Perhaps no market is more ripe with opportunity for financial technology firms than China’s. More than 800 million of its citizens (60 percent of its massive population) actively use the Internet and do so almost entirely via mobile devices. In 2016 alone, nearly $23 trillion of Chinese consumer expenditures were made through mobile-payment platforms. And the country has a growing small and midsize business community—which already accounts for 60 percent of China’s GDP—hungry for lending, payments, and other financial services that larger Chinese financial institutions have typically been unable to provide, with their resources focused largely on serving state-owned enterprises.
While Alibaba and Tenpay own about 90 percent of the mobile-payment market share, the remaining 10 percent, along with the demand for other financial services, still presents a substantial windfall for fintechs. Among the more modestly sized players cashing in on the opportunity are companies such as CreditEase, a Chinese fintech firm providing wealth-management, lending, and other services to customers in more than 250 cities in China and, increasingly, around the world. CreditEase founder and CEO Ning Tang recently shared with us his perspective on how fintech players, from both within and outside China, can capture the opportunity among small and midsize Chinese businesses and how artificial intelligence (AI) can help. The following commentary is adapted from that conversation.
How data help Chinese fintechs serve small businesses
China’s financial system is several decades behind that of the US. For example, China doesn’t yet have a robust, nationwide credit-bureau system, so our citizens don’t have credit scores, making it difficult to serve small businesses. Chinese fintechs have come in to fill this large vacuum.
But small-business lending is a worldwide challenge. Even the US and UK haven’t been able to address it in a very satisfying way. Is a credit score alone good enough to make a lending decision to a small-business owner?
Small businesses in China, the US, the UK, and other parts of the world are going digital at increasing speed. A small restaurant in China, for example, is more often than not already wired up. I’m not just talking about the ordering or booking or payment experience. That’s more front line, the consumer end. I’m talking about the middle and back end, like enterprise resource planning, supply chain, employee management, shift management, and so on. It’s all digital. So we collect and analyze small-business data in real time to determine creditworthiness and provide financial help.
Also, we work with companies like eBay and Amazon. On those platforms, Chinese e-merchants sell things outside of China to the US and other parts of the world. Those companies often don’t have the tangible assets that traditional financial institutions look for as collateral. But they have digital assets—operating data, transaction data, and more. We can analyze those digital assets to evaluate small businesses’ creditworthiness and help them access financing. Basically, this is a huge opportunity for credit-card and marketplace-lending companies to play a key role ten years, 20 years into the future.
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The secret to fintech success in China
China doesn’t have a well-established investor community or investing environment where people understand things like risk diversification, global asset allocation, long-term investing, and portfolio value. These notions are actually very new to Chinese investors.
So when US and Western financial-services companies go into China, there’s no low-hanging fruit for them. They can’t just go there and start a land grab. Whoever goes into the China market to win needs to utilize technology to do high-quality investor education. Otherwise, there’s no way they can distribute their products and services effectively.
We learn from best practices all around the world to make our investor education very entertaining, like a video game. It’s very interactive, very participative. Investors learn more, and in the process, we collect data on them and get a much better understanding of our investor base, their risk preferences, and their investment experiences.
In financial services, what’s the most effective customer-acquisition tool? It’s educating your prospect. If you become the prospect’s teacher, of course you can win his or her heart, right? You really help the person better understand and better appreciate what you can offer.
How CreditEase uses AI to achieve efficiency
We don’t do technology for technology’s sake. Technology should be applied to make financial services better.
We achieve great efficiency by utilizing technology, including AI, within our company. For example, in the past, our IT infrastructure databases were handled by IT experts. There were several dozen of them doing key tasks like maintenance and monitoring. Now we have an AI-based system that can handle some of the minor problems. Of course, that system learns how to do things from our experienced engineers and experts, so more and more problems can be identified and handled by our AI-based system. Then we have a very lean team to run our IT infrastructure, monitoring, troubleshooting, and so on.
In addition to using AI in what we do, we are also a key investor in AI technology in China. Our wealth-management business serves China’s elite, high-, and ultrahigh-net-worth individuals, mostly successful entrepreneurs in traditional industries like manufacturing, real estate, importing, and exporting. They’re interested in investing in the new economy, including AI. To enable that, we serve as a limited investment partner for pretty much all tier-one venture capitalists in China. Venture capitalists need long-term capital to allow them to invest in start-ups that can take ten years or more to grow into the next Tencent, the next Alibaba, the next CreditEase. In the past, that type of patient capital hasn’t been available in China’s ecosystem, so we’ve come in to give it to them.
We need to figure out which venture-capitalist and private-equity (VC/PE) firms can be really good. We can see if they’ve done a good job in the past, but is there going to be consistent performance ten years down the road? In the past, this type of analysis has been done by a group of experts. But today AI is helping these experts tremendously.
We’ve been able to put fund and team information—both public and proprietary—for more than 20,000 VC/PE investors into an AI tool that can do over 60 dimensions of analytics and help our investment experts in many ways. For example, in the past, investment experts could do only one or two degrees of reference—call someone they know for information about someone the contact knows. But you can’t do six degrees of separation. You know everybody if you do six degrees. The machine can do that. It creates beautiful knowledge graphs with all the relationships among these portfolio companies, allowing our investment team to be very effective in asking the right questions and calling the right people to gain more knowledge about a particular fund.
The tool also helps us to make investment decisions. Every second the tool collects new information and tries to draw some conclusion from it. But then our experts have to decide if the conclusion makes sense. We as humans need to make sure machines come up with recommendations that are based on solid investment logic, not just some random pattern. So the decision making eventually has to be made by human beings. That’s why I foresee this system of machine plus human beings going forward for a very long time.