{"title":"信用评分:绩效与公平","authors":"Stefania Albanesi, Domonkos F. Vamossy","doi":"arxiv-2409.00296","DOIUrl":null,"url":null,"abstract":"Credit scores are critical for allocating consumer debt in the United States,\nyet little evidence is available on their performance. We benchmark a widely\nused credit score against a machine learning model of consumer default and find\nsignificant misclassification of borrowers, especially those with low scores.\nOur model improves predictive accuracy for young, low-income, and minority\ngroups due to its superior performance with low quality data, resulting in a\ngain in standing for these populations. Our findings suggest that improving\ncredit scoring performance could lead to more equitable access to credit.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"58 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Credit Scores: Performance and Equity\",\"authors\":\"Stefania Albanesi, Domonkos F. Vamossy\",\"doi\":\"arxiv-2409.00296\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Credit scores are critical for allocating consumer debt in the United States,\\nyet little evidence is available on their performance. We benchmark a widely\\nused credit score against a machine learning model of consumer default and find\\nsignificant misclassification of borrowers, especially those with low scores.\\nOur model improves predictive accuracy for young, low-income, and minority\\ngroups due to its superior performance with low quality data, resulting in a\\ngain in standing for these populations. Our findings suggest that improving\\ncredit scoring performance could lead to more equitable access to credit.\",\"PeriodicalId\":501273,\"journal\":{\"name\":\"arXiv - ECON - General Economics\",\"volume\":\"58 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-08-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - ECON - General Economics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2409.00296\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - ECON - General Economics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.00296","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.