China's distinct digital ecosystem often creates confusion and misunderstanding for those on the outside looking in. Its burgeoning credit system is one such example. Sarah Zhang, co-founder of Points (PTS)—an artificial intelligence-based blockchain solutions provider—spoke with eMarketer’s Man-Chung Cheung about her take on China's social credit system, and the biggest challenge in establishing it.
What type of credit system does China have?
In China today, there's no parallel version to FICO credit scoring services in the US, which covers 90% of the population. There's only the central banks, and only one-third of the population has some type of credit history.
For that reason, it's important to introduce alternative variables beyond FICO, such as how many credit cards you have, have you paid your loans, etc.
There are usually 30 to 50 behavioral variables—a much wider range than FICO—that can assess the creditworthiness of a person and give them a credit line so that they can be on-boarded into the financial system. It's similar to what Zest Finance—a fintech startup based in LA founded by an ex-CIO of Google—does. If you only use the FICO way, there's no way to onboard everyone.
[Social credit] is more of a philosophy and a set of initiatives promoted by the government together with other partners that likens to 'harmonious society.'
The media interprets China’s 'social credit' system as a modern-day Orwellian social control mechanism. What's your understanding of this matter?
Social credit is in fact translated from the phrase 'She Hui Xin Yong' (社会信用). It's more of a philosophy and a set of initiatives promoted by the government together with other partners that likens to 'harmonious society' (和谐社会), a campaign slogan with some programs supporting that idea. But it's not that the government is trying to use a heavy-handed approach to give a credit score to everyone.
There isn't a list of 10 things you need to do to be harmonious. It's the government trying to say, 'We need to build a society where everyone supports everyone, and it's very important to make sure a lot of the social conflicts are being resolved in a peaceful way.'
Who is in charge of the 'social credit' initiative?
'Social credit' (社会信用) isn't really led by anyone. Like many things in China, once there is a slogan or policy imperative, the related agencies will try to implement initiatives echoing the idea.
There is a credit bureau—a credit data-sharing platform called 'Bai Hang Zheng Xin' (百行征信). That's where the government wants to involve the eight licensed credit bureaus in sharing their data.
Are internet companies like Alibaba subsidiary Ant Financial or Tencent involved?
Yes, they are two out of the eight licensed credit bureaus.
The idea makes sense, but you need a technical infrastructure better than just asking participants to submit raw data. That's where blockchain can play a role.
What are some of the behavioral variables being considered for China's credit system?
There are variables such as online shopping behaviors, education or payment histories. You may have also read things like putting garbage out correctly. There's a much wider range of variables in the so-called 'social credit' system because the 'social credit' (社会信用) idiom is trying to say, 'Integrity is important, and is giving or denying loans the only way to promote integrity?'
They're also trying to incorporate broader-range experiments. The idea is to have people with a higher credit score be able to live more conveniently at a lower cost, and the people who have bad credit will be more inconvenienced.
What are the challenges of implementing this type of credit rating system?
It's not clear whether all the players—especially the large ones—will wholeheartedly submit their raw data. The idea makes sense, but you need a technical infrastructure better than just asking participants to submit raw data. That's where blockchain can play a role.
In a broader, blockchain-based technical architectural design, it's possible for Points (PTS) to design a data collaboration protocol so participants don't need to expose their raw data but can still share the aggregated statistics results with each other.