An optimised credit scorecard to enhance cut-off score determination
Background: Credit scoring is a statistical tool allowing banks to distinguish between good and bad clients. However, literature in the world of credit scoring is limited. In this article parametric and non-parametric statistical techniques that are used in credit scoring are reviewed. Aim: To b...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
AOSIS
2018-06-01
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| Series: | South African Journal of Economic and Management Sciences |
| Subjects: | |
| Online Access: | https://sajems.org/index.php/sajems/article/view/1571 |
| _version_ | 1818096596943896576 |
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| author | Nico Kritzinger Gary W. van Vuuren |
| author_facet | Nico Kritzinger Gary W. van Vuuren |
| author_sort | Nico Kritzinger |
| collection | DOAJ |
| description | Background: Credit scoring is a statistical tool allowing banks to distinguish between good and bad clients. However, literature in the world of credit scoring is limited. In this article parametric and non-parametric statistical techniques that are used in credit scoring are reviewed.
Aim: To build an optimal credit scoring matrix model to predict which clients will go bad in the future. This article also illustrates the use of the credit scoring matrix model to determine an appropriate cut-off score on a more granular level.
Setting: Data used in this article are based on a bank in South Africa and are Retail Banking specific.
Methods: The methods used in this article were regression, statistical analysis, matrix and comparative study.
Results: The matrix provides uplift in the Gini-coefficient when compared to a one-dimensional model and provides greater granularity when setting the appropriate cut-off.
Conclusion: The article provides steps to construct a credit scoring matrix model to optimise separation between good and bad clients. An added contribution of the article is the manner in which the credit scoring matrix model provides a greater granularity option for establishing the cut-off score for accepting clients, more appropriately than a one-dimensional scorecard. |
| first_indexed | 2024-12-10T23:07:09Z |
| format | Article |
| id | doaj.art-e17a0e3227bc44bba62a9fee1e734526 |
| institution | Directory Open Access Journal |
| issn | 1015-8812 2222-3436 |
| language | English |
| last_indexed | 2024-12-10T23:07:09Z |
| publishDate | 2018-06-01 |
| publisher | AOSIS |
| record_format | Article |
| series | South African Journal of Economic and Management Sciences |
| spelling | doaj.art-e17a0e3227bc44bba62a9fee1e7345262022-12-22T01:30:02ZengAOSISSouth African Journal of Economic and Management Sciences1015-88122222-34362018-06-01211e1e1510.4102/sajems.v21i1.1571673An optimised credit scorecard to enhance cut-off score determinationNico Kritzinger0Gary W. van Vuuren1Department of Business Mathematics and Informatics (BMI), Faculty of Natural Sciences, North-West UniversityDepartment of Business Mathematics and Informatics (BMI), Faculty of Natural Sciences, North-West UniversityBackground: Credit scoring is a statistical tool allowing banks to distinguish between good and bad clients. However, literature in the world of credit scoring is limited. In this article parametric and non-parametric statistical techniques that are used in credit scoring are reviewed. Aim: To build an optimal credit scoring matrix model to predict which clients will go bad in the future. This article also illustrates the use of the credit scoring matrix model to determine an appropriate cut-off score on a more granular level. Setting: Data used in this article are based on a bank in South Africa and are Retail Banking specific. Methods: The methods used in this article were regression, statistical analysis, matrix and comparative study. Results: The matrix provides uplift in the Gini-coefficient when compared to a one-dimensional model and provides greater granularity when setting the appropriate cut-off. Conclusion: The article provides steps to construct a credit scoring matrix model to optimise separation between good and bad clients. An added contribution of the article is the manner in which the credit scoring matrix model provides a greater granularity option for establishing the cut-off score for accepting clients, more appropriately than a one-dimensional scorecard.https://sajems.org/index.php/sajems/article/view/1571credit riskcredit scoringcredit risk management |
| spellingShingle | Nico Kritzinger Gary W. van Vuuren An optimised credit scorecard to enhance cut-off score determination South African Journal of Economic and Management Sciences credit risk credit scoring credit risk management |
| title | An optimised credit scorecard to enhance cut-off score determination |
| title_full | An optimised credit scorecard to enhance cut-off score determination |
| title_fullStr | An optimised credit scorecard to enhance cut-off score determination |
| title_full_unstemmed | An optimised credit scorecard to enhance cut-off score determination |
| title_short | An optimised credit scorecard to enhance cut-off score determination |
| title_sort | optimised credit scorecard to enhance cut off score determination |
| topic | credit risk credit scoring credit risk management |
| url | https://sajems.org/index.php/sajems/article/view/1571 |
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