{"title":"传统和非传统借款人数据在预测金融合作社违约中的相关性","authors":"Silas Juma, David Mathuva","doi":"10.1016/j.jcom.2023.100202","DOIUrl":null,"url":null,"abstract":"<div><p>In this paper, we examine the relevance of both traditional and non-traditional data in predicting default in two financial co-operatives (co-ops) in Kenya. Using micro-level secondary data representing 1753 borrower data extracted from the co-op systems of the two sample financial co-ops from June 2018 to July 2019, random panel logistic regressions are performed. The results, which are performed at both disaggregated and aggregated levels for both traditional and non-traditional features, reveal that both sets of features are useful in predicting default in financial co-ops. More specifically, we find that traditional features such as a longer member duration, higher value of deposits, and higher outstanding loan amounts are associated with lower default. In the case of non-traditional features, we find that borrowers drawn from the top 5 centres exhibit higher default rates. The results further show that borrowers who visit co-op offices more often are less likely to default. We further establish that the predictive power of the models improves when both traditional and non-traditional features are incorporated. The results in this study provide useful insights to managers and leaders when seeking operational and loan management systems for co-ops.</p></div>","PeriodicalId":43876,"journal":{"name":"Journal of Co-operative Organization and Management","volume":"11 1","pages":"Article 100202"},"PeriodicalIF":2.2000,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The relevance of traditional and non-traditional borrower data in predicting default in financial co-operatives\",\"authors\":\"Silas Juma, David Mathuva\",\"doi\":\"10.1016/j.jcom.2023.100202\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>In this paper, we examine the relevance of both traditional and non-traditional data in predicting default in two financial co-operatives (co-ops) in Kenya. Using micro-level secondary data representing 1753 borrower data extracted from the co-op systems of the two sample financial co-ops from June 2018 to July 2019, random panel logistic regressions are performed. The results, which are performed at both disaggregated and aggregated levels for both traditional and non-traditional features, reveal that both sets of features are useful in predicting default in financial co-ops. More specifically, we find that traditional features such as a longer member duration, higher value of deposits, and higher outstanding loan amounts are associated with lower default. In the case of non-traditional features, we find that borrowers drawn from the top 5 centres exhibit higher default rates. The results further show that borrowers who visit co-op offices more often are less likely to default. We further establish that the predictive power of the models improves when both traditional and non-traditional features are incorporated. The results in this study provide useful insights to managers and leaders when seeking operational and loan management systems for co-ops.</p></div>\",\"PeriodicalId\":43876,\"journal\":{\"name\":\"Journal of Co-operative Organization and Management\",\"volume\":\"11 1\",\"pages\":\"Article 100202\"},\"PeriodicalIF\":2.2000,\"publicationDate\":\"2023-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Co-operative Organization and Management\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2213297X23000058\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"MANAGEMENT\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Co-operative Organization and Management","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2213297X23000058","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MANAGEMENT","Score":null,"Total":0}
The relevance of traditional and non-traditional borrower data in predicting default in financial co-operatives
In this paper, we examine the relevance of both traditional and non-traditional data in predicting default in two financial co-operatives (co-ops) in Kenya. Using micro-level secondary data representing 1753 borrower data extracted from the co-op systems of the two sample financial co-ops from June 2018 to July 2019, random panel logistic regressions are performed. The results, which are performed at both disaggregated and aggregated levels for both traditional and non-traditional features, reveal that both sets of features are useful in predicting default in financial co-ops. More specifically, we find that traditional features such as a longer member duration, higher value of deposits, and higher outstanding loan amounts are associated with lower default. In the case of non-traditional features, we find that borrowers drawn from the top 5 centres exhibit higher default rates. The results further show that borrowers who visit co-op offices more often are less likely to default. We further establish that the predictive power of the models improves when both traditional and non-traditional features are incorporated. The results in this study provide useful insights to managers and leaders when seeking operational and loan management systems for co-ops.