Application and development of survival analysis techniques to the credit lending process
Creators: name: Kelly, Joanne
Publisher: name: RMIT University
Year: 2024

description: The Credit and Banking industries have long used statistical methods to improve their lending techniques, enabling banks and financial institutions to bring a consistency and reliability to lending decisions, as well as allowing automation and simpler profit/loss forecasting. Models developed utilising past performance allow the prediction of 'likelihood to default' over the course of the loan. However, as well as determining 'if a customer is likely to default on their loan, there is increasing interest in determining 'when' the customer is likely to default, or modelling 'time to default'. The prediction of 'time to pay-out (early repayment)' of a loan would enable lenders to determine the likely return based on balance and estimated length of loan, enabling the decline of potentially unprofitable lending, or the adjustment of interest rates, length of term or loan amount, to ensure the decision to approve lending is financially viable, and that forecasted profits are accurate.<br><br>Survival analysis methods can be applied to the modelling of time to the occurrence of an event, such as default, or repayment of a loan. The advantage of survival analysis is that it deals specifically with censored data, seen a great deal in financial lending analysis. There is strong evidence to suggest that survival analysis techniques could be very useful in financial lending. In the lending context, there may often be more than one 'time to event' associated with each failure that need to be modelled by a separate time function. The developments of multi-stage models in survival analysis to deal with these problems would be of great benefit, and is one area I have explored using real industry data.<br><br>Upon analysing the current uses of survival analysis techniques, these ideas were extended to some of the processes mentioned above. Practical experiments were run to determine if.survival analysis techniques are competitive, or indeed superior to the current process. Initial analysis concentrated on customers applying for personal lending, focusing on the prediction of both default and early repayment using single stage techniques and then expanding to multi-stage modelling. These ideas were then applied to the recovery side of lending, which is of particular interest in the current climate.<br><br>The results indicate the survival analysis models provide a far more accurate prediction of loan lifetimes than traditional models, which, when incorporated with pricing models, provide more accurate profit forecasts over the lifetime of the loan. An extension to the results presented would be to develop a 'complete lending process' that incorporates the customers' likelihood to default, likelihood to pay-out early, profitability and probability of recovery of the debt. More powerful models could be produced by modifying the models using account behaviour information, giving lenders a complete picture of the true profit over the life of the loan. This is an area I am currently developing with the support and assistance of personnel from a large personal loans portfolio.<br><br>