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

IF 2.2 Q3 MANAGEMENT
Silas Juma, David Mathuva
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引用次数: 0

摘要

在本文中,我们研究了传统和非传统数据在预测肯尼亚两家金融合作社(合作社)违约中的相关性。利用2018年6月至2019年7月从两个样本金融合作社的合作社系统中提取的代表1753名借款人数据的微观二级数据,进行随机面板logistic回归。在传统和非传统特征的分解和汇总水平上进行的结果表明,这两组特征在预测金融合作社违约方面都很有用。更具体地说,我们发现,较长的成员存续期、较高的存款价值和较高的未偿还贷款金额等传统特征与较低的违约率有关。在非传统特征的情况下,我们发现来自前5大中心的借款人表现出更高的违约率。研究结果进一步表明,经常光顾合作公寓的借款人违约的可能性更小。我们进一步证实,当传统特征和非传统特征都被纳入时,模型的预测能力有所提高。本研究的结果为寻求合作经营和贷款管理系统的管理者和领导者提供了有用的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.

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来源期刊
CiteScore
4.40
自引率
19.00%
发文量
27
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