A latent class Cox model for heterogeneous time-to-event data

Credit risk plays a vital role in the era of digital finance and it is one of primary interests to identify customers with similar types of risk categories so that personalized financial services can be offered accordingly. Motivated by the bourgeoning need for default risk modeling in finance, we propose herein a latent class Cox model for heterogeneous time-to-event data. The proposed model naturally extends the Cox proportional hazards model to flexibly take into account the heterogeneity of covariate effects as often manifested in real data. Without a priori specification of the number of latent classes, it simultaneously incorporates the commonalities and disparities of individual customers’ risk behaviors and provides a more refined modeling technique than existing approaches. We further propose a penalized maximum likelihood approach to identify the number of latent classes and estimate the model parameters. A modified expectation–maximization algorithm is then developed for its numerical implementation. Simulation studies are conducted to assess the finite-sample performance of the proposed approach. Its illustration with a real credit card data set is also provided.