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

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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[7] McCullagh P., Nelder J. A (1989) Generalized linear model, Chapman Hall, Newyork.
[8] O. Adem., & Waititu, A. (2012). Parametric modeling of the probability of bank loan default in Kenya. Journal of Applied Statistics, 14 (1), 61-74.
[9] Rafaella, C. Giampiero, M. Bankruptcy Prediction of small and medium enterprises using s flexible binary GEV extreme value model. American Journal of Theoretical and Applied Statistics, 1307 (2), 3556-3798.
[10] Nick Hillman, Don Hossler, Jacob P. K. Gross & Osman Cekic What Matters in Student Loan Default: A Review of the Research Literature Journal of Student Financial Aid, Issue 1, Article 2, 1-10-2010.
[11] Blom, Andreas, Reehana Raza, Crispus Kiamba, Himdat Bayusuf, and Mariam Adil. 2016. Expanding Tertiary Education for Well-Paid Jobs: Competitiveness and Shared Prosperity in Kenya. World Bank Studies. Washington, DC: World Bank. Doi: 10.1596/978-1-4648-0848-7. License: Creative Commons Attribution CC BY 3.0 IGO.
[12] Anamaria Felicia Ionescu The Federal Student Loan Program: Quantitative Implications for College Enrollment and Default Rates Economics Faculty Working Papers, Colgate University Libraries, Summer 6-2008.
[13] Felicia Ionescu & Nicole Simpson Default Risk and Private Student Loans: Implications for Higher Education Policies Finance and Economics Discussion Series, 2014- 066.
[14] Michal T. Njenga. The Determinant of Sustainability of Student Loan Schemes: Case Study of Higher Education Loans Board Scool of Business, University of Nairobi, November 2014.
[15] Mwangi Johnson Muthii Predicting Student’s Loan Default in Kenya: Fisher’s Discriminant Analysis Approach School of Mathematics, University of Nairobi, 2015.
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[18] Stephen Crowley Maximum Likelihood Estimation of the Negative Binomial Distribution Unpublished Working Paper, 2012.
[19] Elizabeth Herr & Larry Burt Predicting Student Loan Default for the University of Texas at Austin.
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Cite This Article
  • 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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    AU  - Pauline Nyathira Kamau
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    JF  - International Journal of Statistical Distributions and Applications
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    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
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Author Information
  • Institute of Mathematical Sciences, Strathmore University, Nairobi, Kenya

  • Institute of Mathematical Sciences, Strathmore University, Nairobi, Kenya

  • Institute of Mathematical Sciences, Strathmore University, Nairobi, Kenya

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