Big data against credit gap


There is a trap at the heart of America’s financial system: To get credit, you must already have established a credit history. Millions of Americans never find a way around the contradiction and, as a result, are excluded from things like credit cards or student loans that the rest of the population may take for granted.

Banks and other financial companies typically rely on the three major credit bureaus to decide whether or not to give you credit, using what’s called your FICO score, an algorithm based on your credit history. No credit history; no FICO score. (If you have a poor credit history or bad score, you may be able to get a car or loan, but you will pay higher interest rates and fees.)

But not having a credit history is not the same as being a credit risk. In fact, many people without a credit history can be at very good credit risk; they found ways to pay the rent, buy groceries, and keep electricity without the convenience of cards or other forms of credit.

A number of financial startups, recognizing that many of these transactions are digitally recorded, are exploring ways to open the door for these people with new methods of credit scoring, using a wider range of financial data to better identify who is likely to repay a loan. If it works, it promises to help more people start businesses, buy homes, and get higher education. Businesses also see an opportunity to reach a new set of customers.

“People with little or no credit history, or who don’t have a credit score, have fewer opportunities to borrow money to build a future and any available credit usually costs more.” , Richard Cordray, Former Director of the Consumer Financial Protection Bureau. , said last year announcing that the office would explore the idea. “This only exacerbates their economic vulnerability.”

But there is a catch. New businesses are using software and data sets that deeply probe people’s personal and financial lives and raise concerns among privacy advocates. Other critics fear the new methods and datasets will repeat the same types of discrimination that have kept credit unavailable or expensive for minorities for generations.

The opening of credit could potentially benefit a large number of people: A 2015 study by the bureau found that 26 million adult Americans were “invisible when it comes to credit,” meaning for practical purposes they don’t exist for the credit companies that collect background information. financial borrowers. According to the study, 19 million more Americans had some sort of credit history, but they weren’t important enough to establish a traditional credit score.

People with low incomes, as well as African Americans and Hispanics, were more likely to be invisible when it comes to credit or have too thin financial records for most lenders, according to the study. Immigrants and young adults who have not been in the workforce for this long may also have more difficulty getting a loan or be charged much higher interest rates when they do. New credit scoring methods promise to reduce these disparities.

“It raises the credit scores of communities of color in particular that have yet to benefit from home ownership,” said Aracely Panameño, housing expert who also heads Latin American affairs for the Center for Responsible. Lending.

Federal law prohibited any financial practice that leads to a pattern of discrimination in lending against low income or minority communities. The CFPB is responsible for ensuring that alternative credit raters do not end up reproducing patterns of discrimination or creating new ones.

The companies that build these alternative credit models analyze different data points to try to identify people who are more likely to repay their loans than a traditional credit score might indicate. Last fall, one of those lenders, a company called Upstart, received permission from the CFPB to experiment with collecting a wider range of data to assess potential borrowers. Under the terms of the agreement, Upstart must share its loan and borrower data with the CFPB and continue to adhere to non-discrimination laws.

Upstart incorporates ‘non-traditional variables’ such as education, employer information, and online behavioral models to extend credit to more borrowers with poor credit and reduce the cost of loans for them. The company claims that its “algorithmic underwriting” – that is, it uses proprietary software models to determine whether a person can be a good risk – can identify a creditworthy person who has scores below 700 on the The traditional FICO credit score scale, a level at which it generally becomes more difficult to qualify for a loan without paying higher interest rates or fees.

One of the central questions about algorithm tuning, said Paul Gu, the company’s co-founder and senior data guru, is, “How do you know if your model is right if you’re doing something that nobody else does? “

Another approach is being tried by another startup called Petal relies on a relatively simple data point to determine if it should accept customer requests for the credit card it launched: do you earn and save yourself more money than you spend ? Cash flow analysis, often used in small business loans, plays an important role in its borrower ratings. Applicants voluntarily provide bank statements that allow the company to analyze their spending habits.

Petal started in part thanks to the experience of one of the company’s co-founders, Berk Ustun. Ustun immigrated from Turkey to study at the University of California-Berkeley for his undergraduate degree, and could not claim a credit card, cell phone, or apartment because he had no background credit in the United States. He is now doing post-doctoral studies at Harvard, in addition to his work as an advisor to Petal.

Many established financial institutions, such as banks, have been slower to adopt an alternative data-driven approach to solvency, in part because they remain cautious after the 2008 financial crash: Sour.

Consumer advocates, like CRL’s Panameño, don’t see the incorporation of alternative data into underwriting as a “panacea” for minority borrowers, and fear that, if used in certain ways, it could simply displace funds. credit spreads rather than resolving them. For example, using a borrower’s address or zip code or location-related data, such as shopping habits, could reinforce existing disparities.

“Alternative data is not created equal. Alternative data means different things to different people, ”Panameño said. “This can lead to disparate effects, potential racial discrimination, [and] redlining, which means they might end up being charged more for certain products and services depending on where they live.

Some are also concerned about confidentiality. Social media is making it easier than ever to collect personal data about consumers, a tempting information well for lenders to tap into. But sensitive information that lenders are legally supposed to avoid when assessing creditworthiness, such as race, gender, and sexual orientation, can often be gleaned from a Facebook profile.

There are also broader concerns about the societal impact of using different data points in addition to traditional credit history. China has launched a pilot project “social creditProgram that some see as a threat to privacy, even a means of social control. The system, which the Chinese government plans to implement nationwide in 2020, would include behavioral factors, as well as the credit of your friends and contacts, to determine your social credit score. The goal is to make it easier for the government to track individuals, to assign privileges, and to encourage behavior that the government deems useful. It is seen as a way for the Communist Party to uphold social and political norms and avoid potential political unrest.

For now, the debate over alternative data in the United States remains primarily focused on traditional concerns about loan equity and access to credit. And proponents argue that the benefits could extend beyond the recipients to help stimulate the economy as a whole.

Thoughtful and responsible use of financial data about individuals could expand the credit available to underserved consumers, ”Cordray said last year. “If it is possible to expand the opportunities in this way, it would not only benefit these consumers, but perhaps stimulate the economy in a way that benefits all of us.”

Colin Wilhelm covers the POLITICO Pro Financial Services conference.


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