时间：2024年5月28日上午8:30

地点：经管楼313室

主讲人：顾彬 教授，美国波士顿大学

题目：The Effect of AI-Enabled Credit Scoring on Financial Inclusion: Evidence from One Million Underserved Population  

摘要：We studied the effect of adopting an AI-enabled credit scoring model by a major bank on financial inclusion as measured by changes to the approval rate, default rate, and utilization level of a personal loan product for the underserved population. The bank served over 50 million customers and used a traditional rule-based model to evaluate the default risk of each loan application. It recently developed an AI model with higher prediction accuracy of default risk and used the AI model and the traditional model together in assessing loan applications for one of its personal loan products. Although the AI model could be more accurate in estimating default risk, little is known about its impact on financial inclusion. We investigated this question using a difference-in-differences approach by comparing changes in financial inclusion of the personal loan product adopting the AI model to that of a similar personal loan product without adopting the AI model. We found that the AI model enhanced financial inclusion for the underserved population by simultaneously increasing the approval rate and reducing the default rate. Further analysis attributed the enhancement in financial inclusion to the use of weak signals (i.e., data not conventionally used to evaluate creditworthiness) by the AI model and its sophisticated machine learning algorithms. Our finding is consistent with the statistical discrimination theory, as the use of weak signals and sophisticated machine learning algorithms improves prediction accuracy at the individual level, thus reducing the reliance on group characteristics that often lead to financial exclusion. We elaborated on the development process of the AI model to illustrate how and why the AI model can better evaluate the underserved population. We also found the impacts of the AI model heterogeneous across subgroups, and those with missing weak signals saw smaller improvements in the approval rate. A simulation-based analysis showed that simplified AI models could still increase the approval rate and reduce the default rate of the underserved population. We further discussed the compliance and generalizability issues about using AI and privacy-sensitive data in credit scoring. Our findings provided rich theoretical and practical implications for social justice by documenting how an AI model designed for improving prediction accuracy can enhance financial inclusion.

个人简介：顾彬教授是波士顿大学Questrom商学院的Everett W. Lord杰出学院学者、教授和信息系统系主任。他的研究兴趣包括未来工作、金融科技、在线社交媒体和社交网络、数字平台、共享经济以及数据分析和人工智能的社会/商业价值。他的研究成果发表在Management Science, MIS Quarterly, Production and Operations Management, Information Systems Research, Journal of Management Information Systems等杂志上。担任或曾任Information Systems Research和MIS Quarterly编辑委员会的高级编辑，并担任主要信息系统会议的联席主席、分领域主席或副编辑。于2002年获得宾夕法尼亚大学沃顿商学院博士学位。