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1860796950523150336
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INTELEK Repository
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Online Access
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https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072
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2024-10-02 15:24
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Restricted Document
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10801
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UniSZA
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[1] Agresti, A., Categorical Data Analysis, New York: John Wiley & Sons, Inc., (2002). [2] Bartolozzi, E., Cornford, M., Leticia G.E., Deoc´on, C.P., Vasquez, O.I., Plaza, F.J., Credit Scoring Modelling for Retail Banking Sector, Working Paper, II Modelling Week, Universidad Complutense de Madrid,16th - 24th June (2008). [3] Bolton, C., Logistic Regression and Its Application in Credit Scoring, Disertation, Universiteit of Pretoria, (2009). [4] Czepiel, S.A., Maximum Likelihood Estimation of Logistic Regression Models: Theory and Implementation,(2005). (On Line). [5] Emel, A.B., Oral, M., Reisman, A., Yolalan, R., A credit scoring approach for the commercial banking sector, Socio-Economic Planning Sciences 37 (2003) 103–123, (2003). [6] Feelders, A.J., Credit Scoring and Reject Inference With Mixture Models, Working Paper, Tilburg University, The Netherlands, Copyright © 2000 John Wiley & Sons, Ltd., (2000). [7] Goldberg, D.E., Genetic Algorithms in Search Optimization and Machine Learning, USA: Addison Westley, (1989). [8] Hans Dellien, H., Schreiner, M., Credit Scoring, Banks, and Microfinance: Balancing High-Tech with High-Touch, Working Paper, Women’s World Banking, 8 West 40th Street, 9th Floor, New York, NY 10018, U.S.A., (2005). [9] Hosmer, D.W., Lemeshow, S., Applied Logistic Regression, Canada : John Wiley & Sons, Inc., (1989). [10] Hermanto, D. Genetic Algorithm and Application Example (Algoritma Genetik dan Contoh Aplikasinya). Working Paper. (2007). http://dennyhermanto.webhop.org. [11] Islam, S., Zhou, L., Li, F., Application of Artificial Intelligence (Artificial Neural Network) to Assess Credit Risk: A Predictive Model For Credit Card Scoring, Thesis for the Degree of MSc in Business Administration, Spring (2009). [12] Koh, H.C., Tan, W.C., Goh, C.P., A Two-step Method to Construct Credit Scoring Models with Data Mining Techniques, International Journal of Business and Information, Volume 1 Number 1, 2006 pp 96- 118, (2006). [13] Lahsasna, A., Ainon, R.N., Wah, T.Y., Credit Scoring Models Using Soft Computing Methods: A Survey, The International Arab Journal of Information Technology, Vol. 7, No. 2, April (2010). [14] Thanh, D.T.H., Kleimeier, S., Credit Scoring for Vietnam’s Retail Banking Market: Implementation and Implications for Transactional versus Relationship Lending, Working Paper, Limburg Institute of Financial Economics (LIFE), March 15, (2006). [15] Wu, X., Credit Scoring Model Validation, Master Thesis, Faculty of Science, Korteweg-de Vries Institute for Mathematics, Universiteit Van Amesterdam, June 29, (2008).
