Analysis of mortgage insurance data by mixture models

Purpose: This paper demonstrates how mixture survival models can be applied to analyse mortgage insurance data that include default-prone and default-free loans, assess risk factors, and predict default rate. Originality: Although with proven advantages, mixture survival models have not previously been applied to mortgage insurance or other general insurance products with large numbers of default-free policies. Key literature / theoretical perspective: Mixture models have the flexibility of isolating default-free policies from the estimation of the survival function for the default-prone policies. Design/methodology/approach: We provide examples to identify and analyse the effects of two commonly used risk factors using the likelihood-ratio test and improper proportional hazard (PH) models. Moreover, given a set of plausible parametric models, we show how to select the best one based on the goodness of fit and model complexity. Findings: After applying both parametric and non-parametric estimation methods, we propose a Weibull mixture model for the survival function for default-prone policies. Research limitations/implications: The methodology applied in this research is ready to be extended to any other credit risk modelling. Practical and Social implications: Mortgage default is a crucial issue in assessing financial and insurance risks. It is well known that a large scale of mortgage defaults was the root of the sub-prime loan problems and the subsequent global financial crisis.