How fintech startups are using big data to solve China’s huge credit gap

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It was almost Angel Zhang’s 22nd birthday. She wanted to do something special, spend a little extra on herself. But instead of asking her parents for more money – they give her a US$300 monthly stipend – she used Huabei, a virtual credit card run by Ant Financial.

“Almost every shopping app has their own credit service these days,” says Zhang, a fourth-year university student in northern China. Like many of her classmates, Zhang doesn’t earn any income. “As long as you pay back your loans on time, you can increase your credit line.”

She pays for all kinds of things with credit now: clothes, makeup, toiletries, hotels, train tickets, even her phone bill. The time it takes to save up for something – say a face mask or a new pair of shoes – has been shortened, she tells Tech in Asia.

China has been a very cash-based, non-debt-focused consumer base. This is changing.

University students like Angel Zhang have only recently been able to take out monthly micro-loans in China. According to World Bank estimates from 2014, only 10 percent of China’s adult population had ever borrowed from a financial institution, despite 79 percent having an account. That’s partly because consumer credit scoring is relatively new. China’s banking regulators didn’t develop a consumer credit database until 2006. In contrast, US credit scoring company FICO launched its scoring system in 1989.

The national credit system also has limited coverage. Though the People’s Bank of China (PBOC) had data on roughly two-thirds of the population as of 2015, only about a third had a credit history.

Thanks to big data, however, China’s fintech companies are rising to the challenge.

“China has been a very cash-based, non-debt-focused consumer base. This is changing,” says Zennon Kapron, director of Kapronasia, an Asia-focused financial industry research and consulting firm. “The uptick in consumer credit in terms of borrowing, peer-to-peer platforms, consumer lending, credit cards, and mortgages has increased significantly.”

Still, the PBOC’s credit database is not very robust, he says. Not all lending companies have access to it either. “They have to create their own credit rating system or use third-party scoring companies.”

Faster loans

To gauge someone’s creditworthiness, you have to answer the following question: what is the borrower’s ability and willingness to pay? And in the era of big data, there’s an added dimension: how do you train computer systems to make that decision – with as little human intervention as possible?

“It’s all about the data. The more data we get, the richer the data, the more diverse the data, the better the model,” says Ren Ran, vice president of Dumiao, a credit lending unit under Beijing-based fintech services company PINTEC. According to the company, Dumiao processes about 3 million loan requests per month.

“It’s like cooking. To cook dishes, you need to have the ingredients first. If you don’t have the ingredients, then it doesn’t matter how good a chef you are,” he emphasizes.

Filtering out noise

Data crunch

Black box