Credit Risk Assessment for an Islamic Bank in Bosnia and Herzegovina



Assessing creditworthiness of a company using financial data analysis is one of the most important elements of an overall credit risk estimate. The Basel II and III rules have precisely defined, among other rules, risk management tools, minimal capital requirements as well as higher level of financial discipline. Similar rules exist in the USA as well. Still, there are many differences in commercial banking implementations of credit risk assessment, whereas all methods strive to predict default probability more precisely. In that sense, clients’ financial data over time are analyzed by the banks in order to make credit decision. In this paper, we present some introductory results in Islamic bank credit risk assessment where an Islamic bank data are taken from a case of diminishing musharakah. Financial ratios as hard banking data were used for evaluation of Islamic bank client credit risk level. The aim of the paper is to develop credit risk model for company clients in order to predict probability of default (PD). The dataset used in this paper consisted of 151 small and medium companies that are clients of one Islamic bank in Bosnia and Herzegovina (B&H) for 2012 and 2013. Logistic regression (LR) is used for model development. Islamic banks due to their profit–loss sharing (PLS) have additional need for assessing probability of successful client financing, or joint investment. Up until 2008 and big worldwide financial credit crisis, Islamic banks relied only on expert opinions in credit decisions instead of more sophisticated mathematical and statistical methods. As globalization is taking hold and due to Islamic financing worldwide growth there is a need for better creditworthiness predictions and better tools for Islamic banks.


