Neural network survival analysis for personal loan data
B Baesens (),
T Van Gestel,
Dirk Van den Poel () and
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B Baesens: University of Southampton
T Van Gestel: Credit Methodology, Global Market Risk, Dexia Group
M Stepanova: UBS AG, Financial Services Group
J Vanthienen: KU Leuven, DTEW
Journal of the Operational Research Society, 2005, vol. 56, issue 9, 1089-1098
Abstract Traditionally, credit scoring aimed at distinguishing good payers from bad payers at the time of the application. The timing when customers default is also interesting to investigate since it can provide the bank with the ability to do profit scoring. Analysing when customers default is typically tackled using survival analysis. In this paper, we discuss and contrast statistical and neural network approaches for survival analysis. Compared to the proportional hazards model, neural networks may offer an interesting alternative because of their universal approximation property and the fact that no baseline hazard assumption is needed. Several neural network survival analysis models are discussed and evaluated according to their way of dealing with censored observations, time-varying inputs, the monotonicity of the generated survival curves and their scalability. In the experimental part, we contrast the performance of a neural network survival analysis model with that of the proportional hazards model for predicting both loan default and early repayment using data from a UK financial institution.
Keywords: credit scoring; survival analysis; neural networks (search for similar items in EconPapers)
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Working Paper: Neural Network Survival Analysis for Personal Loan Data (2004)
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