| Summary: | The event history analysis of debt portfolios concerns the repayment behaviours of accounts under the management of a debt collection team. Typically, an account can experience a series of events throughout the course of the debt recovery process, such as payment commencement, missing a payment, settlement, etc. In this thesis, we aim to provide a new perspective of modelling the evolution of the process and evaluating the collection performance using various statistical techniques in survival analysis and event history analysis. In the first three chapters, we describe the consumer debt purchase and collection industry, explore the data sets and review the related statistical methods to be used in the thesis. In Chapter 4, we investigate the directly settled accounts, which form a special group of accounts settled at the beginning of the recovery process. The time until commencement of repayment, which is considered as an important indicator for accounts’ repayment performance, is studied extensively in Chapter 5 using a number of survival analysis techniques. For accounts that have started to make monthly repayments, missing a payment is an interesting event to investigate. As such events may occur more than once, a specially structured multi-state model is explored in Chapter 6. Performance covariates are also introduced to the modelling procedure to reflect the series of historical events experienced. With an increased number of covariates, a tailored model selection procedure is proposed to achieve improved interpretability of regression results.
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