Credit risk estimate using internal explicit knowledge

  • Published March 31, 2017
  • Author(s)
    Abdallah Al-Shawabkeh , Rama Kanungo
  • DOI
    http://dx.doi.org/10.21511/imfi.14(1).2017.06
  • Article Info
    Volume 14 2017, Issue #1, pp. 55-66
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    7 articles
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This work is licensed under a Creative Commons Attribution 4.0 International License

Jordanian banks traditionally use a set of indicators, based on their internal explicit knowledge to examine the credit risk caused by default loans of individual borrowers. The banks are reliant on the personal and financial information of the borrowers, obtained by knowing them, often referred as internal explicit knowledge. Internal explicit knowledge characterizes both financial and non-financial indicators of individual borrowers, such as; loan amount, educational level, occupation, income, marital status, age, and gender. The authors studied 2755 default or non-performing personal loan profiles obtained from Jordanian Banks over a period of 1999 to 2014. The results show that low earning unemployed borrowers are very likely to default and contribute to non-performing loans by increasing the chances of credit risk. In addition, it is found that the unmarried, younger borrowers and moderate loan amount increase the probability of non-performing loans. On the contrary, borrowers employed in private sector and at least educated to a degree level are most likely to mitigate the credit risk. The study suggests improving the decision making process of Jordanian banks by making it more quantitative and dependable, instead of using only subjective or judgemental based understanding of borrowers.

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    • Table 1. Approaches of credit risk management
    • Table 2. Credit risk variable description
    • Table 3. Descriptive statistics of the credit risk variables
    • Table 4. Correlation results of credit risk variables
    • Table 5. Logistic regression results of the credit risk variables
    • Table 6. Comparison between the commonly used credit risk model
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    Acknowledgment
    I would like to appreciate Fezile Nonjabulo Gcwabaza for love and support throughout this research project.

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