Removing human bias: How AI empowers brokers to achieve fair lending

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Agarwal said that people are put in a difficult situation where they have to balance all the factors of the loan. If the loan is a complex file, it makes the situation even harder for a broker or underwriter to navigate.

“We ask human beings to do the impossible, we put them in impossible situations, and we ask them to do everything,” he said. “It’s not fair. How many people are out there who get declined, especially redlined by a loan officer, because they just don’t want to deal with it? They’re not redlining because of the race or the area, they’re just like, ‘This is too much work and I don’t want to get it wrong.’ Loan officers and underwriters are really good people put in a bad, no-win situation.”

Generational impact

It’s not just the person who was rejected for a loan who is impacted. Often, if a member of a community is turned down in the effort to get a home loan, it may discourage friends and family from even trying, Agarwal said.

“Hispanic communities and Black communities, they tend to be very close-knit,” he said. “So when your cousin goes and applies and gets declined, it has a chilling effect on the whole community. And then others in the community won’t even try. That’s why Black home ownership has been declining over the last 10 to 15 years, because of this feedback loop. When one person in the community is declined, it discourages 10 others.

“The current stats are that for every two Black borrowers who are declined, one should have been approved. For every four veteran borrowers who are declined, three should have been approved. For every seven Hispanic borrowers who are declined, four should have been approved.”