{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,20]],"date-time":"2026-06-20T03:48:24Z","timestamp":1781927304736,"version":"3.54.5"},"reference-count":77,"publisher":"Emerald","issue":"2","license":[{"start":{"date-parts":[[2022,11,16]],"date-time":"2022-11-16T00:00:00Z","timestamp":1668556800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IMDS"],"published-print":{"date-parts":[[2023,2,27]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>Monitoring corporate credit risk (CCR) has traditionally relied on such indicators as income, debt and inventory at a company level. These data are usually released on a quarterly or annual basis by the target company and include, exclusively, the financial data of the target company. As a result of this exclusiveness, the models for monitoring credit risk usually fail to account for some significant information from different sources or channels, like the data of its supply chain partner companies and other closely relevant data yet available from public networks, and it is these seldom used data that can help unveil the immediate CCR changes and how the risk is being propagated along the supply chain. This study aims to discuss the a forementioned issues.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>Going beyond the existing CCR prediction data, this study intends to address the impact of supply chain data and network activity data on CCR prediction, by integrating machine learning technology into the prediction to verify whether adding new data can improve the predictability.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>The results show that the predictive errors of the datasets after adding supply chain data and network activity data to them are made the ever least. Moreover, intelligent algorithms like support vector machine (SVM), compared to traditionally used methods, are better at processing nonlinear datasets and mining complex relationships between multi-variable indicators for CCR evaluation.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>This study indicates that bringing in more information of multiple data sources combined with intelligent algorithms can help companies prevent risk spillovers in the supply chain from causing harm to the company, and, as well, help customers evaluate the creditworthiness of the entity to lessen the risk of their investment.<\/jats:p><\/jats:sec>","DOI":"10.1108\/imds-02-2022-0091","type":"journal-article","created":{"date-parts":[[2022,11,15]],"date-time":"2022-11-15T22:15:18Z","timestamp":1668550518000},"page":"434-450","source":"Crossref","is-referenced-by-count":13,"title":["Monitoring corporate credit risk with multiple data sources"],"prefix":"10.1108","volume":"123","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7070-5255","authenticated-orcid":false,"given":"Du","family":"Ni","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0809-9431","authenticated-orcid":false,"given":"Ming K.","family":"Lim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xingzhi","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingchi","family":"Qu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2201-4510","authenticated-orcid":false,"given":"Mei","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"140","published-online":{"date-parts":[[2022,11,16]]},"reference":[{"key":"key2023100210211893900_ref001","article-title":"Credit shock propagation along supply chains: evidence from the CDS market","year":"2021","journal-title":"Management Science"},{"key":"key2023100210211893900_ref002","first-page":"219","volume-title":"Artificial Intelligence for Sustainable Development: Theory, Practice and Future Applications","year":"2021"},{"key":"key2023100210211893900_ref003","volume-title":"Fintech in Financial Inclusion: Machine Learning Applications in Assessing Credit Risk","year":"2019"},{"key":"key2023100210211893900_ref006","first-page":"66","article-title":"Ordinary least squares regression method approach for site selection of automated teller machines (ATMs)","volume-title":"Procedia Environmental Sciences","year":"2015"},{"issue":"5","key":"key2023100210211893900_ref007","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.1287\/mnsc.2015.2403","article-title":"Managerial ability and credit risk assessment","volume":"63","year":"2017","journal-title":"Management Science"},{"issue":"5","key":"key2023100210211893900_ref008","article-title":"Utilizing and adapting the Delphi method for use in qualitative research","volume":"14","year":"2015","journal-title":"International Journal of Qualitative Methods"},{"key":"key2023100210211893900_ref009","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.neucom.2019.10.118","article-title":"A comprehensive survey on support vector machine classification: applications, challenges and trends","volume":"408","year":"2020","journal-title":"Neurocomputing"},{"issue":"3","key":"key2023100210211893900_ref010","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1961189.1961199","article-title":"LIBSVM: a library for support vector machines","volume":"2","year":"2011","journal-title":"ACM Transactions on Intelligent Systems and Technology"},{"issue":"1","key":"key2023100210211893900_ref011","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10462-015-9434-x","article-title":"Financial credit risk assessment: a recent review","volume":"45","year":"2016","journal-title":"Artificial Intelligence Review"},{"key":"key2023100210211893900_ref012","doi-asserted-by":"crossref","first-page":"155353","DOI":"10.1109\/ACCESS.2021.3123090","article-title":"An efficient SVM-based feature selection model for cancer classification using high-dimensional microarray data","volume":"9","year":"2021","journal-title":"IEEE Access"},{"key":"key2023100210211893900_ref013","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.dss.2017.08.001","article-title":"Solvency prediction for small and medium enterprises in banking","volume":"102","year":"2017","journal-title":"Decision Support Systems"},{"issue":"13","key":"key2023100210211893900_ref015","doi-asserted-by":"crossref","first-page":"5737","DOI":"10.1016\/j.eswa.2015.02.042","article-title":"Enhancing accuracy and interpretability of ensemble strategies in credit risk assessment. 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