Credit risk management and business intelligence approach of the banking sector in Jordan

Abstract

Banking segment is one of the ultimate key segments that support the sustainable economic progress in Jordan. Hence, banks in Jordan are considered as tremendously significant financial establishments that pursue profit by providing various financial services to various customers through dealing with different kinds of risk. Therefore, loan decisions for such institutions are crucial because they can avert credit risk. However, loan sanction assessment at Jordanian banks is particularly based on credit officer’s intuition and sometimes a combination of credit officer’s judgment and traditional credit scoring models. Consequently, it is important to assess the riskiness of the banking sector in Jordan. Then again, banks kept data regarding their clienteles in data warehouses that can be looked as concealed knowledge assets that can be read and exercised via data mining tools. Artificial Neural Networks (ANN) denote a recent development of statistical techniques and promising tools of data mining and data processing. The current study attempts to develop an artificial neural network model as a decision support system to credit approval evaluation at Jordanian commercial banks based on applicant’s characteristics; the proposed model can be utilized to aid credit officers make better decisions when evaluating future loan applications. A real-world credit application of cases of both granted and rejected applications from different Jordanian banks was employed to develop the artificial neural model. The experimental outcomes showed that artificial neural networks area promising addition to the existing classification methods.

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