Modelling Factors Affecting Probability of Loan Default: A Quantitative Analysis of the Kenyan Students' Loan

Pauline Nyathira Kamau; Lucy Muthoni; Collins Odhiambo

doi:doi:10.11648/j.ijsd.20180401.14

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Modelling Factors Affecting Probability of Loan Default: A Quantitative Analysis of the Kenyan Students' Loan

Pauline Nyathira Kamau, Lucy Muthoni, Collins Odhiambo

Published in International Journal of Statistical Distributions and Applications (Volume 4, Issue 1)

Received: 13 June 2018 Accepted: 17 July 2018 Published: 13 August 2018

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Abstract

In this study, we perform a quantitative analysis of loan applications by computing the probability of default of applicants using information provided in the Kenya Higher Education Loans application forms. We revisit theoretical distributions used in loan defaulters’ analysis particularly, when outliers are significant. Log-logistic, two-parameter Weibull, logistic, log-normal and Burr distribution were compared via simulations. Logistic and log-logistic model performs well under concentrated outliers; a situation that replicates loan defaulters data. We then apply logistic regressions where the binomial nominal variable was defaulter or re-payer, and different factors affecting default probability of a student were treated as independent variables. The resulting models are verified by comparing results of observed data from the Kenyan Higher Education Loans Board.

Published in	International Journal of Statistical Distributions and Applications (Volume 4, Issue 1)
DOI	10.11648/j.ijsd.20180401.14
Page(s)	29-37
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), 2018. Published by Science Publishing Group

Keywords

Student Loans, Default Rates, Multiple Logistic Regression

References

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APA Style

Pauline Nyathira Kamau, Lucy Muthoni, Collins Odhiambo. (2018). Modelling Factors Affecting Probability of Loan Default: A Quantitative Analysis of the Kenyan Students' Loan. International Journal of Statistical Distributions and Applications, 4(1), 29-37. https://doi.org/10.11648/j.ijsd.20180401.14

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ACS Style

Pauline Nyathira Kamau; Lucy Muthoni; Collins Odhiambo. Modelling Factors Affecting Probability of Loan Default: A Quantitative Analysis of the Kenyan Students' Loan. Int. J. Stat. Distrib. Appl. 2018, 4(1), 29-37. doi: 10.11648/j.ijsd.20180401.14

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AMA Style

Pauline Nyathira Kamau, Lucy Muthoni, Collins Odhiambo. Modelling Factors Affecting Probability of Loan Default: A Quantitative Analysis of the Kenyan Students' Loan. Int J Stat Distrib Appl. 2018;4(1):29-37. doi: 10.11648/j.ijsd.20180401.14

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@article{10.11648/j.ijsd.20180401.14,
  author = {Pauline Nyathira Kamau and Lucy Muthoni and Collins Odhiambo},
  title = {Modelling Factors Affecting Probability of Loan Default: A Quantitative Analysis of the Kenyan Students' Loan},
  journal = {International Journal of Statistical Distributions and Applications},
  volume = {4},
  number = {1},
  pages = {29-37},
  doi = {10.11648/j.ijsd.20180401.14},
  url = {https://doi.org/10.11648/j.ijsd.20180401.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijsd.20180401.14},
  abstract = {In this study, we perform a quantitative analysis of loan applications by computing the probability of default of applicants using information provided in the Kenya Higher Education Loans application forms. We revisit theoretical distributions used in loan defaulters’ analysis particularly, when outliers are significant. Log-logistic, two-parameter Weibull, logistic, log-normal and Burr distribution were compared via simulations. Logistic and log-logistic model performs well under concentrated outliers; a situation that replicates loan defaulters data. We then apply logistic regressions where the binomial nominal variable was defaulter or re-payer, and different factors affecting default probability of a student were treated as independent variables. The resulting models are verified by comparing results of observed data from the Kenyan Higher Education Loans Board.},
 year = {2018}
}

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TY  - JOUR
T1  - Modelling Factors Affecting Probability of Loan Default: A Quantitative Analysis of the Kenyan Students' Loan
AU  - Pauline Nyathira Kamau
AU  - Lucy Muthoni
AU  - Collins Odhiambo
Y1  - 2018/08/13
PY  - 2018
N1  - https://doi.org/10.11648/j.ijsd.20180401.14
DO  - 10.11648/j.ijsd.20180401.14
T2  - International Journal of Statistical Distributions and Applications
JF  - International Journal of Statistical Distributions and Applications
JO  - International Journal of Statistical Distributions and Applications
SP  - 29
EP  - 37
PB  - Science Publishing Group
SN  - 2472-3509
UR  - https://doi.org/10.11648/j.ijsd.20180401.14
AB  - In this study, we perform a quantitative analysis of loan applications by computing the probability of default of applicants using information provided in the Kenya Higher Education Loans application forms. We revisit theoretical distributions used in loan defaulters’ analysis particularly, when outliers are significant. Log-logistic, two-parameter Weibull, logistic, log-normal and Burr distribution were compared via simulations. Logistic and log-logistic model performs well under concentrated outliers; a situation that replicates loan defaulters data. We then apply logistic regressions where the binomial nominal variable was defaulter or re-payer, and different factors affecting default probability of a student were treated as independent variables. The resulting models are verified by comparing results of observed data from the Kenyan Higher Education Loans Board.
VL  - 4
IS  - 1
ER  -

