Integration of factor analysis and Tsukamoto’s fuzzy logic method for quality control of credit provisions in rural banks
Giving credit to debtors can pose a default risk. This risk arises because of an error in analyzing the credit risk rate of the debtor. Therefore, this study aims to design a framework for analyzing the credit risk rate of debtors so that the default risk can be reduced. This framework is c...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Growing Science
2023-01-01
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| Series: | Decision Science Letters |
| Online Access: | http://www.growingscience.com/dsl/Vol12/dsl_2023_7.pdf |
| _version_ | 1797865865306177536 |
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| author | Yuyun Hidayat Sukono Sukono Predy Hartanto Titi Purwandari Riza Andrian Ibrahim Moch Panji Agung Saputra Jumadil Saputra |
| author_facet | Yuyun Hidayat Sukono Sukono Predy Hartanto Titi Purwandari Riza Andrian Ibrahim Moch Panji Agung Saputra Jumadil Saputra |
| author_sort | Yuyun Hidayat |
| collection | DOAJ |
| description | Giving credit to debtors can pose a default risk. This risk arises because of an error in analyzing the credit risk rate of the debtor. Therefore, this study aims to design a framework for analyzing the credit risk rate of debtors so that the default risk can be reduced. This framework is created using the integration of factor analysis and Tsukamoto’s fuzzy logic method. This integration method can group many credit assessment variables into several decisive factors. In addition, the integration method can estimate credit risk rate firmly based on the α-predicate of each basic rule. This analytical framework is simulated on credit application data at a Rural Bank, in Indonesia. The simulation results show that there are three factors and one variable to measure the credit risk rate, namely: factor 1 represents repayment capacity, business length, working capital, and liquidity value; factor 2 represents the age and the difference between the granted and the proposed loan amount; factor 3 represents the stay length, character, and credit history; and one variable represents a dependent number. This research is expected to help credit institutions measure the credit risk rate in making credit decisions for prospective debtors. |
| first_indexed | 2024-04-09T23:16:03Z |
| format | Article |
| id | doaj.art-19a8afd94f5248b08d67bc5a0ebe82ea |
| institution | Directory Open Access Journal |
| issn | 1929-5804 1929-5812 |
| language | English |
| last_indexed | 2024-04-09T23:16:03Z |
| publishDate | 2023-01-01 |
| publisher | Growing Science |
| record_format | Article |
| series | Decision Science Letters |
| spelling | doaj.art-19a8afd94f5248b08d67bc5a0ebe82ea2023-03-22T08:44:43ZengGrowing ScienceDecision Science Letters1929-58041929-58122023-01-0112226727810.5267/j.dsl.2023.1.008Integration of factor analysis and Tsukamoto’s fuzzy logic method for quality control of credit provisions in rural banksYuyun HidayatSukono SukonoPredy HartantoTiti PurwandariRiza Andrian IbrahimMoch Panji Agung SaputraJumadil Saputra Giving credit to debtors can pose a default risk. This risk arises because of an error in analyzing the credit risk rate of the debtor. Therefore, this study aims to design a framework for analyzing the credit risk rate of debtors so that the default risk can be reduced. This framework is created using the integration of factor analysis and Tsukamoto’s fuzzy logic method. This integration method can group many credit assessment variables into several decisive factors. In addition, the integration method can estimate credit risk rate firmly based on the α-predicate of each basic rule. This analytical framework is simulated on credit application data at a Rural Bank, in Indonesia. The simulation results show that there are three factors and one variable to measure the credit risk rate, namely: factor 1 represents repayment capacity, business length, working capital, and liquidity value; factor 2 represents the age and the difference between the granted and the proposed loan amount; factor 3 represents the stay length, character, and credit history; and one variable represents a dependent number. This research is expected to help credit institutions measure the credit risk rate in making credit decisions for prospective debtors.http://www.growingscience.com/dsl/Vol12/dsl_2023_7.pdf |
| spellingShingle | Yuyun Hidayat Sukono Sukono Predy Hartanto Titi Purwandari Riza Andrian Ibrahim Moch Panji Agung Saputra Jumadil Saputra Integration of factor analysis and Tsukamoto’s fuzzy logic method for quality control of credit provisions in rural banks Decision Science Letters |
| title | Integration of factor analysis and Tsukamoto’s fuzzy logic method for quality control of credit provisions in rural banks |
| title_full | Integration of factor analysis and Tsukamoto’s fuzzy logic method for quality control of credit provisions in rural banks |
| title_fullStr | Integration of factor analysis and Tsukamoto’s fuzzy logic method for quality control of credit provisions in rural banks |
| title_full_unstemmed | Integration of factor analysis and Tsukamoto’s fuzzy logic method for quality control of credit provisions in rural banks |
| title_short | Integration of factor analysis and Tsukamoto’s fuzzy logic method for quality control of credit provisions in rural banks |
| title_sort | integration of factor analysis and tsukamoto s fuzzy logic method for quality control of credit provisions in rural banks |
| url | http://www.growingscience.com/dsl/Vol12/dsl_2023_7.pdf |
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