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研究表明,在线平台同样难以避免借贷歧视

专栏全新的互联网金融模式国际资讯

研究表明,在线平台同样难以避免借贷歧视

随着在线申请贷款的人越来越多,人们本希望贷款行业能够变得更加公平。

按理来说,如果借贷机构没有面对面见到借款人,就不会因为借款人的肤色而产生歧视。比如,与传统借贷机构相比,自动化借贷机构更容易批准少数族裔借款人的贷款。

但是,加州大学伯克利分校教授的最新研究"金融科技时代的消费借贷歧视"表明,算法也可能像贷款人员一样,对借款人产生偏见。加州大学伯克利分校法学教授兼研究共同作者Robert Bartlett表示:"这个发现令人惊讶,因为没有人工涉及。"

在线平台Quicken Loans是美国最大的借贷机构之一,上述研究选取这一平台进行了案例研究。

研究人员发现,少数族裔在线贷款申请利率一般会高出5.3个基点,与他们在借贷机构支付的5.6附加点相差并不多。

换句话说,如果贷款30万美元,非裔或拉丁裔申请者需要额外支付1%以下,或预先支付2000美元 "折扣点"或预付利息,才能享受与白人申请者同样的贷款利率。

研究表明,每年拉丁裔和非裔借款人需额外支付2.5亿至5亿美元的贷款利息。

研究人员使用机器学习技术来分析美国贷款的四大数据集,这些数据集用来控制信贷风险。

加州大学伯克利分校法学教授兼研究共同作者Robert Bartlett表示:"我们看到的利率差别都不是由信誉差异导致的。"

这些算法生成不公平利率的原因尚未可知,因为承保认购"非常复杂"。

美国银行家协会发言人Jeff Sigmund也强调,任何重要的贷款审核借贷实践都必须考虑大量措施,因为这些措施间接说明了客户的偿还能力。即使借款人的信贷评分和贷款价值比率类似,仍然会有因素可能导致借款人支付不同的利率。

其中一个可能的解释就是,在线借贷机构使用的可变因素与传统金融机构的信贷评分等因素不同,比如在线借贷机构可能会关注借款人的地理位置或教育水平,借此来确定贷款价格。

比如Barlett指出,算法借贷中"大数据"使用的增加可能会进一步加深歧视。例如,某些人上过的高中可能就决定了他们的违约率,同时也可能确定他们的种族。

 

 

 

 

With more people applying for mortgages online, the lending landscape was expected to become more equitable.

The logic was that lenders couldn't discriminate against a borrower based on their skin color if they weren't face-to-face with them.

Yet algorithms can be just as biased as a loan officer sitting across a desk, according to a new study by professors at the University of California, Berkeley titled, "Consumer-Lending Discrimination in the Era of Fintech."

Online platform Quicken Loans is one of the largest mortgage lenders in the United States, according to the study, and nearly all major lenders offer applications that can be completed entirely online.

"It's a surprise finding — because there's no human," said Robert Bartlett, a law professor at the University of California, Berkeley and a co-author of the study.

The researchers found that minorities paid 5.3 basis points extra in interest with online mortgage applications, little different than the 5.6 additional points they shell out with the overall set of lenders.

In other words: on a $300,000 mortgage, an African-American or Latino applicant would need to pay just under 1 percent — or around $2,000 more upfront in "discount points" or prepaid interest to secure the same mortgage rate as a white applicant, Bartlett said.

Each year, Latino and African-American borrowers pay between $250 million and $500 million extra in mortgage interest, the study said.

The researchers used machine learning techniques to analyze four large data sets of U.S. mortgages. They controlled for credit risk.

"Whatever difference in rates that we see, it's not due to differences in credit worthiness," Bartlett said.

How exactly these algorithms result in unfair rates is unknown because the underwriting is a "black box," Bartlett said.

One potential explanation, however, is that online lenders utilize variables other than the traditional financial ones like credit score; they might be factoring in a borrower's geography or education level to price their loans, Bartlett said.

Any meaningful review of mortgage lending practices must consider a myriad of measures that indicate a customer's ability to repay, said Jeff Sigmund, a spokesman for the American Banker's Association.

"Some of those factors could result in borrowers paying different interest rates even if their credit scores and loan-to-value ratios are similar," Sigmund said.

However, the increasing use of "big data," in algorithmic lending, Bartlett said, could deepen discrimination further.

For example, the high school someone attended might predict their default rate. But it could predict their ethnicity, too.

The researchers found one sign of progress among automated lenders: they're more likely than traditional lenders to approve minority borrowers.

"Conventional lenders are leaving money on the table — they're turning away black and Latino borrowers that would appear to be acceptable," Bartlett said. "Those applicants are in turn being picked up by the fintech lenders."

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