NOTE: non-parametric oversampling technique for explainable credit scoring
Abstract Credit scoring models are critical for financial institutions to assess borrower risk and maintain profitability. Although machine learning models have improved credit scoring accuracy, imbalanced class distributions remain a major challenge. The widely used Synthetic Minority Oversampling...
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Nature Portfolio
2024-10-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-78055-5 |
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| author | Seongil Han Haemin Jung Paul D. Yoo Alessandro Provetti Andrea Cali |
| author_facet | Seongil Han Haemin Jung Paul D. Yoo Alessandro Provetti Andrea Cali |
| author_sort | Seongil Han |
| collection | DOAJ |
| description | Abstract Credit scoring models are critical for financial institutions to assess borrower risk and maintain profitability. Although machine learning models have improved credit scoring accuracy, imbalanced class distributions remain a major challenge. The widely used Synthetic Minority Oversampling TEchnique (SMOTE) struggles with high-dimensional, non-linear data and may introduce noise through class overlap. Generative Adversarial Networks (GANs) have emerged as an alternative, offering the ability to model complex data distributions. Conditional Wasserstein GANs (cWGANs) have shown promise in handling both numerical and categorical features in credit scoring datasets. However, research on extracting latent features from non-linear data and improving model explainability remains limited. To address these challenges, this paper introduces the Non-parametric Oversampling Technique for Explainable credit scoring (NOTE). The NOTE offers a unified approach that integrates a Non-parametric Stacked Autoencoder (NSA) for capturing non-linear latent features, cWGAN for oversampling the minority class, and a classification process designed to enhance explainability. The experimental results demonstrate that NOTE surpasses state-of-the-art oversampling techniques by improving classification accuracy and model stability, particularly in non-linear and imbalanced credit scoring datasets, while also enhancing the explainability of the results. |
| format | Article |
| id | doaj-art-6f51d7d4f3b44c48a47e17c0fd3ddfc0 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-6f51d7d4f3b44c48a47e17c0fd3ddfc02025-08-20T02:18:24ZengNature PortfolioScientific Reports2045-23222024-10-0114111810.1038/s41598-024-78055-5NOTE: non-parametric oversampling technique for explainable credit scoringSeongil Han0Haemin Jung1Paul D. Yoo2Alessandro Provetti3Andrea Cali4School of Computing & Mathematical Sciences, University of London, Birkbeck CollegeDepartment of Industrial & Management Engineering, Korea National University of TransportationSchool of Computing & Mathematical Sciences, University of London, Birkbeck CollegeSchool of Computing & Mathematical Sciences, University of London, Birkbeck CollegeSchool of Computing & Mathematical Sciences, University of London, Birkbeck CollegeAbstract Credit scoring models are critical for financial institutions to assess borrower risk and maintain profitability. Although machine learning models have improved credit scoring accuracy, imbalanced class distributions remain a major challenge. The widely used Synthetic Minority Oversampling TEchnique (SMOTE) struggles with high-dimensional, non-linear data and may introduce noise through class overlap. Generative Adversarial Networks (GANs) have emerged as an alternative, offering the ability to model complex data distributions. Conditional Wasserstein GANs (cWGANs) have shown promise in handling both numerical and categorical features in credit scoring datasets. However, research on extracting latent features from non-linear data and improving model explainability remains limited. To address these challenges, this paper introduces the Non-parametric Oversampling Technique for Explainable credit scoring (NOTE). The NOTE offers a unified approach that integrates a Non-parametric Stacked Autoencoder (NSA) for capturing non-linear latent features, cWGAN for oversampling the minority class, and a classification process designed to enhance explainability. The experimental results demonstrate that NOTE surpasses state-of-the-art oversampling techniques by improving classification accuracy and model stability, particularly in non-linear and imbalanced credit scoring datasets, while also enhancing the explainability of the results.https://doi.org/10.1038/s41598-024-78055-5Conditional Wasserstein generative adversarial networksStacked autoencoderExplainable AIImbalanced classOversamplingCredit scoring |
| spellingShingle | Seongil Han Haemin Jung Paul D. Yoo Alessandro Provetti Andrea Cali NOTE: non-parametric oversampling technique for explainable credit scoring Scientific Reports Conditional Wasserstein generative adversarial networks Stacked autoencoder Explainable AI Imbalanced class Oversampling Credit scoring |
| title | NOTE: non-parametric oversampling technique for explainable credit scoring |
| title_full | NOTE: non-parametric oversampling technique for explainable credit scoring |
| title_fullStr | NOTE: non-parametric oversampling technique for explainable credit scoring |
| title_full_unstemmed | NOTE: non-parametric oversampling technique for explainable credit scoring |
| title_short | NOTE: non-parametric oversampling technique for explainable credit scoring |
| title_sort | note non parametric oversampling technique for explainable credit scoring |
| topic | Conditional Wasserstein generative adversarial networks Stacked autoencoder Explainable AI Imbalanced class Oversampling Credit scoring |
| url | https://doi.org/10.1038/s41598-024-78055-5 |
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