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
  • TO CITE АНОТАЦІЯ
  • Cited by
    7 articles
  • 1460 Views
  • 306 Downloads

Creative Commons License
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.

view full abstract hide full abstract
    • 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
    • Aas, K., Huseby, B., and Thune, M. (1999). Data mining: A survey. Report, Norwegian Computing Centre.
    • Aktan, S. (2011). Application of machine learning algorithms for business failure prediction. Investment Management and Financial Innovations, 8(2), 53-65.
    • 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.
    • 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.
    • 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.
    • Al-Tamimi, H., and AL-Mazrooei, M. (2007). Banks’ risk management: A comparison study of UAE national and foreign banks. The Journal of Risk Finance, 8(4), 394-409.
    • Baesens, B., Setiono, R., Mues, C., and Vanthienen, J. (2003). Using Neural Network Rule Extraction and Decision Tables for Credit-Risk Evaluation. Management Science, 49(1-2), 312-329.
    • Bakshi, G., Dilip, M., and Frank, Z. (2001). Understanding the Role of Recovery in Default Risk Models: Empirical Comparisons and Implied Recovery Rates. Finance and Economics Discussion Series, Federal Reserve Board of Governors, Washington D.C.
    • Basel Committee on Banking Supervision. (2014). Results of the Basel III monitoring exercise as of 30 June 2013. Basel Committee.
    • Basel Committee on Banking Supervision. (2013). Principles for effective risk data aggregation and risk reporting. Basel Committee.
    • Basel Committee on Banking Supervision. (2000). International Convergence of Capital Measurement and Capital Standards: A Revised Framework. Basel Committee.
    • Bhattacharya, B., and Sinha Roy, N. (2008). Macroeconomic Determinants of Asset Quality of Indian Public Sector Banks: A Recursive VAR Approach. ICFAI Journal of Bank Management, 7(1), 20-40.
    • Bianco, A. M., and Yohai, V. J. (1996). Robust Estimation in the Logistic Model, in Robust Statistics, Data Analysis, and Computer Intensive Methods, 17-34; Lecture Notes in Statistics 109, Ed. H. Rieder, Springer Verlag, New York.
    • Campbell, A. (2007). Bank insolvency and the problem of nonperforming loans. Journal of Banking Regulation, 9(1), 25-45.
    • Central Bank of Jordan. (2006). Yearly Statistical Series. Central Bank of Jordan, Amman.
    • Central Bank of Jordan. (2010). Annual Report. Central Bank of Jordan, Amman.
    • Central Bank of Jordan. (2014). Yearly Statistical Series. Central Bank of Jordan, Amman.
    • Chabane, A., Laurent J., and Julien S. (2004). Double Impact: Credit Risk Assessment and Collateral Value. Revue Finance Journal, 25(1), 157-78.
    • Chatterjee, S., Corbae, Nakajima, M., and Ríos-Rull, J. (2007). A Quantitative Theory of Unsecured Consumer Credit with Risk of Default. Econometrica, 75(6),1525-90.
    • Cheng, M., Neamtiu, M. (2009). An Empirical Analysis of Changes in Credit Rating Properties: Timeliness, Accuracy and Volatility. Journal of Accounting and Economics, 47(3), 108-130.
    • Christensen, R. (1997). Log-Linear Models and Logistic Regression. 2nd Ed. Springer-Verlag Inc., New York, USA, 508.
    • Crouhy, M., and Mark, R. (2000). A comparative analysis of current credit risk models. Journal of Banking and Finance, 24(1-2), 59-117.
    • Croux, C., and Haesbroeck, G. (2003). Implementing the Bianco and Yohai estimator for logistic regression. Computational Statistics and Data Analysis, 44(4/5), 273-295.
    • Curram, S. P., and Mingers, J. (1994). Neural networks, decision tree induction and discriminant analysis: An empirical comparison. Journal of the Operational Research Society, 45(4), 440-450.
    • Dietsch, M., and Petey, J. (2002). The credit risk in SME loan portfolios: Modelling issues, pricing and capital requirements. Journal of Banking and Finance, 26(2), 303-322.
    • Fensterstock, A. (2005). Credit Scoring and the Next Step. Business Credits, 107(3), 46-49.
    • Goyal, A., and Joshi, V. (2010). Indian Banking Industry: Challenges and Opportunities. International Journal of Business Research and Management, 3(1), 18-28.
    • Gup, E., Avram, K., Beal, D., Lambert, R., and Kolari, W. (2007). Commercial Banking: The Management of Risk. (3rd Ed.). Australia: John Wiley and Sons Ltd.
