The Fairness of Credit Scoring Models
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2022
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| _version_ | 1866917584602923008 |
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| author | Hurlin, Christophe Pérignon, Christophe Saurin, Sébastien |
| author_facet | Hurlin, Christophe Pérignon, Christophe Saurin, Sébastien |
| contents | In credit markets, screening algorithms aim to discriminate between good-type and bad-type borrowers. However, when doing so, they can also discriminate between individuals sharing a protected attribute (e.g. gender, age, racial origin) and the rest of the population. This can be unintentional and originate from the training dataset or from the model itself. We show how to formally test the algorithmic fairness of scoring models and how to identify the variables responsible for any lack of fairness. We then use these variables to optimize the fairness-performance trade-off. Our framework provides guidance on how algorithmic fairness can be monitored by lenders, controlled by their regulators, improved for the benefit of protected groups, while still maintaining a high level of forecasting accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2205_10200 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | The Fairness of Credit Scoring Models Hurlin, Christophe Pérignon, Christophe Saurin, Sébastien Machine Learning Risk Management In credit markets, screening algorithms aim to discriminate between good-type and bad-type borrowers. However, when doing so, they can also discriminate between individuals sharing a protected attribute (e.g. gender, age, racial origin) and the rest of the population. This can be unintentional and originate from the training dataset or from the model itself. We show how to formally test the algorithmic fairness of scoring models and how to identify the variables responsible for any lack of fairness. We then use these variables to optimize the fairness-performance trade-off. Our framework provides guidance on how algorithmic fairness can be monitored by lenders, controlled by their regulators, improved for the benefit of protected groups, while still maintaining a high level of forecasting accuracy. |
| title | The Fairness of Credit Scoring Models |
| topic | Machine Learning Risk Management |
| url | https://arxiv.org/abs/2205.10200 |