A Model Fusion Approach for Enhancing Credit Approval Decision Making
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| _version_ | 1866911384081530880 |
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| author | Wu, Yuanhong Xu, Jingyan Ye, Wei Schweikert, Christina Hsu, D. Frank |
| author_facet | Wu, Yuanhong Xu, Jingyan Ye, Wei Schweikert, Christina Hsu, D. Frank |
| contents | Credit default poses significant challenges to financial institutions and consumers, resulting in substantial financial losses and diminished trust. As such, credit default risk management has been a critical topic in the financial industry. In this paper, we present Combinatorial Fusion Analysis (CFA), a model fusion framework, that combines multiple machine learning algorithms to detect and predict credit card approval with high accuracy. We present the design methodology and implementation using five pre-trained models. The CFA results show an accuracy of 89.13% which is better than conventional machine learning and ensemble methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_12684 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Model Fusion Approach for Enhancing Credit Approval Decision Making Wu, Yuanhong Xu, Jingyan Ye, Wei Schweikert, Christina Hsu, D. Frank Computational Engineering, Finance, and Science Credit default poses significant challenges to financial institutions and consumers, resulting in substantial financial losses and diminished trust. As such, credit default risk management has been a critical topic in the financial industry. In this paper, we present Combinatorial Fusion Analysis (CFA), a model fusion framework, that combines multiple machine learning algorithms to detect and predict credit card approval with high accuracy. We present the design methodology and implementation using five pre-trained models. The CFA results show an accuracy of 89.13% which is better than conventional machine learning and ensemble methods. |
| title | A Model Fusion Approach for Enhancing Credit Approval Decision Making |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2601.12684 |