Financial Firms Using Intangible and Tangible Correlations to Determine Creditworthiness

"Their's was not to reason why..." Alfred Lord Tennyson may have been talking about 600 cavalrymen in 'The Charge of the Light Brigade,' but the implicit question lingers to this day: if you are unwilling or unable to ask why you may find yourself in trouble about the how and what.

Data science and the information it produces have given organizations the ability to analyze reams of seemingly disparate data sources resulting in often illuminating insights into human and institutional behavior.

If, as the following article explains, someone uses caps versus lower case letters in a credit application, or lives in a certain neighborhood or went to certain schools, it could provide predictive wisdom about their future behavior and their worthiness as a client, borrower or athletic draft pick.

But if the people analyzing the data are concerned only with the strength of the correlations and not with the implications or historical, sociological or psychological inferences, those correlations could prove to be discriminatory - at best. Taken a couple of steps further, they could also lead to conclusions that stifle innovation, misallocate resources or stymie economic development.

While the power of the data itself can be compelling, without informed interpretation and knowledgeable assessment of its potential impact, it could produce outcomes that contravene the intent of the analysis at a macro level and quite possibly at the micro level as well.

Employing data that were once, ignorantly, considered immeasurable is a positive and welcome development. But to use such information effectively requires more time, effort and insight than most machines can yet provide. JL

Steve Lohr reports in the New York Times:

Data scientists focus on finding reliable correlations in the data rather than trying to determine why proper capitalization may be a hint of creditworthiness.The danger is that the software could end up discriminating against racial or ethnic groups without being programmed to do so.

When bankers of the future decide whether to make a loan, they may look to see if potential customers use only capital letters when filling out forms, or at the amount of time they spend online reading terms and conditions — and not so much at credit history.

These signals about behavior — picked up by sophisticated software that can scan thousands of pieces of data about online and offline lives — are the focus of a handful of start-ups that are creating new models of lending.

No single signal is definitive, but each is a piece in a mosaic, a predictive picture, compiled by collecting an array of information from diverse sources, including household buying habits, bill-paying records and social network connections. It amounts to a digital-age spin on the most basic principle of banking: Know your customer.

“We’re building the consumer bank of the future,” said Louis Beryl, chief executive of Earnest, one of the new lenders.

Yet the technology is so new that the potential is unproved. Also, applying the modern techniques of data science to consumer lending raises questions, especially for regulators who enforce anti-discrimination laws.

None of the new start-ups are consumer banks in the full-service sense of taking deposits. Instead, they are focused on transforming the economics of underwriting and the experience of consumer borrowing — and hope to make more loans available at lower cost for millions of Americans.

Earnest uses the new tools to make personal loans. Affirm, another start-up, offers alternatives to credit cards for online purchases. And another, ZestFinance, has focused on the relative niche market of payday loans.

They all envision consumer finance fueled by abundant information and clever software — the tools of data science, or big data — as opposed to the traditional math of creditworthiness, which relies mainly on a person’s credit history.

The new technology, proponents say, can open the door to far more accurate assessments of creditworthiness. Better risk analysis, they say, will broaden the lending market and reduce the cost of borrowing.

“The potential is there to save millions of people billions of dollars,” said Rajeev V. Date, a venture investor and former banker, who also was deputy director of the Consumer Financial Protection Bureau.

Investors certainly see the potential; money and talent are flowing into this emerging market. Major banks, credit card companies and Internet giants are watching the upstarts and studying their techniques — and watching for the perils.

By law, lenders cannot discriminate against loan applicants on the basis of race, religion, national origin, sex, marital status, age or the receipt of public assistance. Big-data lending, though, relies on software algorithms largely working on their own and learning as they go.

The danger is that with so much data and so much complexity, an automated system is in control. The software could end up discriminating against certain racial or ethnic groups without being programmed to do so.

Even enthusiasts acknowledge that pitfall. “A decision is made about you, and you have no idea why it was done,” Mr. Date said. “That is disquieting.”

The data scientists focus on finding reliable correlations in the data rather than trying to determine why, for instance, proper capitalization may be a hint of creditworthiness.

“It is important to maintain the discipline of not trying to explain too much,” said Max Levchin, chief executive of Affirm. Adding human assumptions, he noted, could introduce bias into the data analysis.

Regulators are waiting to see how the new technology performs. The Consumer Financial Protection Bureau wants to encourage innovation but is monitoring the emerging market closely, said Patrice A. Ficklin, head of its fair lending office.