Modelling of Credit Risk: Random Forests versus Cox Proportional Hazard Regression

Dyana Kwamboka Mageto; Samuel Musili Mwalili; Anthony Gichuhi Waititu

doi:doi:10.11648/j.ajtas.20150404.13

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Modelling of Credit Risk: Random Forests versus Cox Proportional Hazard Regression

Dyana Kwamboka Mageto, Samuel Musili Mwalili, Anthony Gichuhi Waititu

Published in American Journal of Theoretical and Applied Statistics (Volume 4, Issue 4)

Received: 20 May 2015 Accepted: 26 May 2015 Published: 2 June 2015

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Abstract

In survival analysis several regression modeling strategies can be applied to predict the risk of future events. Often, however, the default choice of analysis tends to rely on Cox regression modeling due to its convenience. Extensions of the random forest approach to survival analysis provide an alternative way to build a risk prediction model. This paper discusses the two approaches in reference to credit management and compares the impact and results of both methods. The Cox Proportional Hazard model displayed a better performance than that of Random Survival Forest when estimating credit risk.

Published in	American Journal of Theoretical and Applied Statistics (Volume 4, Issue 4)
DOI	10.11648/j.ajtas.20150404.13
Page(s)	247-253
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2015. Published by Science Publishing Group

Keywords

Credit Risk, Random Forests, Survival Models

References

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[2]	Basel (2000). Principles for the Management of Credit Risk, Basel Commettee. September 2000 1-30.
[3]	Breiman, L. (1999). Using Adaptive Bagging to Debias Regression, Technocal report. 547, statistical Depertment UCB.
[4]	Creamer, G. (2012). Using Random Forests and Logistic Regression for Performance Prediction of Latin American ADRS and Banks. Journal of Centrum Cathedra, 24-36
[5]	Harell, F. E. Califf, R. M. Pryor, D. M. Lee, K. L. Rosati, R. A. Evaluating the Yield of Medical Tests. JAMA. 1982; 247: 2543-2587
[6]	Katten, M. Hess, K. and Beck, J. (1998) Experiments to Determine whether Reccursive Partittioning (CART) or an Artificial Neural Network Overcomes Theoretical Limitations of Cox PH Regression, Computer Biomedical Research, 363-373. IBM, Commuter Survey (2014).
[7]	May, M. Royston, P. Egger, M. Justice, A. C. and Sterne, J.A.C. (2004) Development and Validation of Prognosis Model for Survival Time Data: Application to Prognosis of HIV Positive Patients Treated with Anti-retroviral Therapy. Statistics Medicine, 23: 2373-2398.
[8]	Narain, B. 1992. Survival analysis and the credit granting decision. L. C. Thomas, J. N. Crook, D. B. Edelman, eds.Credit Scoring and Credit Control. OUP, Oxford, U.K., 109–121.
[9]	Njanike, K. (2009). The Impact of Effective Credit Risk Mangement on Bank Survival. Annals of the University of Petrosani Economics, 9(2), 173-184.
[10]	Wekesa O. (2012), Modelling Credit Risk for Personal Loans Using Product-Limit Estimator. International Journal of Financial Research. (3) 22-32.
[11]	Thomas, L. C. Banasik, J. N. Crook. (1999). Not if but when Loans Default. J.Operations Research Society. 50 1185-1190.
[12]	Zhang, A. (2009). Statistical Methods in Credit Risk Modelling, University of Michigan, 3 4-27.
[13]	Zhou, L. and Wang, H. (2012). Loan Default Prediction on Large Imbalanced Data Using Random Forests, Telkonmika Indonesian Journal of Electrical Engeneering. (10) 1519-1525.

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APA Style

Dyana Kwamboka Mageto, Samuel Musili Mwalili, Anthony Gichuhi Waititu. (2015). Modelling of Credit Risk: Random Forests versus Cox Proportional Hazard Regression. American Journal of Theoretical and Applied Statistics, 4(4), 247-253. https://doi.org/10.11648/j.ajtas.20150404.13

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ACS Style

Dyana Kwamboka Mageto; Samuel Musili Mwalili; Anthony Gichuhi Waititu. Modelling of Credit Risk: Random Forests versus Cox Proportional Hazard Regression. Am. J. Theor. Appl. Stat. 2015, 4(4), 247-253. doi: 10.11648/j.ajtas.20150404.13

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AMA Style

Dyana Kwamboka Mageto, Samuel Musili Mwalili, Anthony Gichuhi Waititu. Modelling of Credit Risk: Random Forests versus Cox Proportional Hazard Regression. Am J Theor Appl Stat. 2015;4(4):247-253. doi: 10.11648/j.ajtas.20150404.13

