人工神经网络
计算机科学
贷款
比例危险模型
生存分析
计量经济学
精算学
违约
人工智能
经济
统计
财务
数学
作者
Bart Baesens,Tony Van Gestel,Maria Stepanova,Dirk Van den Poel,Jan Vanthienen
标识
DOI:10.1057/palgrave.jors.2601990
摘要
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.
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