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논문 기본 정보

자료유형
학술저널
저자정보
Noha Ibrahem Hasan (Sadat Academy for Administration Science) Haitham Elghareeb (Mansoura University) Farahat Farag Farahat (Sadat Academy for Administration Science) Ahmed AboElfotouh (Mansoura University)
저널정보
한국지능시스템학회 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS Vol.21 No.1
발행연도
2021.3
수록면
66 - 75 (10page)
DOI
10.5391/IJFIS.2021.21.1.66

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초록· 키워드

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Non-financial analysis is one of the varied crucial directive tools of credit study that is used for judging whether the client has a genuine desire to pay the assigned amounts of the loan at its maturity dates. Fuzzy logic can help to solve the problem of dealing with factors of non-financial analysis by converting the linguistic variables to numerical variables to calculate their accuracy. This study proposes a fuzzy model that contains a complete database of non-financial factors used by the decision-maker using a fuzzy logic technique, which helps in building the fuzzy rules with great accuracy and helps in predicting the actual situation of the client. In addition, it provides constant following-up of the uses of the granted loan to guarantee that all terms set by the bank are met so that the bank can avoid future defaulting of the client. The proposed model is applied in the credit department of a private Egyptian bank (QNB), with random samples of previous real clients. Some real standards are set to calculate non-financial factors that are related to the client, management, economic situation, and project activity. The results of the proposed model revealed that the correlation factor is 95.3% between real successful payment clients and successful model clients. To guarantee the accuracy of the knowledge base quality and validation, the knowledge model was presented to the credit manager of the bank under study (expert), who provided a full evaluation of the results of the proposed model compared to the actual situation of clients.
#Credit risk #Non-performing loans #Fuzzy information systems #Knowledge base #Decision making

목차

Abstract
1. Introduction
2. Literature Review
3. Proposed Fuzzy Expert Model Domain
4. Fuzzy Expert System Development
5. Results
6. Evaluation of Proposed Model
7. Discussion of Results
8. Comparison between the Proposed Model and Another Models
9. Conclusion
References

참고문헌 (17)

참고문헌 신청
Central Bank of Egypt / Annual Report of Financial and Economic for 2018/2019 google schola Moody’s Investors Service / Rating Action: Moody’s takes rating action on 13 Turkish corporates following sovereign downgrade google schola W. Siler / 2004 / Fuzzy Expert Systems and Fuzzy Reasoning / John Wiley & Sons Inc google schola N. R. Darwish / 2016 / A fuzzy logic model for credit risk rating of Egyptian commercial banks / International Journal of Computer Science and Information Security 14(2):11~18 google schola S. Brkic / 2017 / Fuzzy logic model of soft data analysis for corporate client credit risk assessment in commercial banking / Proceedings of the 5th Scientific Conference with International Participation “Economy of Integration” (ICEI) google schola

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