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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.20298 |
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| _version_ | 1866915836129705984 |
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| author | Thu, Huyen Giang Thi Doan, Thang Viet Ban, Ha-Bang Quy, Tai Le |
| author_facet | Thu, Huyen Giang Thi Doan, Thang Viet Ban, Ha-Bang Quy, Tai Le |
| contents | The digitalization of credit scoring has become essential for financial institutions and commercial banks, especially in the era of digital transformation. Machine learning techniques are commonly used to evaluate customers' creditworthiness. However, the predicted outcomes of machine learning models can be biased toward protected attributes, such as race or gender. Numerous fairness-aware machine learning models and fairness measures have been proposed. Nevertheless, their performance in the context of credit scoring has not been thoroughly investigated. In this paper, we present a comprehensive experimental study of fairness-aware machine learning in credit scoring. The study explores key aspects of credit scoring, including financial datasets, predictive models, and fairness measures. We also provide a detailed evaluation of fairness-aware predictive models and fairness measures on widely used financial datasets. The experimental results show that fairness-aware models achieve a better balance between predictive accuracy and fairness compared to traditional classification models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_20298 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | An Experimental Study on Fairness-aware Machine Learning for Credit Scoring Problems Thu, Huyen Giang Thi Doan, Thang Viet Ban, Ha-Bang Quy, Tai Le Machine Learning Computers and Society The digitalization of credit scoring has become essential for financial institutions and commercial banks, especially in the era of digital transformation. Machine learning techniques are commonly used to evaluate customers' creditworthiness. However, the predicted outcomes of machine learning models can be biased toward protected attributes, such as race or gender. Numerous fairness-aware machine learning models and fairness measures have been proposed. Nevertheless, their performance in the context of credit scoring has not been thoroughly investigated. In this paper, we present a comprehensive experimental study of fairness-aware machine learning in credit scoring. The study explores key aspects of credit scoring, including financial datasets, predictive models, and fairness measures. We also provide a detailed evaluation of fairness-aware predictive models and fairness measures on widely used financial datasets. The experimental results show that fairness-aware models achieve a better balance between predictive accuracy and fairness compared to traditional classification models. |
| title | An Experimental Study on Fairness-aware Machine Learning for Credit Scoring Problems |
| topic | Machine Learning Computers and Society |
| url | https://arxiv.org/abs/2412.20298 |