Modelling Factors Affecting Probability of Loan Default: A Quantitative Analysis of the Kenyan Students' Loan
International Journal of Statistical Distributions and Applications
Volume 4, Issue 1, March 2018, Pages: 29-37
Received: Jun. 13, 2018; Accepted: Jul. 17, 2018; Published: Aug. 13, 2018
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Authors
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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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.
Keywords
Student Loans, Default Rates, Multiple Logistic Regression
To cite this article
Pauline Nyathira Kamau, Lucy Muthoni, Collins Odhiambo, Modelling Factors Affecting Probability of Loan Default: A Quantitative Analysis of the Kenyan Students' Loan, International Journal of Statistical Distributions and Applications. Vol. 4, No. 1, 2018, pp. 29-37. doi: 10.11648/j.ijsd.20180401.14
Copyright
Copyright © 2018 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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