根据世界银行在2014年做出的预估，
虽然有79%的成年人拥有个人银行账户，但只有10％的人会向金融机构提出贷款需求。这一定程度上是消费者信贷评分较新的原因。
直到2006年中国的银行监管机构才开发出消费者信贷数据库，相比之下，美国信用评级公司FICO在1989年就已推出了评分系统。
那么在这个大数据时代
对于金融科技创业公司来说，如何利用大数据来解决中国巨大的信贷差距问题呢？
我们不妨来听听专业人士的回答：
文章截取自：TECHINASIA
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According to World Bankestimates 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-thirdsof 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.
Filtering out noise
Of course, using big data to calculate how much users can borrow comes with its own set of challenges. Machine learning models have to be updated regularly to take into account new data and changes in the market.
Fintech startups also have to automate fraud detection, like catching people who steal personal IDs to apply for loans. That’s usually done by tracking user behavior, like the way someone types out their ID number, or their physical location, if users agree to share that information.
It could be a case of fraud, or it could be an anomaly, explains a company spokesperson. Once, the system identified a group of users buying the same Canon camera. It turned out that a local photography club was recommending the camera to its members.
Sync Shan, head of big data at Dianrong. Photo credit: Dianrong.
Not all data is created equal either. In fact, the best data comes from the PBOC’s credit report, which is harder to access, says Sync Shan. Previously at Chinese search giant Baidu, Shan now runs the big data team at Dianrong, a Chinese online lending marketplace co-founded by Lending Club co-founder Soul Htite.
（单忆南说：“并不是所有的数据都是同等权重的。事实上，最好的数据是来自中国央行的信用报告，但这一报告很难获得。”单忆南曾任职于中国搜索巨头百度，现在点融公司任数据团队负责人，这是一家由互联网借贷公司Lending Club的联合创始人苏海德与中国资深金融法律律师郭宇航在中国共同创立的互联网金融公司。）
“If we can get our hands on a personal credit report, we’ll definitely use it,” says Shan. It’s very efficient data, which means its ability to differentiate and classify good lenders from the bad is high.
（“如果我们能拿到个人信用报告，我们肯定会使用它的，”这是非常有效的数据，也就是说，它能够有效的区分好坏贷款人。）
He shows me a diagram of a triangle – a hierarchy of data for analyzing creditworthiness. At the top, there’s data from the national credit database, which includes housing loans and credit card information. The level underneath is consumption or purchasing habits, then mobile phone data, social connections, and user behavior on the lending app itself.
（他向我展示了一个三角形的图表——一个用于分析信用度的数据层次结构模型。它的顶部是来自国家信用数据库的数据，其中包括住房贷款和信用卡信息。往下是消费或购买习惯，然后是移动电话数据、社交连接和用户行为。）
“The higher you go, the more effective the data is,” he explains. “But it covers less people.” Conversely, the further down you go, the weaker the correlation, but the wider the coverage.
(他解释道。“从下向上看这个三角，数据越来越有效，但它覆盖的人越来越少。相反，从上向下过渡，相关性越弱，覆盖范围就越广。”)
Then there’s the cost of data. It has to be balanced with its performance in Dianrong’s model, says Shan, which classifies users into different tiers of credit risk. A cheaper data point will be about 15 cents,while others can be much more expensive, he says, declining to specify.
(然后就是数据的成本。点融的应用需要在成本和效果之间做一个平衡，它将用户划分为不同等级的信用风险。他说，一个便宜的数据点大约是15美分，其他的可能要贵得多。）
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