70% of Americans Would Share More Personal Data for Fairer Credit Decisions

New research from the Harris Poll and ZestFinance shows deep dissatisfaction among most Americans with the traditional credit scoring system. A majority of younger Americans as well as minority groups believe the way their credit is assessed stacks the economic odds against them relative to other demographics. Eight out of ten Hispanics and African Americans say their credit score is an inaccurate representation of who they are, and want lenders to look at more factors in lending decisions. A majority would be happy to give up more personal data if it meant fairer access to loans.

Low Confidence in Credit Scoring System

The study found that many Americans don’t have confidence of the current credit scoring system. More than half (54%) of loan applicants don’t even have a clear understanding of why they receive the interest rate they do from a lender, while a majority (70%) say it is difficult finding lenders who will look at them as something other than their credit score.

  •     7 in 10 American adults (71%) wish there was another way to prove themselves to credit lenders outside of the standard credit score.
  •         Hispanics (82%) and African Americans (81%) are more likely than Whites (67%) to want lenders to look at additional factors in lending decisions.
  •     77% believe more data is better when evaluating potential borrowers’ credit.
  •     71% would be willing to share more personal data with a lender if it resulted in a fairer credit decision. The motivation is even higher among middle-class earners. 79% of people making $50,000 to $75,000 would share more personal data to prove their creditworthiness, compared to 66% of people making over $100,000.
  •     84% think their bank should use modern technology to assess their creditworthiness.
  •         Specifically, about half of loan applicants (53%) would like their ideal lender to use machine learning to make fairer credit decisions.
  •         More than 2 in 5 (42%) would like their ideal lender to use machine learning to make the credit for homeownership more accessible to everyone.
  •         Surprisingly, older generations want newer technology even more. Baby boomers and seniors (83% and 87%, respectively) wanted their banks to use new technologies to score them, compared to 79% for Millennials and Gen Zers.

“Consumers are fed up with the current way loans are made. People deserve a better shot at being assessed using factors beyond just their credit score,” said Douglas Merrill, CEO, ZestFinance. “This only confirms what we’ve known for years: That people want more data and the latest tools to be used to score them instead of the outdated process that is still largely in place today.”

Inequity Felt Most by Minority Groups

Survey results found that minority groups, including African Americans and Hispanics, are frustrated with many aspects of how their credit is assessed.

  •     Hispanics (65%) and African Americans (61%) are more likely than Whites (48%) to say the current credit scoring system is set up for consumers to fail.
  •     Hispanics (80%) and African Americans (79%) are more likely than their White counterparts (67%) to cite difficulty finding lenders who will look at them as something other than their credit score.
  •     African Americans (64%) and Hispanics (60%) are also more likely to say they’ve been taken advantage of at some point in the loan process than Whites (45%).

Millennials Also Lack Confidence in System

Minority groups aren’t the only ones who feel they are getting the short end of the stick when it comes to lending.

  •     Nearly 3 in 5 Gen Z/Millennials, aged 18-34, (57%) say they’ve been taken advantage of at some point in the loan process.
  •     Nearly 3 in 5 Gen Z/Millennials (59%) say their credit score has prevented them from doing something important (such as buying/leasing a car or owning a home).
  •     More than 3 in 5 Gen Z/Millennials (64%) say the current credit scoring system is set up for consumers to fail.

 

 

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