Abstract
This study on prototype self-learning knowledge based system (KBS) is focused on evaluation of loan application used to overcome the challenges that resulted from lack of domain experts and poor loan evaluations. We attempted to design and develop a prototype self-learning KBS that provide advisory services for any credit customers and assists the domain experts in evaluation of customer’s requests for the loan. To develop this prototype system, knowledge was acquired using semi-structured interview from domain experts which are selected using purposive sampling technique from Commercial Bank of Ethiopia (CBE) and critique the acquired knowledge. Explicit knowledge is acquired by analyzing the secondary source of knowledge by method of document analysis. Then, the acquired knowledge is modeled using decision tree that represents concepts and procedures involved in credit evaluation and production rules are used to represent the domain knowledge. The prototype system is implemented using SWI Prolog editor tool. To determine the applicability of the prototype system in the domain area, the system has been evaluated and tested by the domain experts. Eighteen (18) test cases were selected purposively. Test cases are equally selected from both ineligible and eligible cases. The overall total performance of the prototype system is 77.71%. The performance of the prototype system is hopeful and meets the objective of the study. The study concludes that the major credit production type that advanced to customer is import letter of credit facility, export credit facility, pre-shipment credit facility and merchandise. The eligibility of application is focused on general and specific criteria. Credit customer is classified as business, corporate and commercial based on the score sheet they achieved. Generally, in this study, the applicability of knowledge of prototype self-learning KBS is proved as hopeful approach in banking industry for credit evaluation. Keywords : KBS, self-learning and credit (loan). DOI: 10.7176/IKM/10-5-02 Publication date: August 31 st 2020
Highlights
Banks are financial institutions that accept deposit and make loans
In doing such boring and complex tasks, a bank has a high probability expose to risks because of credit scoring evaluation process depends on domain experts who are accountable for assessment of the credit applications and making decision to accept or to reject it
We developed a low-cost automated self-learning knowledge based system (KBS) fitting in the skills of credit evaluator experts to help domain experts within evaluation of loan application and provide advisory services for any credit customers
Summary
Banks are financial institutions that accept deposit and make loans. Its role in the economy of any country is very significant. The financial soundness of a bank is largely dependent on the riskiness of its loan portfolio [5].In CBE, each individual loan applicant is assigned to customer relationship manager for the purpose of evaluation of their application This is very difficult and time consuming in the case of more applicants. To overcome the above problems, we motivated to design and develop selflearning KBS incorporating the skills of domain experts to assist credit evaluator or officer in a credit evaluation as well as provide advisory services for credit applicant requirements. The main goal of this study is to examine whether ES technology is an effective and efficient means of providing advice and support for the agricultural loan evaluation process. The proposed self-learning KBS can assist domain expert during loan evaluation process and credit customer by providing advisory services. Some of the most common domain areas of Prolog are environmental modeling, sales modeling, medical domain, fungus identification, image recognition, management consultancy, etc. it has the ability to change its program whilst that program is running and it relates to a logic called predicate calculus [14] [15]
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