用机器学习改进信用评分，让低收入少数群体更公平获得贷款。
Credit Scores: Performance and Equity
- 用机器学习模型对比传统信用评分，发现低分人群误判严重。
- 新模型对年轻、低收入、少数族裔群体预测准确率显著提升。
- 适合关注金融公平与信用算法优化的研究者和政策制定者。
信用评分在美国消费者信贷分配中至关重要，但其实际表现证据有限。本文将一种广泛使用的信用评分与基于消费者违约的机器学习模型进行基准对比，发现对低分借款人的误分类问题显著，尤其在年轻、低收入及少数族裔群体中更为突出。由于该机器学习模型在低质量数据下仍具优异性能，使这些群体的信用评级得到明显改善。结果表明，提升信用评分的准确性有望实现更公平的信贷获取。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。