    • Hayashi, F. (1987). Tests of Liquidity Constraints: A Critical Survey in Truman. Advances in Econometrics. Cambridge: Cambridge University Press.
    • Hsieh, N. (2000). An Integrated Data Mining and Behavioural Scoring Model for Analysing Bank Customers. Expert System with application, 27(1-2), 623-633.
    • Hui-Chung Yeh, Min-Li Yang, and Li-Chuen Lee. (2007). An Empirical Study of Credit Scoring Model for Credit Card, Second International Conference on Innovative Computing, Information and Control (ICICIC 2007), 216-220.
    • Jarrow, A. (2001). Default Parameter Estimation Using Market Prices. Financial Analysts Journal, 57(5), 75-92.
    • IMF. (2016). International Monetary Fund Global Financial Stability Report.
    • Lee, T., and Chen, A. (2002). Two-stage Hybrid Credit Scoring Model Using Artificial Neural Networks and Multivariate Adaptive Regression Splines. Expert Systems with Applications, 28(4), 743-752.
    • Lopez, A., and Saidenberg, R. (2000). Evaluating Credit Risk Models. Journal of Banking and Finance, 24(3-4), 151-165.
    • Mashhour, A., and Zaatreh, Z. (2000). A Framework for Evaluating the Effectiveness of Information Systems at Jordan Banks: An Empirical Study. Journal of Internet Banking and Commerce, 13(1), 44-50.
    • Ministry of Planning and International Cooperation. (2000). Access to Finance in Jordan. Jordan.
    • Ministry of Planning and International Co-operation. (2014). Jordan’s National Employment Strategy 2011-2020.
    • Nazari, M., and Alidadi, M. (2013). Measuring Credit Risk of Bank Customers Using Artificial Neural Network. Journal of Management Research, 5(2), 17-27.
    • Ohlson, A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109-131.
    • Ong, C., Huang, J., and Tzeng, G. (2005). Building credit scoring models using genetic programming. Expert Systems with Application, 29(1), 1-7.
    • Poudel, R. (2012). The impact of credit risk management on financial performance of commercial banks in Nepal. International Journal of Arts and Commerce, 1(5), 9-15.
    • Quartey, P. (2006). Migration and Development: Challenges and Opportunities for Sending Countries, Ghana country Case Study, A Report Prepared for the German Marshal Fund of the USA. Washington, 22-26.
    • Quercia, G., and Stegman, M. (1992). Residential Mortgage Default: A Review of the Literature. Journal of Housing Research, 3(2), 341-379.
    • Richard, R. (2003). Trade, International Competitiveness, and Entrepreneurial Cooperation: The Case of Jordan. Arab Bank Review, 5(2), 20-24.
    • Ripley, D. (1996). Pattern Recognition and Neural Nets. Cambridge University Press, New York.
    • Saunders, A., and Cornet, M. (2008). Financial institutions Management. A Risk Management Approach. (6thed.): McGraw-Hill Irwin.
    • Saurina, J., and Jimenez, G. (2006). Credit Cycles, Credit Risk, and Prudential Regulation. International Journal of Central Banking, 2(5), 65-98.
    • Serrano-Cinca, C., and Gutiérrez-Nieto, B. (2013). Partial Least Square Discriminant Analysis for bankruptcy prediction. Journal of Decision Support Systems, 54(6), 1245-1255.
    • Siam, A. (2007). Effects of Collateral Pledges in Reducing Credit Risks – Confronting Banks in Jordan, as Lending Institutions. Journal of Social Sciences, 3(3), 134-137.
    • Yeh, I., and Lien, C. (2009). The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients. Expert Systems with Applications, 36(2), 2473-2480.
    • Verstraen, G., and Van den Poel, D. (2004). The Impact of Sample Bias on Consumer Credit Scoring Performance and Profitability. Journal of the Operational Society, 56(8), 981-992.
    • West, D. (2000). Neural network credit scoring models. Computers and Operations Research, 27(1-2), 1131-1152.
    • Xiao, W., Zhao, Q., and Fei, Q. (2006). A comparative study of data mining methods in consumer loans credit scoring management. Journal of Systems Science and Systems Engineering, 15(4), 419-435.
    • Xu, M., and Walton, J. (2005). Gaining customer knowledge through analytical CRM. Industrial Management and Data Systems, 105(7), 955-971.
    • Zorn, M., and Lea, J. (1989). Mortgage Borrower Repayment Behaviour: A Microeconomic Analysis with Canadian Adjustable Rate Mortgage Data. AREUEA Journal, 17(1), 118-136.
    • Zurada, J., and Lonial, S. (2005). Comparison of the Performance of Several Data Mining Methods for Bad Debt Recovery inthe Healthcare Industry. The Journal of Applied Business Research, 22(2), 37-54.
Related Articles

