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An Examination of the Conceptual Issues Involved in Developing Credit-scoring Models

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  • Reichert, Alan K
  • Cho, Chien-Ching
  • Wagner, George M

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  • Reichert, Alan K & Cho, Chien-Ching & Wagner, George M, 1983. "An Examination of the Conceptual Issues Involved in Developing Credit-scoring Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 1(2), pages 101-114, April.
    • Handle: RePEc:bes:jnlbes:v:1:y:1983:i:2:p:101-14
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    Citations

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    Cited by:

    1. Li Gan & Roberto Mosquera, 2008. "An Empirical Study of the Credit Market with Unobserved Consumer Typers," NBER Working Papers 13873, National Bureau of Economic Research, Inc.
    2. Bastos, Joao, 2007. "Credit scoring with boosted decision trees," MPRA Paper 8034, University Library of Munich, Germany.
    3. Lu Gao & Kanshukan Rajaratnam & Peter Beling, 2016. "Loan origination decisions using a multinomial scorecard," Annals of Operations Research, Springer, vol. 243(1), pages 199-210, August.
    4. Ha Thu Nguyen, 2016. "Reject inference in application scorecards: evidence from France," Working Papers hal-04141601, HAL.
    5. Thomas, Lyn C., 2000. "A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers," International Journal of Forecasting, Elsevier, vol. 16(2), pages 149-172.
    6. Ha-Thu Nguyen, 2016. "Reject inference in application scorecards: evidence from France," EconomiX Working Papers 2016-10, University of Paris Nanterre, EconomiX.
    7. Hazar Altinbas & Goktug Cenk Akkaya, 2017. "Improving the performance of statistical learning methods with a combined meta-heuristic for consumer credit risk assessment," Risk Management, Palgrave Macmillan, vol. 19(4), pages 255-280, November.
    8. Crook, Jonathan & Banasik, John, 2004. "Does reject inference really improve the performance of application scoring models?," Journal of Banking & Finance, Elsevier, vol. 28(4), pages 857-874, April.
    9. Pérez-Martín, A. & Pérez-Torregrosa, A. & Vaca, M., 2018. "Big Data techniques to measure credit banking risk in home equity loans," Journal of Business Research, Elsevier, vol. 89(C), pages 448-454.
    10. Ahmed Almustfa Hussin Adam Khatir & Marco Bee, 2022. "Machine Learning Models and Data-Balancing Techniques for Credit Scoring: What Is the Best Combination?," Risks, MDPI, vol. 10(9), pages 1-22, August.
    11. João A. Bastos, 2022. "Predicting Credit Scores with Boosted Decision Trees," Forecasting, MDPI, vol. 4(4), pages 1-11, November.
    12. Sanjeev Mittal & Pankaj Gupta & K. Jain, 2011. "Neural network credit scoring model for micro enterprise financing in India," Qualitative Research in Financial Markets, Emerald Group Publishing Limited, vol. 3(3), pages 224-242, October.
    13. Mark Schreiner, 2001. "A Scoring Model of the Risk of Costly Arrears at a Microfinance Lender in Bolivia," Development and Comp Systems 0109005, University Library of Munich, Germany.
    14. Indu Singh & D. P. Kothari & S. Aditya & Mihir Rajora & Charu Agarwal & Vibhor Gautam, 2024. "A hybrid metaheuristic optimised ensemble classifier with self organizing map clustering for credit scoring," Operational Research, Springer, vol. 24(4), pages 1-42, December.
    15. Lee, Tian-Shyug & Chiu, Chih-Chou & Chou, Yu-Chao & Lu, Chi-Jie, 2006. "Mining the customer credit using classification and regression tree and multivariate adaptive regression splines," Computational Statistics & Data Analysis, Elsevier, vol. 50(4), pages 1113-1130, February.
    16. José Carlos Trejo-García & Miguel Ángel Martínez-García & Francisco Venegas-Martínez, 2017. "Credit risk management at retail in Mexico: An econometric improvement in the selection of variables and changes in their characteristics," Contaduría y Administración, Accounting and Management, vol. 62(2), pages 13-14, Abril-Jun.
    17. Chi-Feng Peng & Li-Hsing Ho & Sang-Bing Tsai & Yin-Cheng Hsiao & Yuming Zhai & Quan Chen & Li-Chung Chang & Zhiwen Shang, 2017. "Applying the Mahalanobis–Taguchi System to Improve Tablet PC Production Processes," Sustainability, MDPI, vol. 9(9), pages 1-17, September.

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