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@article{10.11648/j.ajtas.20150404.13,
  author = {Dyana Kwamboka Mageto and Samuel Musili Mwalili and Anthony Gichuhi Waititu},
  title = {Modelling of Credit Risk: Random Forests versus Cox Proportional Hazard Regression},
  journal = {American Journal of Theoretical and Applied Statistics},
  volume = {4},
  number = {4},
  pages = {247-253},
  doi = {10.11648/j.ajtas.20150404.13},
  url = {https://doi.org/10.11648/j.ajtas.20150404.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20150404.13},
  abstract = {In survival analysis several regression modeling strategies can be applied to predict the risk of future events. Often, however, the default choice of analysis tends to rely on Cox regression modeling due to its convenience. Extensions of the random forest approach to survival analysis provide an alternative way to build a risk prediction model. This paper discusses the two approaches in reference to credit management and compares the impact and results of both methods. The Cox Proportional Hazard model displayed a better performance than that of Random Survival Forest when estimating credit risk.},
 year = {2015}
}

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TY  - JOUR
T1  - Modelling of Credit Risk: Random Forests versus Cox Proportional Hazard Regression
AU  - Dyana Kwamboka Mageto
AU  - Samuel Musili Mwalili
AU  - Anthony Gichuhi Waititu
Y1  - 2015/06/02
PY  - 2015
N1  - https://doi.org/10.11648/j.ajtas.20150404.13
DO  - 10.11648/j.ajtas.20150404.13
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
SP  - 247
EP  - 253
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20150404.13
AB  - In survival analysis several regression modeling strategies can be applied to predict the risk of future events. Often, however, the default choice of analysis tends to rely on Cox regression modeling due to its convenience. Extensions of the random forest approach to survival analysis provide an alternative way to build a risk prediction model. This paper discusses the two approaches in reference to credit management and compares the impact and results of both methods. The Cox Proportional Hazard model displayed a better performance than that of Random Survival Forest when estimating credit risk.
VL  - 4
IS  - 4
ER  -

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Author Information

Dyana Kwamboka Mageto

Jomo Kenyatta University of Agriculture and Technology, Department of Statistics and Actuarial Science, Nairobi, Kenya
Samuel Musili Mwalili

Jomo Kenyatta University of Agriculture and Technology, Department of Statistics and Actuarial Science, Nairobi, Kenya
Anthony Gichuhi Waititu

Jomo Kenyatta University of Agriculture and Technology, Department of Statistics and Actuarial Science, Nairobi, Kenya

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APA Style

Dyana Kwamboka Mageto, Samuel Musili Mwalili, Anthony Gichuhi Waititu. (2015). Modelling of Credit Risk: Random Forests versus Cox Proportional Hazard Regression. American Journal of Theoretical and Applied Statistics, 4(4), 247-253. https://doi.org/10.11648/j.ajtas.20150404.13

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ACS Style

Dyana Kwamboka Mageto; Samuel Musili Mwalili; Anthony Gichuhi Waititu. Modelling of Credit Risk: Random Forests versus Cox Proportional Hazard Regression. Am. J. Theor. Appl. Stat. 2015, 4(4), 247-253. doi: 10.11648/j.ajtas.20150404.13

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AMA Style

Dyana Kwamboka Mageto, Samuel Musili Mwalili, Anthony Gichuhi Waititu. Modelling of Credit Risk: Random Forests versus Cox Proportional Hazard Regression. Am J Theor Appl Stat. 2015;4(4):247-253. doi: 10.11648/j.ajtas.20150404.13

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@article{10.11648/j.ajtas.20150404.13,
  author = {Dyana Kwamboka Mageto and Samuel Musili Mwalili and Anthony Gichuhi Waititu},
  title = {Modelling of Credit Risk: Random Forests versus Cox Proportional Hazard Regression},
  journal = {American Journal of Theoretical and Applied Statistics},
  volume = {4},
  number = {4},
  pages = {247-253},
  doi = {10.11648/j.ajtas.20150404.13},
  url = {https://doi.org/10.11648/j.ajtas.20150404.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20150404.13},
  abstract = {In survival analysis several regression modeling strategies can be applied to predict the risk of future events. Often, however, the default choice of analysis tends to rely on Cox regression modeling due to its convenience. Extensions of the random forest approach to survival analysis provide an alternative way to build a risk prediction model. This paper discusses the two approaches in reference to credit management and compares the impact and results of both methods. The Cox Proportional Hazard model displayed a better performance than that of Random Survival Forest when estimating credit risk.},
 year = {2015}
}

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TY  - JOUR
T1  - Modelling of Credit Risk: Random Forests versus Cox Proportional Hazard Regression
AU  - Dyana Kwamboka Mageto
AU  - Samuel Musili Mwalili
AU  - Anthony Gichuhi Waititu
Y1  - 2015/06/02
PY  - 2015
N1  - https://doi.org/10.11648/j.ajtas.20150404.13
DO  - 10.11648/j.ajtas.20150404.13
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
SP  - 247
EP  - 253
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20150404.13
AB  - In survival analysis several regression modeling strategies can be applied to predict the risk of future events. Often, however, the default choice of analysis tends to rely on Cox regression modeling due to its convenience. Extensions of the random forest approach to survival analysis provide an alternative way to build a risk prediction model. This paper discusses the two approaches in reference to credit management and compares the impact and results of both methods. The Cox Proportional Hazard model displayed a better performance than that of Random Survival Forest when estimating credit risk.
VL  - 4
IS  - 4
ER  -

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