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Now, when the idea is out there and work is in motion, it seems so natural: Web search history is really a record of personal traits, interests, work, plans and draws the most precise picture of a personality and… The level of trustworthiness, probably. Web search history is a golden ticket for search giants into banking and Chinese search juggernaut Baidu is going to cash it in with its undisclosed investment in ZestFinance, a Californian big data underwriting company.
“China interests me from a mission perspective,” ZestFinance Founder and CEO Douglas Merrill shared with TC. “There are a lot of people who deserve credit but live in a cash economy [with] no formal banking service to serve them.”
Baidu — which controls almost 80% of search market in China and has over 660 million monthly active users, — and ZestFinance are natural allies in leveraging big search data to determine the creditworthiness of Chinese consumers.
Indeed, given that only 20% of Chinese citizens have credit cards while the rest of the population uses only cash and debit cards to pay for items, China — a country with the largest population on Earth with >50% Internet penetration — and credit scoring based on Web search are meant for each other. As Merrill said, “China needs a way to provide credit in a world where there is no credit score.”
How will search history determine a credit score?
Search history contains the most private information about the person that no traditional approach can capture based on bill payments or income. Web search history is a goldmine of hints about product interests, services, hobbies and much more. Sophisticated analytical algorithms that are able to extract necessary flags out of massive unstructured search data can probably become the next big thing in microloans from e-commerce giants and in banking.
Baidu will be using ZestFinance’s underwriting technology to determine the creditworthiness of its users. Fortune cited a perfect example, explaining the determination of employment status. If an adult user is searching for video games in the middle of the day, he/she probably doesn’t have a job. In addition, if his/her previous history did not contain searches inherent to students, that person is probably not a student. Doubtfully, an unemployed adult — who is not a student — would represent the most creditworthy borrower as there is no indication of the ability to repay the loan.
Of course, nothing is that simple, and in partnership, companies will build smart algorithms and more accurate ways to extract meaningful information from Web search history. In addition, as Merrill admits, “Nobody has ever proven that it’s possible to turn search data into credit data, and this is exciting.” Companies are on the quest to either build a very discriminatory way to cash in on very private information or will discover the holy grail of credit scoring.
Can search-based credit scoring take discrimination to the next level?
I mentioned the word ‘discriminatory’ because, after all, big data containing everything the person is and wants to be, can be of disservice to the end-user.
Casey Oppenheim, Co-founder of Disconnect, which helps keep people anonymous online, fairly points out the possibility of negative outcomes of such ventures. As Oppenheim commented, “Nobody understands the long-term impact of this data collection. Imagine that someone has 40 years of your search history. There is no telling what happens to that data.”
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