Credit risk estimate using internal explicit knowledge

Author:

Al-Shawabkeh Abdallah1,Kanungo Rama2

Affiliation:

1. Dr., MIS Department, College of Business Administration, Al Ain University of Science and Technology

2. Newcastle University London

Abstract

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.

Publisher

LLC CPC Business Perspectives

Subject

Strategy and Management,Economics and Econometrics,Finance,Business and International Management

Reference57 articles.

1. Aas, K., Huseby, B., and Thune, M. (1999). Data mining: A survey. Report, Norwegian Computing Centre.

2. Aktan, S. (2011). Application of machine learning algorithms for business failure prediction. Investment Management and Financial Innovations, 8(2), 53-65. - http://businessperspectives.org/journals_free/imfi/2011/imfi_en_2011_02_Aktan.pdf

3. Altman, I., and Saunders, A. (1998). Credit risk measurement: Developments over the last 20 years. Journal of Banking and Finance, 21(3-4), 1721-1742.

4. Atiya, F. (2001). Bankruptcy Prediction for Credit Risk Using Neural Nets: A survey and New Results. IEEE Transactions on Neural Nets, 12(4), 929-935.

5. Altman, I., Resti, A., and Sironi, A. (2006). Default Recovery Rates: A Review of the Literature and Recent Empirical Evidence. Journal of Finance Literature, 12(1), 21-45.

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