大数据和人工智能如何改变网贷（How Big Data And Artificial Intelligence are Changing Online Lending）

As
digital lending continues to grow in size, companies are looking for ways to make their services more efficient and profitable to both lenders and borrowers. And they believeartificial
intelligence and big data hold the key to the future of loans.
随着数字借贷体量的不断增大，网贷公司也在寻求使它们服务更高效、对放款方和借款方都有利的方法。他们相信人工智能和大数据技术是开启网贷未来的钥匙。

Lenders traditionally make decisions based on a loan applicant’s credit score, a three-digit number obtained from credit bureaus such as Experian and Equifax.

放款人以往根据从Experian 和 Equifax等信用机构获得的贷款申请者的一个三位数的信用分数做决定。

Credit scores are calculated from data such as payment history, credit history length and credit line amounts.They’re used to determine how likely applicants are to repay their debts and to calculate the interest rate of loans.

信用分数根据支付记录、信用历史长度和信用额度计算。它们被用来计算申请者偿还贷款的概率和贷款利率。

If you have a low credit score, you’re considered a risky borrower, which either means your loan application will be denied, or you’ll receive it at a high-interest rate.
如果你的信用分数很低，你会被认为是一个高风险的借款人，这意味着或者你的贷款申请被拒绝，或者以高利息拿到贷款。

Digital lending platforms believe that this kind of information does not paint a complete picture of a loan applicant’s creditworthiness. They’ve taken on to add hundreds and thousands of other data points to their process, not all of which are
necessarily related to financial interactions.

数字借贷平台认为通过这类信息无法得到贷款申请人的完整信用画像。他们将成百上千的其它数据加到评估流程中，而且并不要求所有数据都与金融相关。

This can include information such as your educational merits and certifications, employment history, and even trivial information such as when you go to sleep, which websites you browse to, your messaging habits and daily location patterns.
这些数据可以包括例如教育经历、证书、雇用历史，甚至何时睡觉、浏览了哪些网站、消息习惯和日常位置等琐碎信息。

                                                 


To be fair, big data can be a double-edged sword and create more confusion than clarity, and artificial intelligence has in large part become a marketing term for companies that want to sell their products and services. But experts in the online
lending industry believe it can have a big impact on how fintech companies perform.
客观地说，大数据是一把双刃剑，会造成更多麻烦而不是把问题变得简单，而人工智能在很大程度上已经变成那些想要销售他们产品和服务的公司的一种营销术语。但是网贷行业专家相信其会对金融科技公司的运作方式产生巨大影响。

The data can enable companies to create a more complete profile of a loan applicant. This can help make more accurate underwriting decisions, which results in a reduction in defaults for lenders and lower interest rates for borrowers. It can also
help automate parts—and maybe all—of the process.
数据可以确保公司得到更完整的贷款申请人概况。这可以帮助制定更为精确的承保决策，既对放款人降低了违约风险，又对借款人降低了利率。同时也有助于部分或全部流程的自动化。

How lending startups are leveraging AI

借贷初创公司如何利用AI技术

Upstart is a California-based
peer-to-peer online lending company that is enhancing loans with artificial intelligence. Upstart uses machine learning algorithms, a subset of AI, to make underwriting decisions.
Upstart是一家位于加利福尼亚的P2P网贷公司，正在使用人工智能技术放款。Upstart使用机器学习算法，一种人工智能算法，来制定承保决策。

Machine learning can analyze and correlate huge amounts of customer data to find patterns that would otherwise require considerable manual effort or go unnoticed to human analysts.

机器学习能分析并关联大量用户数据，同样的工作将需要大量的人力，其中一些信息还可能被人工分析遗漏。

For instance, it can determine if applicants are telling the truth about their income by looking through their employment history and comparing their data with that of similar clients. It can also find hidden patterns that might favor an applicant.
通过浏览申请人的雇用历史并与其他类似客户数据进行比较，机器学习能够立即判别申请人是否对于自己的收入情况说谎。机器学习同样可以发现一些有助于贷款申请的信息。

Upstart believes this can benefit people with limited credit history, low incomes and young borrowers, who are usually hit with higher interest rates. The company has also managed to automate 25 percent of its less risky loans, a figure it plans
to improve over time.

