Improving the Allocation Consumer Credit with Machine Learning

通过机器学习改善消费信贷配置

• 批准号: 2018245
• 负责人: Stefania Albanesi
• 金额: $ 19万
• 依托单位: National Bureau of Economic Research Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2018245&HistoricalAwards=false
• 关键词: Improving; Allocation; Consumer; Credit; Machine

AbstractThe project will examine the accuracy and fairness of credit scores to improve the allocation of consumer credit and mitigate disparities for disadvantaged groups, such as young and minority borrowers. The research will implement a machine learning approach that is at the frontier of computer science to explore this economic question. The research will investigate the notion that there need not be a trade-off between accuracy of credit scores and a more equitable allocation of loans. The project will further look at factors related to credit conditions and default behavior for disadvantaged groups. The resulting findings will generate direct and actionable implications for policies and regulations pertaining to consumer debt to help reduce inequality and improve welfare.The project will introduce pioneering advances in constrained machine learning to develop predictions of consumer default that are fair such that the credit allocation does not penalize borrowers in disadvantaged groups, such as young or minority borrowers. The notion of fairness is grounded in economic theory, measurable in the data, and has an intuitive interpretation. Constrained machine learning is at the frontier of computer science, and this project will apply these techniques in examining this economic issue. The project will isolate the most important factors associated with default and how they vary over time for different subpopulations. The results of the project will be useful in designing policies on consumer finance that enables equitable allocation of credit that improves welfare.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目将检查信用评分的准确性和公平性，以改善消费信贷的分配，并减轻弱势群体，如年轻人和少数民族借款人的差距。该研究将采用计算机科学前沿的机器学习方法来探索这一经济问题。 这项研究将调查的概念，有没有必要之间的权衡准确性的信用评分和更公平的分配贷款。该项目将进一步研究与弱势群体的信贷条件和违约行为有关的因素。研究结果将对消费者债务相关政策和法规产生直接和可操作的影响，以帮助减少不平等和改善福利。该项目将引入受约束机器学习的开创性进展，以开发公平的消费者违约预测，从而使信贷分配不会惩罚弱势群体的借款人，如年轻人或少数民族借款人。公平的概念是建立在经济理论基础上的，可以用数据来衡量，并有一个直观的解释。受约束的机器学习是计算机科学的前沿，本项目将应用这些技术来研究这一经济问题。该项目将分离与违约相关的最重要因素，以及它们如何随时间推移而变化。该项目的成果将有助于制定消费者金融政策，使信贷的公平分配，提高福利。该奖项反映了NSF的法定使命，并已被认为是值得支持的评价使用基金会的知识价值和更广泛的影响审查标准。