【摘要】 数字经济时代新技术的发展，使数据的储存、共享和优化都更加便利。随着国内对民间征信市场的开放，民间征信公司和金融科技平台已经使用“另类数据”对用户进行行为分析和预测。机器学习下的另类数据征信，虽然能弥补传统征信中存在的数据片面等缺陷，缓解信贷歧视，帮助因信贷历史不足等征信因素和歧视因素导致借款失败的弱势金融群体，提高金融普惠程度，但仍然存在阻碍信贷公平实现的歧视因素。通过构建金融消费者和网贷平台的完全信息静态博弈模型，发现对另类数据的保护和应用进行监管和立法，对金融科技行业进行规范十分必要。更多还原

【Abstract】 The development of new technologies in the digital economy makes it easier to store,share and optimize data. With the opening of the private credit investigation market in China,private credit investigation companies and fintech platforms have used “alternative data” to conduct behavior analysis and prediction of users.The paper based on P2P network credit platform from the United States found that the big data credit investigation under the machine learning can make up for the traditional credit reporting data of partial defects,such as ease credit discrimination,help the weak financial group due to insufficient credit history and increase the degree of financial inclusion. Nevertheless there are still discriminatory factors that hinder the realization of credit fairness.By constructing a static game model of complete information between financial consumers and online loan platforms,it is found that it is necessary to regulate and legislate the protection and application of alternative data and regulate the fintech industry.更多还原