Credit Scores: Performance and Equity
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866917765596577792 |
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| author | Albanesi, Stefania Vamossy, Domonkos F. |
| author_facet | Albanesi, Stefania Vamossy, Domonkos F. |
| contents | Credit scores are critical for allocating consumer debt in the United States, yet little evidence is available on their performance. We benchmark a widely used credit score against a machine learning model of consumer default and find significant misclassification of borrowers, especially those with low scores. Our model improves predictive accuracy for young, low-income, and minority groups due to its superior performance with low quality data, resulting in a gain in standing for these populations. Our findings suggest that improving credit scoring performance could lead to more equitable access to credit. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_00296 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Credit Scores: Performance and Equity Albanesi, Stefania Vamossy, Domonkos F. Risk Management Machine Learning General Economics Economics Computational Finance Credit scores are critical for allocating consumer debt in the United States, yet little evidence is available on their performance. We benchmark a widely used credit score against a machine learning model of consumer default and find significant misclassification of borrowers, especially those with low scores. Our model improves predictive accuracy for young, low-income, and minority groups due to its superior performance with low quality data, resulting in a gain in standing for these populations. Our findings suggest that improving credit scoring performance could lead to more equitable access to credit. |
| title | Credit Scores: Performance and Equity |
| topic | Risk Management Machine Learning General Economics Economics Computational Finance |
| url | https://arxiv.org/abs/2409.00296 |