Estimating Default in Microfinance Institutions: A Model for Bad Planning, Unforeseen Circumstances, and Strategic Default

There have been several methods for estimating default in microfinance institutions; however, these methods often overlook key underlying factors that can influence default. This paper seeks to add to knowledge by developing a default model that incorporates three distinct categories: bad planning, unforeseen circumstances, and strategic default. We examine three key factors that significantly contribute to each of these distinct categories. We consider the following as bad planning: a lack of financial literacy, a lack of market research, and poor management of business operations. Unforeseen circumstances include economic downturns or recessions, natural disasters, and political instability. Finally, for strategic default, we look at loan deviation, high interest rates, and loan delay. To do this, we formulate a model that estimates default by thoroughly investigating these categories and exploring the factors that contribute to default within them. Specifically, we derive probability distributions for borrowers’ missing payments using these factors. In addition, we analyze the joint probability distributions of missing payments that occur when factors from different categories interact and set their respective thresholds to obtain the default model. The model’s effectiveness is then evaluated through simulations using R Studio. According to the findings of our simulation study with respect to the conditions set, unforeseen circumstances continually had the highest probability of default, suggesting the major influence of such events on loan repayment. Furthermore, the intersection of factors with high probabilities in individual risk categories led to increased default probabilities, though at a lower level than those observed in individual categories. We propose that lenders should have real-time updates of borrowers’ profiles by including these key factors as metrics and early follow-up with borrowers to avoid defaults.