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https://intelek.unisza.edu.my/intelek/pages/view.php?ref=10801
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10801 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=10801 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 Restricted Document Article Journal image/jpeg inches 96 96 627 2024-10-02 15:24 1341 1341x627 4932-01-FH02-FIK-14-00726.jpg UniSZA Private Access Credit scoring for Cooperative of financial services using logistic regression estimated by genetic algorithm Applied Mathematical Sciences One of the Cooperative of Financial Services is disbursed loans to debtors (members and prospective members). In lending (provision of credit) is likely to arise the problem, namely the possibility of debt default by the debtor. To anticipate the risk of default (credit risk), to prospective debtors applying for credit risk analysis was performed using credit scoring. In this paper the analysis of credit scoring is done using logistic regression model, which is estimated using genetic algorithms. As a numerical illustration, the method used to analyze the credit scoring on a cooperative of financial services in Indonesia. Of the eight factors were analyzed, it was only six factors that significantly influence to the risk of default. Six of these factors include: number of dependents, the amount of savings, the value of collateral, monthly income, credit limit is realized, and the loan repayment period. 8 1 45-57 [1] Agresti, A., Categorical Data Analysis, New York: John Wiley & Sons, Inc., (2002). [2] Bartolozzi, E., Cornford, M., Leticia G.E., Deoc´on, C.P., Vasquez, O.I., Plaza, F.J., Credit Scoring Modelling for Retail Banking Sector, Working Paper, II Modelling Week, Universidad Complutense de Madrid,16th - 24th June (2008). [3] Bolton, C., Logistic Regression and Its Application in Credit Scoring, Disertation, Universiteit of Pretoria, (2009). [4] Czepiel, S.A., Maximum Likelihood Estimation of Logistic Regression Models: Theory and Implementation,(2005). (On Line). [5] Emel, A.B., Oral, M., Reisman, A., Yolalan, R., A credit scoring approach for the commercial banking sector, Socio-Economic Planning Sciences 37 (2003) 103–123, (2003). [6] Feelders, A.J., Credit Scoring and Reject Inference With Mixture Models, Working Paper, Tilburg University, The Netherlands, Copyright © 2000 John Wiley & Sons, Ltd., (2000). [7] Goldberg, D.E., Genetic Algorithms in Search Optimization and Machine Learning, USA: Addison Westley, (1989). [8] Hans Dellien, H., Schreiner, M., Credit Scoring, Banks, and Microfinance: Balancing High-Tech with High-Touch, Working Paper, Women’s World Banking, 8 West 40th Street, 9th Floor, New York, NY 10018, U.S.A., (2005). [9] Hosmer, D.W., Lemeshow, S., Applied Logistic Regression, Canada : John Wiley & Sons, Inc., (1989). [10] Hermanto, D. Genetic Algorithm and Application Example (Algoritma Genetik dan Contoh Aplikasinya). Working Paper. (2007). http://dennyhermanto.webhop.org. [11] Islam, S., Zhou, L., Li, F., Application of Artificial Intelligence (Artificial Neural Network) to Assess Credit Risk: A Predictive Model For Credit Card Scoring, Thesis for the Degree of MSc in Business Administration, Spring (2009). [12] Koh, H.C., Tan, W.C., Goh, C.P., A Two-step Method to Construct Credit Scoring Models with Data Mining Techniques, International Journal of Business and Information, Volume 1 Number 1, 2006 pp 96- 118, (2006). [13] Lahsasna, A., Ainon, R.N., Wah, T.Y., Credit Scoring Models Using Soft Computing Methods: A Survey, The International Arab Journal of Information Technology, Vol. 7, No. 2, April (2010). [14] Thanh, D.T.H., Kleimeier, S., Credit Scoring for Vietnam’s Retail Banking Market: Implementation and Implications for Transactional versus Relationship Lending, Working Paper, Limburg Institute of Financial Economics (LIFE), March 15, (2006). [15] Wu, X., Credit Scoring Model Validation, Master Thesis, Faculty of Science, Korteweg-de Vries Institute for Mathematics, Universiteit Van Amesterdam, June 29, (2008).
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| spellingShingle |
Credit scoring for Cooperative of financial services using logistic regression estimated by genetic algorithm
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| summary |
One of the Cooperative of Financial Services is disbursed loans to debtors (members and prospective members). In lending (provision of credit) is likely to arise the problem, namely the possibility of debt default by the debtor. To anticipate the risk of default (credit risk), to prospective debtors applying for credit risk analysis was performed using credit scoring. In this paper the analysis of credit scoring is done using logistic regression model, which is estimated using genetic algorithms. As a numerical illustration, the method used to analyze the credit scoring on a cooperative of financial services in Indonesia. Of the eight factors were analyzed, it was only six factors that significantly influence to the risk of default. Six of these factors include: number of dependents, the amount of savings, the value of collateral, monthly income, credit limit is realized, and the loan repayment period.
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| title |
Credit scoring for Cooperative of financial services using logistic regression estimated by genetic algorithm
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| title_full |
Credit scoring for Cooperative of financial services using logistic regression estimated by genetic algorithm
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| title_fullStr |
Credit scoring for Cooperative of financial services using logistic regression estimated by genetic algorithm
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| title_full_unstemmed |
Credit scoring for Cooperative of financial services using logistic regression estimated by genetic algorithm
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| title_short |
Credit scoring for Cooperative of financial services using logistic regression estimated by genetic algorithm
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| title_sort |
credit scoring for cooperative of financial services using logistic regression estimated by genetic algorithm
|