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A time-dependent proportional hazards survival model for credit risk analysis
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Journal Article
A time-dependent proportional hazards survival model for credit risk analysis
2012
Overview
In the consumer credit industry, assessment of default risk is critically important for the financial health of both the lender and the borrower. Methods for predicting risk for an applicant using credit bureau and application data, typically based on logistic regression or survival analysis, are universally employed by credit card companies. Because of the manner in which the predictive models are fit using large historical sets of existing customer data that extend over many years, default trends, anomalies, and other temporal phenomena that result from dynamic economic conditions are not brought to light. We introduce a modification of the proportional hazards survival model that includes a time-dependency mechanism for capturing temporal phenomena, and we develop a maximum likelihood algorithm for fitting the model. Using a very large, real data set, we demonstrate that incorporating the time dependency can provide more accurate risk scoring, as well as important insight into dynamic market effects that can inform and enhance related decision making.
Publisher
Taylor & Francis,Palgrave Macmillan,Palgrave Macmillan UK,Taylor & Francis Ltd
Subject
/ data analysis: estimating survival models
/ Datasets
/ Default
/ Exact sciences and technology
/ failure models: survival analysis for credit risk modelling
/ Linear inference, regression
/ Modeling
/ Operational research and scientific management
/ Operational research. Management science
/ Operations Research/Decision Theory
/ Profits
/ Reliability, life testing, quality control
/ Risk theory. Actuarial science
/ risk: predicting consumer credit risk
/ Sciences and techniques of general use
/ Studies