Hard data Probabilistic models Credit risk Default risk Islamic banking 


  1. Abdou, H., Alam, S., & Mulkeen, J. (2014). Would Credit Scoring Work for Islamic Finance? A Neural Network Approach. International Journal of Islamic and Middle Eastern Finance and Management, 7(1), 112–125.Google Scholar
  2. Agencija za Bankarstvo Federacije Bosne i Hercegovine. (2019, February 27).
  3. Ali, S. (2007). Financial Distress and Bank Failure: Lessons from Closure of Ihlas Finance in Turkey. Islamic Economic Studies, 14(1/2), 1–52.Google Scholar
  4. Altman, E. (1968). Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. Journal of Finance, 23, 589–609.Google Scholar
  5. Altman, E. I. (1984). The Success of Business Failure Prediction Models: An International Survey. Journal of Banking and Finance, 8(2), 171–198.Google Scholar
  6. Altman, E., Haldeman, R., & Narayanan, P. (1977). Zeta Analysis: A New Model to Identify Bankruptcy Risk of Corporations. Journal of Banking & Finance, 1, 29–54.Google Scholar
  7. Analytics Vidhya Content Team. (2015, November 1). Retrieved May 25, 2018, from
  8. Andreev, Y. A. (2005). Predicting Financial Distress of Spanish Companies. Barcelona: Department of Business Economics, University of Barcelona.Google Scholar
  9. Appiah, K. (2011). Corporate Failure Prediction: Some Empirical Evidence from Listed Firms in Ghana. China-USA Business Review, 10(1), 32–41.Google Scholar
  10. Bakiciol, T., Cojocaru-Durand, N., & Lu, D. (2013). Basel II. Retrieved November 29, 2018, from
  11. Basel Committee on Banking Supervision. (2003). Consultative Document; Overview of the New Basel Capital Accord. Basel: Bank for International Settlements.Google Scholar
  12. Basel Committee on Banking Supervision. (2017, December 7). Basel III: International Regulatory Framework for Banks. Retrieved June 25, 2018, from
  13. Brkić, S., Hodzic, M., & Dzanic, E. (2018). Soft-Hard Data Fusion via Uncertainty Balance Principle—Evidence from Corporate Credit Risk Assessment in Commercial Banking. Work in Progress. Sarajevo.Google Scholar
  14. Centralna Banka Bosne i Hercegovine. (2018). Retrieved from
  15. Ciampi, F., & Gordini, N. (2008). Using Economic Financial Ratios for Small Enterprise Default Prediction Modelling: An Empirical Analysis. In Oxford Business & Economics Conference Program (pp. 1–21).Google Scholar
  16. Desai, V. S., Crook, J. N., & Overstreet, G. A. (1997). Credit Scoring Models in Credit Union Environment Using Neural Network and Generic Algorithms. IMA Journal of Mathematic Applied in Business & Industry, 8, 323–346.Google Scholar
  17. Dvoracek, I., Sousedikova, R., & Domaracka, L. (2008). Industrial Enterprises Bankruptcy Forecasting. Metalurgija, 47, 33–36.Google Scholar
  18. Galindo, J., & Tamayo, P. (2000). Credit Risk Assessment Using Statistical and Machine Learning: Basic Methodology and Risk Modeling Applications. Computational Economics, 15, 107–143.Google Scholar
  19. Gerantonis, N., & Christopoulos, A. (2009). Can Altman Z-score Models Predict Business Failures in Greece. Research Journal of International Studies, 12(21), 21–28.Google Scholar
  20. Islamic Financial Services Board (IFSB). (2005). Guiding Principles of Risk Management. Retrieved October 28, 2018, from
  21. Memić, D. (2015). Assessing Credit Default Using Logistic Regression and Multiple Discriminant Analysis: Empirical Evidence from Bosnia and Herzegovina. Interdisciplinary Description of Complex Systems, 13(1), 128–153.Google Scholar
  22. Model Evaluation—Classification. (2018, May 25). Retrieved from
  23. Nam, J. H., & Jinn, T. (2000). Bankruptcy Prediction: Evidence from Korean Listed Companies During the IMF Crisis. Journal of International Financial Management and Accounting, 11(3), 178–197.Google Scholar
  24. Odipo, M., & Sitati, A. (2010). Evaluation of Applicability of Altman´s Revised Model in Prediction of Financial Distress: A Case of Companies Quoted in the Nairobi Stock Exchange. Retrieved October 28, 2018, from
  25. Ohlson, J. (1980). Financial Ratios and Probabilistic Prediction of Bankruptcy. Journal of Accounting Research, 18(1), 109–131.Google Scholar
  26. Saracevic, N., & Sarlija, N. (2017). The Usefulness of Financial Ratios in Discriminating Between Healthy and Distressed Companies: A Case of an Islamic bank. In V. Efendic, F. Hadzic, & H. Izhar (Eds.), Critical Issues and Challenges in Islamic Economics and Finance Development (pp. 137–155). Berlin: Springer-Verlag Gmbh.Google Scholar
  27. Šarlija, N., Šoric, K., & Vojvodic, V. (2009). Logistic Regression and Multicriteria Decision Making in Credit Scoring. Retrieved October 28, 2018, from
  28. Šarlija, N., & Gereč, A. Z. (2008). Kratak pregled Basela 2. Retrieved November 29, 2018, from
  29. Shen, H. C., & Huang, Y. B. (2010). The Prediction of Default with Outliers: Robust Logistic Regression. In Handbook of Quantitative Finance and Risk Management (pp. 965–977). Boston: Springer.Google Scholar
  30. Sheskin, J. D. (2004). Handbook of Parametric and Nonparametric Statistical Procedures. Washington, DC: Chapman & Hall and CRC.Google Scholar
  31. Sidik, G. K., Djatna, T., & Buono, A. (2013). An It2fs Model for Sharia Credit Scoring: Analysis & Design. Journal of Information System, 8(2), 58–66.Google Scholar
  32. Train, F. (2018). Effective Maturity in Basel II. Retrieved January 10, 2019, from
  33. Zekic-Susac, M., Sarlija, N., & Bensic, M. (2004). Small Business Credit Scoring: A Comparison of Logistic Regression, Neural Network and Decision Tree Models. In Proceedings of the 26th International Conference on Information Technology Interfaces (pp. 265–270). Cavtat, Dubrovnik, Croatia.Google Scholar
  34. Zmijewski, E. M. (1984). Methodological Issues Related to the Estimation of Financial Distress Prediction Models. Journal of Accounting Research, 12, 59–82.Google Scholar

Copyright information

© The Author(s) 2020

Authors and Affiliations

  1. 1.International University of SarajevoSarajevoBosnia and Herzegovina
  2. 2.University of J.J. StrossmayerOsijekCroatia

Personalised recommendations