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Neural Network Survival Analysis for Personal Loan Data

Neural Network Survival Analysis for Personal Loan Data
Neural Network Survival Analysis for Personal Loan Data
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, 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 U.K. financial institution.
credit scoring, survival analysis, neural networks
0160-5682
1089-1098
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Van Gestel, Tony
e917bd96-d291-4132-958b-e54cb1b9eaf9
Stepanova, Maria
d7e3c9d8-ce4f-4596-b94b-80438009c618
Van den Poel, D.
956e522c-3a91-4885-ac2d-4eee48b27353
Vanthienen, Jan
6f3d818f-0fce-46fa-966b-160e645caf6d
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Van Gestel, Tony
e917bd96-d291-4132-958b-e54cb1b9eaf9
Stepanova, Maria
d7e3c9d8-ce4f-4596-b94b-80438009c618
Van den Poel, D.
956e522c-3a91-4885-ac2d-4eee48b27353
Vanthienen, Jan
6f3d818f-0fce-46fa-966b-160e645caf6d

Baesens, Bart, Van Gestel, Tony, Stepanova, Maria, Van den Poel, D. and Vanthienen, Jan (2005) Neural Network Survival Analysis for Personal Loan Data. Journal of the Operational Research Society, 59 (9), 1089-1098. (doi:10.1057/palgrave.jors.2601990).

Record type: Article

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, 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 U.K. financial institution.

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More information

Published date: 2005
Keywords: credit scoring, survival analysis, neural networks

Identifiers

Local EPrints ID: 36752
URI: http://eprints.soton.ac.uk/id/eprint/36752
ISSN: 0160-5682
PURE UUID: 2e7cdefc-827f-468d-b020-eb5137dc112f
ORCID for Bart Baesens: ORCID iD orcid.org/0000-0002-5831-5668

Catalogue record

Date deposited: 23 May 2006
Last modified: 16 Mar 2024 03:39

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Contributors

Author: Bart Baesens ORCID iD
Author: Tony Van Gestel
Author: Maria Stepanova
Author: D. Van den Poel
Author: Jan Vanthienen

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