Survival Modelling for Credit Risk Assessment in Microfinance Lending: Estimating Time-to-Default and Probability of Default

Abstract

This study investigates the application of survival analysis techniques to credit risk modelling in microfinance lending. Traditional approaches focus on modelling the probability of default at a fixed point in time, without accounting for the timing and evolving nature of borrower default behaviour. To address this limitation, this study applies three survival models: the Cox Proportional Hazards model, the Accelerated Failure Time (AFT) model, and the Mixture Cure Model (MCM). The Cox model is used to identify key factors of relative default risk, while the AFT model captures the timing of default through time-to-event relations. The Mixture Cure Model captures differences among borrowers by separating them into susceptible and non-susceptible borrowers, providing better estimate of default probabilities in lending business. Instead of treating these models as competing specifications, they are combined within a unified risk segmentation framework that evaluates probability of default, time to default, and hazard intensity. This enables borrowers to be grouped into distinct risk categories in a manner that is easy to interpret and statistically grounded. The findings show that behavioral characteristics such as repayment history significantly increase likelihood of default, while income-related characteristics are linked with extended survival times. In addition, the proposed segmentation framework reveals distinct differences in economic outcomes across different risk groups, underscoring its practical value for credit risk management in microfinance institutions.