  • Evaluation of empirical attributes for credit risk forecasting from numerical data

    Augustinos I. Dimitras , Stelios Papadakis , Alexandros Garefalakis doi: http://dx.doi.org/10.21511/imfi.14(1).2017.01
    Investment Management and Financial Innovations Volume 14, 2017         Issue #1         pp. 9-18 Views: 3567 Downloads: 1430 TO CITE АНОТАЦІЯ

    In this research, the authors proposed a new method to evaluate borrowers’ credit risk and quality of financial statements information provided. They use qualitative and quantitative criteria to measure the quality and the reliability of its credit customers. Under this statement, the authors evaluate 35 features that are empirically utilized for forecasting the borrowers’ credit behavior of a Greek Bank. These features are initially selected according to universally accepted criteria. A set of historical data was collected and an extensive data analysis is performed by using non parametric models. Our analysis revealed that building simplified model by using only three out of the thirty five initially selected features one can achieve the same or slightly better forecasting accuracy when compared to the one achieved by the model uses all the initial features. Also, experimentally verified claim that universally accepted criteria can’t be globally used to achieve optimal results is discussed.

  • The impact of credit risk and macroeconomic factors on profitability: the case of the ASEAN banks

    Myra V. De LeonORCID doi: http://dx.doi.org/10.21511/bbs.15(1).2020.03
    Banks and Bank Systems Volume 15, 2020         Issue #1         pp. 21-29 Views: 1918 Downloads: 662 TO CITE АНОТАЦІЯ

    This study investigates the effect of credit risk and macroeconomic factors on profitability of 20 ASEAN banks, particularly from Indonesia, Malaysia, Thailand and Philippines, covering the period of 2012 to 2017. The unbalanced panel data were tested for heteroscedasticity and normality. A fixed effects model and a random effects model were utilized followed by simple ordinary least squares (OLS) regression. The obtained results show that credit risk and GDP growth negatively affect Return on Equity (ROE) at 5% level of significance. The inflation rate increases ROE by 0.323%. In terms of influence, inflation has the highest impact on ROE followed by GDP growth and credit risk. Credit risk and GDP growth negatively affect Return on Assets (ROA) at 5% level of significance. ROA was also influenced by an increase in inflation rate. Therefore, this study will help banks and bank managers, depositors, investors, policy makers and governments to identify factors affecting bank profitability.

  • Factors affecting non-performing loans in commercial banks of selected West African countries

    Thabiso Sthembiso MsomiORCID Researcher ID doi: http://dx.doi.org/10.21511/bbs.17(1).2022.01
    Banks and Bank Systems Volume 17, 2022         Issue #1         pp. 1-12 Views: 1849 Downloads: 1253 TO CITE АНОТАЦІЯ

    This paper examines the macro-economic and bank-specific factors affecting non-performing loans in commercial banks. Using 47 listed commercial banks from six countries, namely 19 banks from Nigeria, 14 banks from Benin, 3 banks from Burkina Faso, 3 banks from Gambia, 3 banks from Guinea, and 5 banks from Liberia for the period 2008 to 2019, fixed and random effect model was used. The Hausman test favored the selection of fixed effect model, and it was found from the estimation that the liquidity ratio, capital adequacy ratio and inflation rate significantly affect non-performing loans. As a result, it is advised that banks depend not only on their ability to achieve the capital adequacy ratio, but also guarantee that loans are thoroughly scrutinized before being issued to beneficiaries. Bank managers should guarantee that banking staff is not simply awarding loans to secure their jobs by accumulating deposits from consumers at the price of the bank’s long-term stake. In addition, the economies of West Africa should keep their inflation rates low so that repayment of loans on time is cheap and realistic.

    Acknowledgment
    I would like to appreciate Fezile Nonjabulo Gcwabaza for love and support throughout this research project.

Download Preview
Close
Investment Management and Financial Innovations
Investment Management and Financial Innovations