CFPB investigates alternative data for credit scores

Source: Consumer Financial Protection Bureau

The Consumer Financial Protection Bureau today launched an inquiry into ways to expand access to credit for consumers who are credit invisible or who lack enough credit history to obtain a credit score.

Traditional credit history includes a borrower’s payment of debts such as mortgages, credit cards, and other loans. It is used by lenders to decide who can get credit and what it will cost. The Bureau is seeking public feedback on the benefits and risks of tapping alternative data sources such as bills for mobile phones and rent payments to make lending decisions about consumers whose lack of credit history might otherwise block opportunities.

“Alternative data from unconventional sources may help consumers who are stuck outside the system build a credit history to access mainstream credit sources,” said CFPB Director Richard Cordray. “We want to learn more about whether this non-traditional approach can offer opportunities to millions of Americans who are credit invisible and how to minimize any risks in how this information is used.”

The Bureau estimates that 26 million Americans are credit invisible, meaning they have no credit history with a nationwide consumer reporting agency. Another 19 million consumers have a credit history that has gone stale, or is insufficient to produce a credit score under most scoring models. A credit score is drawn from a consumer’s credit report. A credit report may reflect if payments are made on time, what debt a consumer owes, and whether they have a debt or bill in collection. Credit reports can also include records such as liens, judgments, and bankruptcies, which can provide insight into a consumer’s financial status and obligations.

Typically, a credit score predicts the likelihood the consumer will repay a debt as agreed. A mathematical formula – the scoring model – is used to create the credit score based on payment record, amount of debts, and other factors. Each has a certain weight, and a credit score is calculated from this formula. A higher score usually makes it easier for a borrower to qualify for a loan and may garner a better interest rate. Most credit scores range from 300-850. Different lenders may use different scoring models, so a consumer’s credit score can vary from lender to lender.

Without a sufficient credit history, consumers face barriers to accessing credit, or pay more for credit. This problem disproportionately impacts consumers who are Black or Hispanic, and people who live in low-income neighborhoods. It also impacts some recent immigrants, young people just getting started, or people who are recently widowed or divorced who don’t have enough credit history on their own. Underserved consumers often resort to high-cost loans that aren’t reported to credit reporting agencies, which may not help build a credit history.

For some consumers, the use of unconventional sources of information, called “alternative data,” may be a way to gain access to credit to build a credit history. Alternative data draws from sources such as bill payments for mobile phones and rent, and electronic transactions such as deposits, withdrawals or transfers. This information could show a track record of meeting obligations that may not turn up in a credit history. As a result, some who now cannot get reasonably priced credit may see more access or lower borrowing costs. The Bureau is also exploring risks posed by alternative data that is inconsistent, incomplete, incorrect, overgeneralized, or biased. Such flaws could adversely affect credit access for low-income and underserved populations, or others. The CFPB’s Request for Information seeks insights into the benefits and risks of alternative data and the techniques used to compile and analyze it. Specifically, Bureau will explore topics including:

  • Access to credit: The CFPB seeks information about whether using alternative data to create or augment a credit score could increase access to credit by helping lenders better assess consumer creditworthiness. For instance, a consumer without a scored credit history may still pay bills on time for utilities or mobile phones. This might reassure lenders that they are viable credit risk. Some lenders might not lend to a consumer with a credit score less than 620. However, they might do so if alternative data suggests they are less of a risk for defaulting on the loan.
  • Complexity of the process: The CFPB is looking at whether the use of this information could make credit decisions more complex for both consumers and industry. This process includes lenders who must notify consumers about credit decisions and financial educators helping consumers grasp their credit standing. The Bureau is examining whether the added complexity makes it harder for consumers to understand and take control of their financial lives.
  • Impact on costs and service: The CFPB is looking into the impact of the use of alternative data, new ways to analyze it, and new technologies on costs and services in credit decisions. The Bureau is studying if this may help produce a faster application process, lower operating costs for lenders, and lower loan costs for borrowers.
  • Implications for privacy and security: The CFPB is looking into privacy and security issues in the use of alternative data that contains sensitive personal information. Consumers may not know that it has been collected and shared or how it will be used in the credit process. The Bureau will also explore whether some data are more prone to errors because of weaker or different standards than data traditionally used in credit decisions, and whether consumers can correct errors in this information.
  • Impact on specific groups: The CFPB is looking into whether the use of alternative data could affect certain groups or behaviors in unpredictable ways. For example, members of the military may frequently move, which might give a false impression of personal instability that would affect whether they can get credit. The Bureau is also studying the impact on fair lending of using data that may be correlated to a person’s race, ethnicity, gender, or other attribute, and how such risks could be managed. 

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