E. Altman, Marco Balzano, Alessandro Giannozzi, Stjepan Srhoj
{"title":"The Omega Score: An improved tool for SME default predictions","authors":"E. Altman, Marco Balzano, Alessandro Giannozzi, Stjepan Srhoj","doi":"10.1080/26437015.2023.2186284","DOIUrl":null,"url":null,"abstract":"ABSTRACT The Omega Score, a novel small and medium-sized enterprise (SME) default predictor developed by Altman et al. in 2022, combines indicators related to financial ratios, payment behavior, and management and employees variables that play an important role in predicting SME defaults. Built with machine-learning techniques and rich dataset information, the Omega Score can be used to categorize an SME into one of the following three groups: healthy, moderate-risk, and high-risk. The Omega Score can be utilized by financial institutions to reduce lending errors and minimize loan defaults, support policy makers in implementing effective restructuring policies, assist credit analytics firms in assessing creditworthiness, assist investors in allocating funds, and asset managers to support decision-making processes.","PeriodicalId":246224,"journal":{"name":"Journal of the International Council for Small Business","volume":"39 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the International Council for Small Business","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/26437015.2023.2186284","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
ABSTRACT The Omega Score, a novel small and medium-sized enterprise (SME) default predictor developed by Altman et al. in 2022, combines indicators related to financial ratios, payment behavior, and management and employees variables that play an important role in predicting SME defaults. Built with machine-learning techniques and rich dataset information, the Omega Score can be used to categorize an SME into one of the following three groups: healthy, moderate-risk, and high-risk. The Omega Score can be utilized by financial institutions to reduce lending errors and minimize loan defaults, support policy makers in implementing effective restructuring policies, assist credit analytics firms in assessing creditworthiness, assist investors in allocating funds, and asset managers to support decision-making processes.