The start-ups’ methods vary, as do the data sources they tap. But their algorithms siftthrough data that can include a person’s social-network connections, web-browsing habits,how they fill out online forms and their online purchases.
这些初创公司的方法各异,利用的数据源也不尽相同。不过,它们用来筛选数据的算法可能会涵盖个人在社交网络上的关系、浏览网页的习惯、填写网上表格的方式,以及网上购物的偏好。
The software looks for patterns and correlations: digital signals that help assess an individual’swillingness and ability to repay. The picture that emerges from the data, enthusiasts say,should result in more accurate risk analysis, thus opening the door to extending consumercredit to millions more people at lower cost.
这种软件寻找的是模式与相关性,即有助于评估一个人的偿还意愿和能力的数字信号。追捧者认为,数据勾勒出来的面貌,应该可以让风险分析变得更加精准,因此有助于以更低的成本把消费者信贷提供给额外的人,而其中涉及的人数成百上千万。
Yet public policy experts say the enthusiasm for the new lending models is outrunning theevidence. The accuracy and fairness of big data credit technology is unproven, said AaronRieke, a former lawyer for the Federal Trade Commission and director of technology projectsfor Upturn, a policy consulting firm. Mr. Rieke was a co-author of a report last year, supportedby the Ford Foundation, that cited ZestFinance as a prime example of big data underwriting,which deploys “fringe alternative scoring models.
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