An Analysis of Factors Influencing Customer Creditworthiness in the Banking Sector of Kingdom of Bahrain
This research is based on Bahraini bankers’ perception on the factors influencing customer creditworthiness in the banking sector of Kingdom of Bahrain. We consider that the research was done in the Kingdom of Bahrain which has a growing banking industry. To enhance the whole procedure of the creditworthiness, it is vital for an employer to understand the most important factors influencing customer creditworthiness. The purpose of the study was to investigate the factors influencing customers creditworthiness in the banking industry. The creditworthiness can be assessed through qualitative factors, quantitative factors and risk factors. The research was conducted through a survey, using the questionnaire as the research instrument. The respondents of the study are employees of banks across the Kingdom dealing with creditworthiness. The statistical tools used in the study are Multiple Regression Analyses and weighted mean. The researcher has found that there is significant relationship between all three factors and creditworthiness, and they don’t equally influence the creditworthiness. The research provides recommendations to banks in assessing the creditworthiness. The researcher recommended that employees must use the most effective methods such as credit scoring to conduct the analysis of creditworthiness in order to make effective decisions. Moreover, the researcher recommended that analysts should take into considerations the most effective factors in the analysis process and they must not neglect other.
 Abdou, H. A., Tsafack , M. D., Ntim, C. G., & Baker, R. D. (2016). Predicting creditworthiness in retail banking with limited scoring data. Journal Elsevier, 103 , 89-103.
 Adeusi, S. O., Akeke, N., Adebis, O. S., & Oladunjoye, O. (2014). Risk management and financial performance of banks in Nigeria. IOSR Journal of Business and Management, 14(6), 52-56.
 Almansour, B. Y. (2015). Empirical model for predicting financial failure. American Journal of Economics, Finance and Management, 1(3), 113-122.
 Aven, T. (2016). Risk assessment and risk management. European Journal of Operational Research, 253, 1-13.
 Brown, K. & Moles, P. (2014). Credit risk management. (1st ed.). Edinburgh: Heriot-Watt University.
 Buchari, I., Rafiki, A., & Al Qassab, M. A. (2014). The employees’ awareness and attitudes towards islamic banking products: A survey of Islamic banks in Bahrain. World Applied Sciences Journal, 32(3), 436-443.
 Burakov, D. V., & Burakov, D. V. (2014). Do Methods of Estimantion Affect Credit Risk Oscillations? Mediterranean Journal of Social Sciences, 5(20), 114-120.
 Neda Abdulkarim Albaqqali & Jayendira Sankar. (2019). Empowerment and its relation with the job performance among the bank employees in the Kingdom of Bahrain. International Journal of Engineering and Management Research, 9(1), 27-31.
 Cole, S. & Shastry, G. K. (2008). If you are so smart, why arent you rich? The effects of education, financial literacy and cognitive ability on financial market participation. Available at: http://www.people.hbs.edu/scole/webfiles/cole-shastry-smarts%20HBS%20working%20paper.pdf.
 Dezfouli, M. H., Hasanzadeh, A., & Shahchera, M. (2014). Assessing the profitability of the iranian banking system's non-linear relationship with liquidity risk . Kuwait Chapter of Arabian Journal of Business and Management Review, 3(9), 226-235.
 Doležal, J., Šnajdr, J., Belás, J., & Vincúrová, Z. (2015). Model of the loan process in the context of unrealized income. Journal of International Studies, 8(1), 91-106.
 Elkelish, W. W., Miniaoui, H., & Al Tamimi, H. A. (2016). Financial risk and islamic banks’ Performance in the gulf cooperation council countries. The International Journal of Business and Finance Research, 9(5), 103-112.
 Ghenimi, A., Chaibi, H., & Omri , M. A. (2017). The effects of liquidity risk and credit risk on bank stability. Borsa Istanbul Review, 17(4), 238-248.
 Jagtiani, J. & Lemieux, C. (2017). Fintech lending: financial inclusion,risk pricing, and alternative information. Chicago: Federal Reserve Bank of Philadelphia.
 Khaliq, A., Hussein, B., Altarturi, M., Thaker, H. M., Yousuf, M. H., & Nahar, N. (2014, January). Identifying financial distress firms. International Journal of Economics, Finance and Management, 3(3), 141-150.
 Roozmehr Saf & Zhangxi Lin. (2014). Using non-financial data to assess the creditworthiness of business in online trade. Available at:
 McNeil, A. J., Frey, R., & Embrechts, P. (2015). Quantitative risk management. (1st ed.). London: Princton University.
 Guidelines on Credit Risk Mangement. (2014). Credit approval process and credit risk management. (1st ed.). Vienna, Austria: OENB.
 Nikolaidoua, E., & Vogiazas, S. (2017). Credit risk determinants in Sub-Saharan banking systems. Elsevier, 7, 52-63.
 Puri, T., Sibdari, S., & Zhang, X. (2016). Improving credit scoring quality through virtual organization. Available at: http://www.bapress.ca/jcm/jcm-article/1929-0136-2017-01-25-14.pdf.
 Sudhakar , M., & Reddy, C. V. (2016). Two step credit risk assesment model for retail bank loan applications using decision tree data mining technique. International Journal of Advanced Research in Computer Engineering & Technology, 5(3), 705-718.
 Thomas, L., Crook, J., & Edelman, D. (2017). Credit scoring and its applications (2nd ed.). London: SIAM.
 Zhanga, Y., Jiaa, H., Diaoa, Y., Haia, M., & Lia, H. (2016). Research on credit Scoring by fusing social media information. Elsevier, 91, 168-174.