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Author Information

Pauline Nyathira Kamau

Institute of Mathematical Sciences, Strathmore University, Nairobi, Kenya
Lucy Muthoni

Institute of Mathematical Sciences, Strathmore University, Nairobi, Kenya
Collins Odhiambo

Institute of Mathematical Sciences, Strathmore University, Nairobi, Kenya

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Plain Text BibTeX RIS

APA Style

Pauline Nyathira Kamau, Lucy Muthoni, Collins Odhiambo. (2018). Modelling Factors Affecting Probability of Loan Default: A Quantitative Analysis of the Kenyan Students' Loan. International Journal of Statistical Distributions and Applications, 4(1), 29-37. https://doi.org/10.11648/j.ijsd.20180401.14

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ACS Style

Pauline Nyathira Kamau; Lucy Muthoni; Collins Odhiambo. Modelling Factors Affecting Probability of Loan Default: A Quantitative Analysis of the Kenyan Students' Loan. Int. J. Stat. Distrib. Appl. 2018, 4(1), 29-37. doi: 10.11648/j.ijsd.20180401.14

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AMA Style

Pauline Nyathira Kamau, Lucy Muthoni, Collins Odhiambo. Modelling Factors Affecting Probability of Loan Default: A Quantitative Analysis of the Kenyan Students' Loan. Int J Stat Distrib Appl. 2018;4(1):29-37. doi: 10.11648/j.ijsd.20180401.14

Copy | Download

@article{10.11648/j.ijsd.20180401.14,
  author = {Pauline Nyathira Kamau and Lucy Muthoni and Collins Odhiambo},
  title = {Modelling Factors Affecting Probability of Loan Default: A Quantitative Analysis of the Kenyan Students' Loan},
  journal = {International Journal of Statistical Distributions and Applications},
  volume = {4},
  number = {1},
  pages = {29-37},
  doi = {10.11648/j.ijsd.20180401.14},
  url = {https://doi.org/10.11648/j.ijsd.20180401.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijsd.20180401.14},
  abstract = {In this study, we perform a quantitative analysis of loan applications by computing the probability of default of applicants using information provided in the Kenya Higher Education Loans application forms. We revisit theoretical distributions used in loan defaulters’ analysis particularly, when outliers are significant. Log-logistic, two-parameter Weibull, logistic, log-normal and Burr distribution were compared via simulations. Logistic and log-logistic model performs well under concentrated outliers; a situation that replicates loan defaulters data. We then apply logistic regressions where the binomial nominal variable was defaulter or re-payer, and different factors affecting default probability of a student were treated as independent variables. The resulting models are verified by comparing results of observed data from the Kenyan Higher Education Loans Board.},
 year = {2018}
}

Copy | Download

TY  - JOUR
T1  - Modelling Factors Affecting Probability of Loan Default: A Quantitative Analysis of the Kenyan Students' Loan
AU  - Pauline Nyathira Kamau
AU  - Lucy Muthoni
AU  - Collins Odhiambo
Y1  - 2018/08/13
PY  - 2018
N1  - https://doi.org/10.11648/j.ijsd.20180401.14
DO  - 10.11648/j.ijsd.20180401.14
T2  - International Journal of Statistical Distributions and Applications
JF  - International Journal of Statistical Distributions and Applications
JO  - International Journal of Statistical Distributions and Applications
SP  - 29
EP  - 37
PB  - Science Publishing Group
SN  - 2472-3509
UR  - https://doi.org/10.11648/j.ijsd.20180401.14
AB  - In this study, we perform a quantitative analysis of loan applications by computing the probability of default of applicants using information provided in the Kenya Higher Education Loans application forms. We revisit theoretical distributions used in loan defaulters’ analysis particularly, when outliers are significant. Log-logistic, two-parameter Weibull, logistic, log-normal and Burr distribution were compared via simulations. Logistic and log-logistic model performs well under concentrated outliers; a situation that replicates loan defaulters data. We then apply logistic regressions where the binomial nominal variable was defaulter or re-payer, and different factors affecting default probability of a student were treated as independent variables. The resulting models are verified by comparing results of observed data from the Kenyan Higher Education Loans Board.
VL  - 4
IS  - 1
ER  -

Copy | Download