Information Version history V1 Version 1 17 October 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords credit risk factoring factoring risk model non-bank financial institutions risk prediction supply chain finance Authors Affiliations Patrick Kuok Kun Chu 0000-0002-8395-8534 [email protected] University of Macau Faculty of Business Administration View all articles by this author Mavis Junru Wu University of Macau Faculty of Business Administration View all articles by this author Philip Kin Fun Law University of Macau Faculty of Business Administration View all articles by this author Metrics & Citations Metrics Article Usage 194 views 94 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Patrick Kuok Kun Chu, Mavis Junru Wu, Philip Kin Fun Law. Credit Risk Assessment Model of Supply Chain Finance Factoring for Non-Bank Financial Institutions in Phases of Pre-Financing and Post-Financing. Authorea . 17 October 2025. DOI: https://doi.org/10.22541/au.176070627.78596208/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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