Baidu does underwriting for consumers with limited credit history using machine learning

Solution:

Unlike traditional underwriting methods, ZAML uses machine learning to analyze tens of thousands of nontraditional and traditional variables to more accurately score borrowers, including thin-file and no-file borrowers. ZAML can analyze vast amounts of data they already have in-house, such as customer support data, payment histories, and purchase transactions. The platform can also add traditional credit information and nontraditional credit variables, such as how a customer fills out a form, how they navigate a lender’s site, and more.

Baidu, the leading Internet search provider in China, partnered with ZestFinance in its effort to turn Baidu's search, location, and payment data into credit scores. They are using this data to assess credit worthiness of Chinese millennials and other thin file customers.