The Role of Expert System in Granting Credit Facilities

Authors

  • Somaye Hoseini University of Mehr Alborz

DOI:

https://doi.org/10.24200/jrset.vol5iss02pp35-38

Abstract

In this study, the expert system considered customer financial ratios as input and prediction of credit risk level as output. This study was a descriptive-case study research. The population consisted of credit experts of Tejarat bank who were the member of bank’s credit Committee and had the right to vote for facilities approval and the individuals whose main task was providing reports for granting facilities and monitoring the use of facilities. After an initial interview and determining the evaluation criteria for facilities and determining the items for each of the criteria, a questionnaire was designed using Likert scale. Data normality test was conducted to ensure the accuracy of the collected data. T-test was performed to realize the selected criteria are important. Then, experts were asked to determine the minimum score for providing the facility to the applicant in each section of the questionnaire. The laws of expert system were provided based on determined minimum scores. 

References

A.B.Emela, M.Oralb, A.Reismanb, R.Yolalan,"A credit scoring approach for the commercial banking sector," Socio-Economic Planning Sciences, 2003, 37, 103–123.

B.Baesens, T.V.Bestel, S.Viaene, M.Stepanova, J.Suykens, J.Vanthienen," Benchmarking state-of-the-art classification algorithms for credit scoring," Journal of Operational Research, 2003, 54, 627–635.

B.Chang, C.W.Chang, C.H.Wu,"Fuzzy DEMATEL method for developing supplier selection criteria," Expert Systems with Applications, 2011, 38, 1850–1858.

Boer, G.B., & Livnat, J. Using expert systems to teach complex accounting issues. Issues in Asccounting Education, Spring, 1990, 108-119.

Grljević Olivera, Bošnjak Zita ,"Development of Creditworthiness Expert System", September 2010, 22-24.

E.Angelini, G.D.Tollo, A.Roli,"A neural network approach for credit risk evaluation," The Quarterly Review of Economics and Finance, 2008, 48(4), 733–755.

L.C.Thomas, "A survey of credit and behavioral scoring: Forecasting financial risk of lending to consumers," International Journal of Forecasting 2002, 16, 149–172.

Ljubica Nedović1 and Vladan Devedžić" EXPERT SYSTEMS IN FINANCE – A CROSS-SECTION OF THE FIELD",School of Business Administration, University of Belgrade, Čačanska banka ad Čačak.

L.Yu, SH.Wang, K.K.Lai," An intelligent-agent-based fuzzy group decision making model for financial multicriteria decision support: The case of credit scoring," European Journal of Operational Research,2009, 195, 942–959.

L.Yu, S.Wang, K.K.Lai," Credit risk assessment with a multistage neural network ensemble learning approach," Expert Systems with Applications, 2008, 34(2), 1434–1444.

Vranes, S. Expert System Shell Flexibility: BEST Case Study, in S. Tzafestas(Ed.) Engineering Systems with Intelligence, Kluwer Academic Publishers, 1992, pp. 33-38.

W.R.J.Ho, C.L.Tsai, G.H.Tzeng, S.K.Fang,"Combined DEMATEL technique with a novel MCDM model for exploring portfolio selection based on CAPM," Expert Systems with Applications, 2011, 38, 16–25.

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Published

2019-09-13

Issue

Section

Articles