作者:开云手机app 发布时间:2023-04-22 01:09
本文摘要:Financial technology start-ups are creating new models of lending. They mine streams of digital data with clever software to calculate creditworthiness instead of relying on a person’s credit history, the main ingredient in traditional cre


Financial technology start-ups are creating new models of lending. They mine streams of digital data with clever software to calculate creditworthiness instead of relying on a person’s credit history, the main ingredient in traditional credit scoring.一些面向金融领域的科技初创公司正在发售新的贷款模式。它们用智能软件挖出电子数据流来计算出来信誉,而不是像传统的信用评分那样,以个人的信用记录为基础。So far, the new breed of big data lenders has focused on niche markets — recent college graduates, immigrants and payday borrowers — where people often have scant or inconsistent repayment records, and the conventional math of risk analysis stumbles.目前早已有一帮新生的“大数据借贷机构”专心于在利基市场上——刚刚毕业的大学生、移民和发薪日借款人。

这类人的偿还记录往往很少,或者不连贯,用于传统的风险分析数学手段效果不欠佳。ZestFinance, a pioneer in the field, is moving into a huge market where credit histories are scarce: China.ZestFinance是这个领域的先驱之一,目前于是以步入一个信用记录较少的可观市场:中国。

ZestFinance and JD.com, a Chinese online retail giant, are announcing a joint venture to provide a consumer credit scoring service in China. The venture, JD-ZestFinance Gaia, will initially be used to assess credit risk and offer installment loans for purchases on JD.com, which has 100 million active customers and generates yearly revenue of $20 billion. The venture intends to eventually offer the credit-analysis service to corporate customers throughout China.ZestFinance和中国网络零售巨头京东宣告正式成立一家合资公司,在中国市场上获取消费者信贷评分服务。京东享有1亿活跃用户,年营收约200亿美元。这家合资企业取名为JD-ZestFinance Gaia,最初将为京东上的分期贷款购物不道德评估信贷风险。

公司想最后为中国各地的企业客户获取信用分析服务。JD.com is also making a minority investment in ZestFinance, though the companies would not disclose the size of the investment or the valuation of the start-up.京东还对ZestFinance展开了少数股权投资,不过双方没透漏投资规模或是ZestFinance的估值。

“This is a great validation that what we’ve built works,” said Douglas C. Merrill, founder and chief executive of ZestFinance.“这是对我们的极大接纳,证明我们的方法是行得通的,”ZestFinance的创始人兼任首席执行官道格拉斯·C·梅里尔(Douglas C. Merrill)说道。There is a lot of enthusiasm for the data science approach to credit analysis, and venture funding is flowing into this emerging field. The promise is that high-tech tools can give greater depth and detail to the basic principle of banking: know your customer. Start-ups in the field, beside ZestFinance, include Affirm, Earnest, Elevate and LendUp.人们对于用数据科学的方法来展开信用分析热情高涨,风险资本也正在流向这个新兴的领域。

银行业的基本原则是理解客户,而高科技工具未来将会为此获取更加深层次的剖析和更加多的细节。除了ZestFinance之外,该领域的初创公司还有Affirm、Earnest、Elevate和LendUp。The start-ups’ methods vary, as do the data sources they tap. But their algorithms sift through data that can include a person’s social-network connections, web-browsing habits, how they fill out online forms and their online purchases.这些初创公司的方法各异,利用的数据源也不尽相同。

不过,它们用来检验数据的算法可能会涵括个人在社交网络上的关系、网页网页的习惯、填上网上表格的方式,以及网上购物的偏爱。The software looks for patterns and correlations: digital signals that help assess an individual’s willingness and ability to repay. The picture that emerges from the data, enthusiasts say, should result in more accurate risk analysis, thus opening the door to extending consumer credit to millions more people at lower cost.这种软件找寻的是模式与相关性,即有助评估一个人的偿还债务意愿和能力的数字信号。欢迎者指出,数据勾勒出来的面貌,应当可以让风险分析显得更为精准,因此有助以更加较低的成本把消费者信贷获取给额外的人,而其中牵涉到的人数成百上千万。

Yet public policy experts say the enthusiasm for the new lending models is outrunning the evidence. The accuracy and fairness of big data credit technology is unproven, said Aaron Rieke, a former lawyer for the Federal Trade Commission and director of technology projects for Upturn, a policy consulting firm. Mr. Rieke was a co-author of a report last year, supported by the Ford Foundation, that cited ZestFinance as a prime example of big data underwriting, which deploys “fringe alternative scoring models.”然而,一些公共政策专家指出,人们对贷款新模式的热情跑完在了证据的前面。阿隆·里克(Aaron Riek)称之为,大数据信用技术的准确性和公正性仍未经过证实。

里克曾在联邦贸易委员会(Federal Trade Commission)任律师,目前是政策咨询公司Upturn的技术项目总监,去年参予编写了福特基金会(Ford Foundation)赞助商的一份报告。该报告将ZestFinance称作大数据贷款审核领域的一个典型,使用“非主流的替代性信用评分模型”。But JD.com sought out ZestFinance, tested its technology and came away impressed. Last fall, Chen Shengqiang, chief executive of the Chinese company’s finance unit, visited the ZestFinance offices in Los Angeles and spoke to Mr. Merrill and members of his team. Soon after, Mr. Merrill traveled to the Chinese company’s headquarters in Beijing to work on setting up a test of ZestFinance’s technology, working with JD.com data.但是京东寻找了ZestFinance,测试了它的技术,并对它印象深刻印象。去年秋天,京东金融集团的首席执行官陈胜强参观了ZestFinance坐落于洛杉矶的办公室,并与梅里尔及其团队的成员展开聊天。

