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

主 讲 人：美国波士顿大学 顾彬 教授

讲座时间：2022年10月28日（周五）上午9：00

腾讯会议：570 233 071

主办单位：服务科学与服务管理研究中心、运营与供应链研究中心、中南大学“三高四新”战略研究院

主 持 人：米乐(中国) 周雄伟 教授

主讲人简介:

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。

讲座摘要:

Financial inclusion has been a global challenge for financial institutions due to the lack of credit history and other financial data of the underserved population. The advances in artificial intelligence (AI) and machine learning, combining with the richness of alternative data, enable financial institutions to use AI models to evaluate underserved borrowers. We investigate whether and how AI models influence financial inclusion and lending performance, as measured by approval rate, default rate, and utilization level. We cooperate with a large regional bank in China with more than 50 million customers which previously use traditional underwriting process (i.e. rules designed by experts). We take advantage of a nationwide financial policy change in China which encourages banks to use AI models in underwriting to complement their current approach. The focal bank developed an AI model for its credit line product A as a pilot and we identify a similar credit line product B which didn’t use the AI model. We apply a difference-in-differences approach to investigate the treatment effect of the AI model. We find a 1.5% decrease in the approval rate for the entire population, which was driven by a 3.5% decrease for the regular population and a 16.5% increase in the underserved population. In the meantime, the default rate for both groups decreases significantly and utilization level increases significantly. Taken together, the introduction of the AI model increases financial inclusion for the underserved population by enhancing approval rate and reducing default rate simultaneously. Further analysis attributes the improvement in financial inclusion to two factors, the inclusion of weak signals that were ignored by the human experts, and the development of more sophisticated decision models. The inclusion of weak signals contributes slightly less than one half of the improvement and the development of more sophisticated decision models contributes slightly more than one half. The inclusion of weak signals is especially helpful to the underserved population while the regular population benefits more from the development of more complicated decision models. Our study documents the impacts of AI models on financial inclusion and provide the first piece of evidence regarding the underlying mechanisms, helping financial institutions and regulators develop a better understanding on how AI models can complement traditional models in improving financial inclusion.