International Journal of Statistical Distributions and Applications

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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.

DOI 10.11648/j.ijsd.20180401.14
Published in International Journal of Statistical Distributions and Applications (Volume 4, Issue 1, March 2018)
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), 2024. Published by Science Publishing Group

Keywords

Student Loans, Default Rates, Multiple Logistic Regression

References
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[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.
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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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  • 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://download.sciencepg.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
    AU  - Lucy Muthoni
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    JO  - 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.
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