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Survival analysis methods for personal loan data

Survival analysis methods for personal loan data
Survival analysis methods for personal loan data
Credit scoring is one of the most successful applications of quantitative analysis in business. This paper shows how using survival-analysis tools from reliability and maintenance modeling allows one to build credit-scoring models that assess aspects of profit as well as default. This survival-analysis approach is also finding favor in credit-risk modeling of bond prices. The paper looks at three extensions of Cox's proportional hazards model applied to personal loan data. A new way of coarse-classifying of characteristics using survival-analysis methods is proposed. Also, a number of diagnostic methods to check adequacy of the model fit are tested for suitability with loan data. Finally, including time-by-characteristic interactions is proposed as a way of possible improvement of the model's predictive power.
risk, estimating credit risk for personal loans, failure models, survival analysis applied to credit scoring models
0030-364X
277-289
Stepanova, Maria
d7e3c9d8-ce4f-4596-b94b-80438009c618
Thomas, Lyn C.
a3ce3068-328b-4bce-889f-965b0b9d2362
Stepanova, Maria
d7e3c9d8-ce4f-4596-b94b-80438009c618
Thomas, Lyn C.
a3ce3068-328b-4bce-889f-965b0b9d2362

Stepanova, Maria and Thomas, Lyn C. (2002) Survival analysis methods for personal loan data. Operations Research, 50 (2), 277-289. (doi:10.1287/opre.50.2.277.426).

Record type: Article

Abstract

Credit scoring is one of the most successful applications of quantitative analysis in business. This paper shows how using survival-analysis tools from reliability and maintenance modeling allows one to build credit-scoring models that assess aspects of profit as well as default. This survival-analysis approach is also finding favor in credit-risk modeling of bond prices. The paper looks at three extensions of Cox's proportional hazards model applied to personal loan data. A new way of coarse-classifying of characteristics using survival-analysis methods is proposed. Also, a number of diagnostic methods to check adequacy of the model fit are tested for suitability with loan data. Finally, including time-by-characteristic interactions is proposed as a way of possible improvement of the model's predictive power.

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More information

Published date: 2002
Keywords: risk, estimating credit risk for personal loans, failure models, survival analysis applied to credit scoring models

Identifiers

Local EPrints ID: 35666
URI: http://eprints.soton.ac.uk/id/eprint/35666
ISSN: 0030-364X
PURE UUID: 7e47e5b3-6802-4650-ba22-6f788bcf67eb

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Date deposited: 23 May 2006
Last modified: 15 Mar 2024 07:53

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Contributors

Author: Maria Stepanova
Author: Lyn C. Thomas

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