A Framework for a Hybrid Intelligent System in Support of Solving Credit Approval Problems in Banks

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I. Introduction (135)

Many consumers utilize credit facilities offered by banks to meet their needs. “Credit approval is the process a business or an individual undergoes to become eligible for a loan or pay for goods and services over an extended period of time” (Leotta, 2011). Banks use various techniques to screen applicants before they allow them to utilize their credit facilities. The tools and techniques used to screen applicants constitute a significant risk to the banks. If they are faulty, then, businesses and individuals who are not creditworthy may receive the nod to utilize credit. This is the source of major problems for banks characterized by bad debt, default payment, and high cost of litigation. Information technology has the capacity to enhance the process of credit approval to reduce the risks associated with provision of credit.

II. Analysis of credit approval problems in banks (550)

[This shows that there is a strong drive towards computerization of the credit approval process. Credit approval has its basis on trust, which a computer cannot measure. In fact, computerized systems rely on ratios and other documented patterns such as default rate on past loans, aiming at establishing a credible credit history.]

[Banks, through the credit rating agencies, do not have any information relating to parking tickets, criminal record, child support information, previously declined applications, defaults and missed payments that are more than six years old.]

[One problem with credit approvals is what Lewis (2010) calls “the rejection spiral”. Here, an applicant gets a series of rejection on credit applications and after a few of them, the rest of the institutions simply reject the application based on rejection ...

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