Credit Assessment of Bank Customers by a Fuzzy Expert System Based on Rules Extracted from Association Rules

Nosratabadi, Hamid Eslami; Nadali, Ahmad; Pourdarab, Sanaz

doi:10.7763/ijmlc.2012.v2.210

Cited by 16 publications

(4 citation statements)

References 18 publications

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“…To create good risk management, we need several other methods of measuring risk, such as those carried out by Oktavina et al, (2014), Nosratabadi et al, (2012) and Maholtra and Maholtra (2002). The results of their research indicate a good impact on internal auditing and measuring the risks that exist.…”

Section: In Financing Riskmentioning

confidence: 99%

Mashlaha in Financing Risk Measurement in Sharia Financing Institutions

2019

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The crisis of confidence in the credit rating agency forced Islamic financing institutions to apply risk measurement methods independently and renewed the study of credit risk measurement. Moreover, this research also discusses mashlaha (public interest) in measuring financing risk. This research uses a mixed method approach, combining quantitative methods to measure risk by utilizing CreditRisk+, and qualitative methods in analyzing mashlaha in these measurements. This study revealed that CreditRisk+ is able to measure financing risk accurately. This study also found that there is mashlaha as part of maqashid al-sharia in risk measurement, namely 1) Tahdzib al-Fard, that makes a financial institution capable of independently measuring the risk of its own financing; 2) Iqamah al-Adl, independent measurement will create information justice by comparing measurement results both internally and externally. 3) Mashlaha itself, with internal risk measurement, will reduce systemic risk. The implications of this study is the use of mashlaha in analyzing financing risk provides more stringent prudential in the measurement of financing risk.

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Section: In Financing Riskmentioning

confidence: 99%

Mashlaha in Financing Risk Measurement in Sharia Financing Institutions

2019

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“…In models based on fuzzy set theory, the scoring system is based on a fuzzy inference procedure, which includes several steps. The initial factors and the resulting attributes are defined using the membership functions and are linked together by a complete system of logical rules [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

The Evaluation of Creditworthiness of Trade and Enterprises of Service Using the Method Based on Fuzzy Logic

Makhazhanova

Kerimkhulle²,

Mukhanova³

et al. 2022

Applied Sciences

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This article considered the problem of determining the creditworthiness of an enterprise operating in the field of trade and services. The assessment of the creditworthiness of borrowers, particularly small businesses, needs to be more careful: the level of development of small enterprises and their specific activities must be considered, as well as the uncertainty in obtaining any financial result. A method for assessing the creditworthiness of enterprises (trade and services) is proposed, based on the use of the mathematical apparatus of the theory of fuzzy sets. This article analyzes the indicators of industry and regional specifics, indicators of the activity of a small enterprise, and financial and economic indicators typical for the service sector and trade. The rules on the basis of which decisions are made are formed in the form of logical formulas containing parameters. In its most general form, one parameter is predicted, called the creditworthiness index, which varies from 0 to 1 and has a natural interpretation. On the basis of the proposed method, examples of calculating the assessment of the creditworthiness of enterprises operating in the field of trade and services are given. The proposed scientific approach can be used as a basis for creating expert decision support systems for lending to small businesses.

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“…In 2012 Hamid Eslami Nosratabadi et.al proposed the fuzzy expert system that classifies the customers of the bank using classification rules and association rules with the help of Apriori algorithm and CRISP-DM process, specifies the Credit Degree of banks' customers [7] [8]. It combines the extracted rules of association mining and the knowledge of experts.…”

Section: Introductionmentioning

confidence: 99%

SPACS: Students’ Performance Analysis and Counseling System using Fuzzy logic and Association Rule Mining

Banswal¹,

Madaan²

2016

IJCA

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Education plays an important role in one's life if the right education and right environment is not provided then it somewhat distract the person from his/her path. In this regard, fuzzy expert system known as SPACS (Students' Performance Analysis and Counselling System) is designed which helps to analyze the students' academic performance by identifying all the critical factors and counsels him/her against those affecting parameters. In this paper, the study highlights on the critical factors that degrades the academic performance of the student based on the some interesting relationships between those factors. The performance of the overall system depends on the accuracy of the fuzzy knowledge base. Hence, association rule mining technique helps to generate the accurate results and also decreases the complexity of the system. General TermsData mining, Expert System, quality education, academic performance, and fuzzy rule based expert system.

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Credit Assessment of Bank Customers by a Fuzzy Expert System Based on Rules Extracted from Association Rules

Cited by 16 publications

References 18 publications

Mashlaha in Financing Risk Measurement in Sharia Financing Institutions

Mashlaha in Financing Risk Measurement in Sharia Financing Institutions

The Evaluation of Creditworthiness of Trade and Enterprises of Service Using the Method Based on Fuzzy Logic

SPACS: Students’ Performance Analysis and Counseling System using Fuzzy logic and Association Rule Mining

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