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.