Upstart相信这有利于那些通常被迫接受高利息的信用记录有限、低收入者和年轻人。该公司还计划将25%的低风险贷款流程自动化，该比例将随时间不断提高。

This can save a lot of time and energy from lenders, who will welcome a return on investments that requires less intervention on their part. The technology is planned to be available to banks, credit unions and even retailers that are interested
in providing low-risk loans to their customers.
通过这项技术，放款人能够节省大量的时间与精力，他们也会欢迎这样一个无需过多介入又能带来回报的投资项目。这项技术可以用于银行业、信用机构甚至那些愿意为顾客提供低风险贷款的零售商户。

Avant, a Chicago-based startup that offers unsecured loans ranging between $1,000 and $35,000, uses analytics and machine learning to streamline borrowing for applicants whose credit score fall below the acceptable threshold of traditional loaning
banks.

Avant，一家位于芝加哥的，提供范围在$1000至$35000的无担保贷款的初创公司，使用分析学与机器学习技术为信用分数低于传统银行申请门槛的申请者简化贷款申请流程。

The platform’s algorithms analyze 10,000 data points to evaluate the financial situation of consumers. For instance, these algorithms are helping the platform identify applicants who have low FICO scores (below 650) but manifest behavior similar
to those with high credit scores.
该平台算法通过分析10000个数据点来评估顾客的财务状况。这些算法能够帮助平台立即识别出FICO分数较低（低于650）但用户行为与高信用分数者相似的申请人。

The company is also using machine learning to
detect fraud by comparing customer behavior with the baseline data of normal customers and singling out outliers. The platform analyzes data such as how much time people spend considering application questions, reading contracts or looking at pricing options.
该公司还使用机器学习技术通过将用户行为数据与正常用户基准值进行比较并挑出异常值来检测欺诈。平台分析的数据包含比如用户在考虑申请问题、阅读合同或价格选项时花费的时间等。

Avant is exploring extending its services to brick-and-mortar banks that are interested in starting or expanding their online lending business.
Avant正在寻求向那些有兴趣开拓自己网贷业务的银行拓展其服务。

Remaining challenges

现存的挑战

Digital lending reportedly accounts for 10 percent of all loans across US and Europe, a figure that is steadily growing. The benefits of applying machine learning and analytics are evident, and according to CB Insights, there are more than a dozen
fintech startups that are using the technology to evaluate loan applications and optimize the process.
据报道，网贷占欧美所有贷款总量的10%，该比例还在稳定增大。采用机器学习与分析学的好处是明显的，根据CB Insights，12个以上的金融科技初创公司正在使用该技术评估贷款申请与优化申请流程。

                                          


However, not everyone agrees that machine learning is the panacea to all the problems of online loans. For instance, many of these applications require you to download apps that collect all sorts of personal data. And as the Equifax hack shows,
entrusting too much personal information to a single company can have dire
security and privacy implications for you.
然而，并不是所有人都同意机器学习是解决网贷行业所有问题的灵丹妙药。举例来说，许多类似应用都要求用户下载能够收集用户各种私人数据的应用程序。正如Equifax黑客表明，过分信任并向某个公司提供过多的私人数据会对用户造成可怕的安全与隐私影响。

There’s also the issue of
algorithmic bias. Machine learning algorithms too often make decisions that reflect the biases and preferences of the people who provide them with training data.Experts are concerned that this can introduce a whole new set of challenges for loan applicants.
And the model has yet to prove its mettle during a downturn or financial crisis.
还存在算法偏好问题。机器学习算法经常做出对为它提供训练数据的人有利的决策。专家担心这会为贷款申请人带来一系列新的挑战。而且该算法还没有在经济低迷或经济危机中得到实践验证。

However, the proponents of machine learning–based loans are confident that AI will eventually become an inherent part of online lending. In an interview with NPR, Dave Girouard, the CEO of Upstart said, "In 10 years, there will hardly be a credit
decision made that does not have some flavor of machine learning behind it."
然而，基于机器学习的信贷的支持者坚信人工智能最终会成为网贷的固有组成部分。在一次NPR的采访中，Upstart公司CEO Dava Girouard说：“十年内，很难再找到没有机器学习技术支持的信贷决策。”

注释：

double-edged sword：双刃剑

brick-and-motar：具体的

panacea：灵丹妙药

关联阅读：

原文链接：

https://cointelegraph.com/news/how-big-data-and-artificial-intelligence-are-changing-online-lending

网贷平台:盈利不靠主业风控依赖外包：

http://www.sohu.com/a/196069594_120702?_f=index_pagerecom_8