Your loan is more likely to be approved if your bank does

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There is good news and bad news when it comes to credit bureaus and biases.

The good news is that credit reporting companies are taking active steps to reduce historical biases in credit reporting decisions, such as factoring lease payments into credit decisions and improving the accuracy of predictive tools. which are generally less accurate (up to 10%) for low-income and minority credit applicants.

The bad news? A historic bias still exists in the credit scoring market and it hurts financially disadvantaged U.S. lenders and creditors who need to step up and rectify the problem.

“It’s no secret that the financial industry has a history of bias,” said LBC Mortgage founder Alex Shekhtman. “From redlining to subprime lending, minority communities have long been underserved by lenders and credit providers.”

Although many institutions have worked to address these issues in recent years, historical biases continue to be a problem.

“A good example of historical bias would be when a financial institution only approves loan applications from people with a certain level of income or who own their own home,” Shekhtman told TheStreet. “This type of bias can prevent people on low incomes or who rent their homes from accessing credit and loans.”

Therefore, historical and systemic bias is a huge problem in the most widely used credit scoring models in the United States.

“Ways of judging ‘creditworthiness’ were determined in the 1950s and have not caught up to reflect today’s consumer,” said Zest AI general manager Mike deVere. “Because of this outdated system, millions of people in the United States are unable to get a loan. When you use more data and better math, they would realistically be a great person to approve a loan. .”

This scenario presents real problems for credit consumers in 2022.

“People can’t get a mortgage, buy a new car, pay for higher education because the system isn’t fair or accurate,” de Vere noted.

Zest offers an artificial intelligence solution

Fintech companies that help lenders and creditors eliminate historical biases in their credit decisions are emerging, and that’s good news for the credit scoring market.

Take Zest AI, which takes “more approvals, less risk” for credit application decisions.

Lenders using Zest have seen, on average, a 25% increase in approval rates with no added risk, and see a 40% reduction in chargebacks, along with solid improvements in automation and inclusive lending.

At First Service Credit Union in Houston, Texas, institution officials have moved away from a manual loan underwriting process and leveraged an auto loan model built by Zest to explore over 12,000 credit variables to find 250 data points that generated a much more accurate view of applicant creditworthiness.

The credit union saw its loan approval rate increase by 23% with less credit risk and less than two-second loan decision times.

Zest AI was able to lead the way to better loan outcomes and no historical credit bias by leveraging a simple AI-driven process – more data and better math.

“Our AI-automated underwriting technology is trained to take thousands of data points about a borrower, determine the top 200 or more that best indicate someone’s ability to repay a loan, and spit out a decision that’s consistent. and fair,” de Vere told TheStreet. “When you can label borrowers more accurately, you can better eliminate bias in your lending because you’re actually able to prove you’re lending fairly.”

A path up for disadvantaged borrowers

For consumers who can’t seem to get a fair deal from lenders and creditors, AI-powered credit models could be a green light.

“The problem is that there’s a black box that prevents consumers from really understanding how their credit is decided, so they probably don’t know it,” de Vere noted. “But they need to understand that they’re not the only ones who think their credit score is inaccurate.”

One step credit consumers can take is to advocate for measures with their elected representatives, such as integrating AI into loan underwriting, that would improve the accuracy and fairness of credit decisions. .

“Consumers know they are more than a three-digit score and deserve fair access to credit,” de Vere said.

Financial institutions also need to step up and improve their track record of credit decisions based on historical bias.

“On the one hand, they can make an effort to increase the transparency of their decision-making process,” Shekhtman said. “That way, consumers can see why they were denied credit or why they received a higher interest rate.”

Shekhtman suggests three additional ways for lenders to ameliorate credit bias issues.

· Financial institutions can work with community groups and organizations to ensure that their products and services are accessible to everyone.

· Financial institutions can provide historical bias training to all employees involved in the lending process. “It will help employees identify instances of bias and take steps to avoid them,” Shekhtman said.

· Finally, institutions can provide financial education resources to help consumers understand the credit and lending process.

If you feel like you’ve been the victim of historical bias in a household’s credit or loan decision, there are a few things you can do.

“First, you can contact the lender or credit grantor and ask for an explanation,” Shekhtman added. “In addition, you can file a complaint with the Consumer Financial Protection Bureau or your state attorney general’s office.”

“It’s also a good idea to consult a housing counselor or civil rights attorney for more information about your credit rights and options.”

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