Credit risk management for the Jordanian commercial banks: A business intelligence approach

• Main Authors: Bekhet, H.A., Eletter, S.F.K.
• Published: 2018
• Online Access: http://dspace.uniten.edu.my/jspui/handle/123456789/9403

Commercial banks in Jordan are regarded as vitally important and competitive financial organizations that seek profit by providing various financial services to various customers while managing different types of risk. Credit forms a cornerstone of the banking industry as credit behavior stronglyinfluences the profitability and stability of a bank. Therefore, loan decisions for such instuitions are crucialbecause they can avert credit risk. However, loan application evaluation at Jordanian banks is subjective based oncredit officer's intuition and sometimes a combination of credit officer'sjudgment and traditional credit scoring models. On the other hand, banks store data about their customers in data warehouses which can be viewed as hidden knowledge assets that can be accessed and used through data mining tools. Artificial Neural Networks (ANN) represent a recent development of a new family 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 systemto 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 accepted and rejected applications from different Jordanian commercial banks was used to build the artificial neural model. The experimental results show that artificial neural networks area promising addition to the existing classification methods.