旋即后,梅里尔前往北京的京东总部,用该公司的数据对ZestFinance的技术展开了一次测试。ZestFinance, founded in 2009, began making loans itself and underwriting loans made by lending partners in 2010. In the United States, ZestFinance has focused its risk analysis on installment loans that are a lower-cost alternative to payday loans. Those borrowers are in the subprime market, and typically have experienced a credit setback in the past, like a personal bankruptcy.ZestFinance正式成立于2009年,从2010年开始自己为客户获取贷款,并审核合作伙伴的贷款。

在美国,ZestFinance仍然专心在分期贷款的风险分析上。对于发薪日贷款,分期贷款是一个成本较低的自由选择。其借款人来自次级贷款市场,一般来说以前都在信用上遭遇过问题,比如个人倒闭。In China, JD.com had a very different assignment for ZestFinance, using different data sources than in America. Only 20 percent of Chinese adults have a credit score, and they often are given credit through the People’s Bank of China, the nation’s central bank, and through affiliations with large state-owned corporations.在中国,京东转交ZestFinance的任务则大不相同,而且用于的数据源也有异于美国。

在中国成年人中,只有20%享有信用评分。他们取得信用的途径往往是通过央行中国人民银行,或是与大型国有企业之间的关系。Across the broader population, lending tends to be more personal and informal — cash loans from networks of friends and relatives.在更好的中国民众那里,贷款往往具备更为个人化的非正式性质——从亲戚朋友那里还债。

But China’s leaders are seeking to stimulate consumer spending to make its economy less dependent on industrial exports. Expanding consumer credit is part of the formula, and the government is allowing private companies, like JD.com, to innovate.但是中国领导层正在希望性刺激消费,以使中国经济减低对工业出口的倚赖。不断扩大消费信贷是整个策略的一部分,政府获准如京东这样的私营企业在这一领域展开创意。Since early 2014, JD.com had been offering its own consumer loans of up to a few thousand dollars for purchases of televisions, smartphones, computers, refrigerators and other merchandise. JD.com’s business model is sometimes compared to a combination of Amazon and UPS.自2014年初开始,京东仍然给它的用户获取贷款(最低约几千美元)借以出售电视、智能手机、电脑、冰箱和其他商品。

京东的商业模式有时被比作亚马逊(Amazon)特UPS。Like Amazon, the company buys goods from manufacturers and has a national network of distribution centers and warehouses. It also has its own fleet of delivery vans. JD.com handles more than two million orders a day, and offers next-day delivery in much of China. It is a full-service online retailer, unlike its better-known rival, Alibaba, whose marketplace connects buyers and sellers.和亚马逊一样,京东也就是指制造商那里进口商,并建设了全国性的物流和仓储网络。


In its test run for the Chinese company, ZestFinance built risk models using JD.com transaction data: what people buy, when they buy it, what brands they choose, where they live and other nuggets of information in the sales data.在为其中国公司展开测试时,ZestFinance利用京东的交易数据——还包括人们卖什么、何时卖、选什么品牌、住在哪里,及交易数据中其他有价值的信息——创建了风险模型。“There’s signals in there,” Mr. Merrill said. “But what would seem like simple signals can actually be very complex.”“这些数据里有一些信号,”梅里尔说。“但那些看上去非常简单的信号,实质上有可能非常复杂。

”For example, one might expect that a person purchasing a lot of luxury goods online is a good credit risk. But Mr. Merrill said that often is not the case. It could be a sign of reckless overspending or even fraud, he said, when linked with other data.比如,人们有可能实在在网上卖很多奢侈品的人信用风险小。但梅里尔回应,情况往往并非如此。他说道,跟其他数据联系一起看,这有可能意味著不计后果地过度消费,甚至有可能是欺诈。If a person is making purchases during the day, that could be a signal that the buyer is unemployed. But, Mr. Merrill said, if the purchases are made during the midday lunchtime, from an office computer, it could well be a sign of a hard-working employee squeezing in time to buy necessities.如果一个人是在白天时间卖东西,有可能回应这个买家没工作。

但如果交易是在午餐时间再次发生,而且是在办公电脑上展开,梅里尔说道,那就很有可能代表这是一个刻苦的员工在挤时间卖必需品。In its test, the creditworthiness predictions made by ZestFinance were compared to the results of JD.com’s experience making loans, which was essentially the control group. The ZestFinance algorithms won handily.在测试中,ZestFinance所作的资信预测,与京东自身借贷的结果不作了对比,后者实质上就是对照组。

ZestFinance的算法精彩落败。The Chinese online retailer, said Josh Gartner, senior director for international communications for JD.com, hopes to “greatly improve the efficiency of deciding who should be offered credit or not.”京东国际公关高级总监约什·加德纳(Josh Gartner)回应,京东期望能“大大提高其贷款决策的效率”。

Data science methods, Mr. Gartner added, can fill a gap “where traditional metrics tend to be less useful, and China would obviously be one of those places.”加德纳补足道,数据科学的方法可以在“传统取决于方法展现出佳的地方”空缺一个空白,“中国似乎就是一个这样的地方”。In a statement, Mr. Chen pointed to the potential value of the joint venture beyond JD.com itself. He called the link-up with ZestFinance “a foundational step toward building a reliable system for assessing credit risk that will help meet the huge market need.”陈胜强在一份声明中认为了这一合资公司在京东之外的潜在价值。他将京东和ZestFinance的牵头叙述为“在创建可信的信用风险评估系统,从而符合辽阔的市场需求方面,是基础性的一步。