Survival analysis of bank loans and credit risk prognosis - Marimo, Mercy (My University, 2015)
Standard survival analysis methods model lifetime data where cohorts are tracked from the point of origin, until the occurrence of an event. If more than one event occurs, a special model is chosen to handle competing risks. Moreover, if the events are defined such that most subjects are not susceptible to the event(s) of interest, standard survival methods may not be appropriate. This project is an application of survival analysis in a consumer credit context. The data used in this study was obtained from a major South African financial institution covering a five year observation period from April 2009 to March 2014. The aim of the project was to follow up on cohorts from the point where vehicle finance loans originated to either default or early settlement events and compare survival and logistic modeling methodologies. As evidenced by the empirical Kaplain Meier survival curve, the data typically had long term survivors with heavy censoring as at March 2014. Cause specific Cox regression models were fitted and an adjustment was made for each model, to accommodate a proportion p of long term survivors. The corresponding Cumulative Incidence Curves were calculated per model, to determine probabilities at a fixed horizon of 48 months. Given the complexity of the consumer credit lifetime data at hand, we investigated how logistic regression methods would compare. Logistic regression models were fitted per event type. The models were assessed for goodness of fit. Their ability to differentiate risk were determined using the model Gini Statistics. Model assessment results were satisfactory. Methodologies were compared for each event type using Receiver Operating Characteristic curves and area under the curves. The Results show that survival methods perform better than logistic regression methods when modelling lifetime data in the presence of competing risks and long term survivors.
A dissertation submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfilment of requirements for the degree of Master of Science. Johannesburg, 2015.
Keywords: Consumer credit.; Bank loans.; Credit.; Statistics.