A trust-based bio-inspired approach for credit lending decisions
Credit scoring computation essentially involves taking into account various financial factors and the previous behavior of the credit requesting person. There is a strong degree of correlation between the compliance level and the credit score of a given entity. The concept of trust has been widely u...
| Main Authors: | , , , , |
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| Format: | Journal Article |
| Published: |
Springer Vienna
2012
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/45722 |
| _version_ | 1848757364098859008 |
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| author | Mirtalaei, M. Saberi, Morteza Hussain, Omar Ashjari, B. Hussain, Farookh Khadeer |
| author_facet | Mirtalaei, M. Saberi, Morteza Hussain, Omar Ashjari, B. Hussain, Farookh Khadeer |
| author_sort | Mirtalaei, M. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Credit scoring computation essentially involves taking into account various financial factors and the previous behavior of the credit requesting person. There is a strong degree of correlation between the compliance level and the credit score of a given entity. The concept of trust has been widely used and applied in the existing literature to determine the compliance level of an entity. However it has not been studied in the context of credit scoring literature. In order to address this shortcoming, in this paper we propose a six-step bio-inspired methodology for trust-based credit lending decisions by credit institutions. The proposed methodology makes use of an artificial neural network-based model to classify the (potential) customers into various categories. To show the applicability and superiority of the proposed algorithm, it is applied to a credit-card dataset obtained from the UCI repository. |
| first_indexed | 2025-11-14T09:26:55Z |
| format | Journal Article |
| id | curtin-20.500.11937-45722 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:26:55Z |
| publishDate | 2012 |
| publisher | Springer Vienna |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-457222017-09-13T16:00:28Z A trust-based bio-inspired approach for credit lending decisions Mirtalaei, M. Saberi, Morteza Hussain, Omar Ashjari, B. Hussain, Farookh Khadeer bio-inspired trust credit scoring artificial neural network Credit scoring computation essentially involves taking into account various financial factors and the previous behavior of the credit requesting person. There is a strong degree of correlation between the compliance level and the credit score of a given entity. The concept of trust has been widely used and applied in the existing literature to determine the compliance level of an entity. However it has not been studied in the context of credit scoring literature. In order to address this shortcoming, in this paper we propose a six-step bio-inspired methodology for trust-based credit lending decisions by credit institutions. The proposed methodology makes use of an artificial neural network-based model to classify the (potential) customers into various categories. To show the applicability and superiority of the proposed algorithm, it is applied to a credit-card dataset obtained from the UCI repository. 2012 Journal Article http://hdl.handle.net/20.500.11937/45722 10.1007/s00607-012-0190-3 Springer Vienna restricted |
| spellingShingle | bio-inspired trust credit scoring artificial neural network Mirtalaei, M. Saberi, Morteza Hussain, Omar Ashjari, B. Hussain, Farookh Khadeer A trust-based bio-inspired approach for credit lending decisions |
| title | A trust-based bio-inspired approach for credit lending decisions |
| title_full | A trust-based bio-inspired approach for credit lending decisions |
| title_fullStr | A trust-based bio-inspired approach for credit lending decisions |
| title_full_unstemmed | A trust-based bio-inspired approach for credit lending decisions |
| title_short | A trust-based bio-inspired approach for credit lending decisions |
| title_sort | trust-based bio-inspired approach for credit lending decisions |
| topic | bio-inspired trust credit scoring artificial neural network |
| url | http://hdl.handle.net/20.500.11937/45722 |