ITEM METADATA RECORD
Title: Neural network survival analysis for personal loan data
Authors: Baesens, Bart ×
Van Gestel, Tony
Stepanova, M
Vanthienen, Jan
Van den Poel, D #
Issue Date: 2005
Series Title: Journal of the Operational Research Society vol:56 issue:9 (Sept.) pages: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.
Description: \emph{Journal of the Operational Research Society}, vol. 59, nb. 9, 2005
ISSN: 0160-5682
Publication status: published
KU Leuven publication type: IT
Appears in Collections:ESAT - STADIUS, Stadius Centre for Dynamical Systems, Signal Processing and Data Analytics
Electrical Engineering - miscellaneous
Research Center for Management Informatics (LIRIS), Leuven
× corresponding author
# (joint) last author

Files in This Item:

There are no files associated with this item.

Request a copy

 




All items in Lirias are protected by copyright, with all rights reserved.

© Web of science