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Racial Bias in the financial system is nothing new, but the way it has played out across the $806 billion Paycheck Protection Program as it relates to the Black-owned businesses is beyond alarming. Researchers from New York University’s Stern School of Business found that Black-owned businesses were 12.1 percentage points more likely to obtain their PPP loan from a fintech lender than a traditional bank. Is it possible that artificial intelligence (A.I.) automation might help reduce racial discrimination in the loan origination process?
The Breakdown You Need to Know:
A.I. to the rescue, well maybe. CultureBanx noted that simply stated, what makes A.I. based lending much more altruistic when it comes to loans, is that they don’t want to leave any money behind. The NYU researchers discovered that when small banks incorporated automation into handing out loans, the technology “more than doubled small banks’ propensity to lend to Black-owned businesses.” They also found smaller banks were much less likely to lend to Black-owned firms, while the Top-4 banks exhibited little to no disparity after including controls.
Part of the ongoing lending discrepancy which has made fintechs a better bet for Black-owned businesses could be their systems rely on “simple automation” in which computers merely verified if a loan recipient’s driver’s license was fraudulent or not. In stark contrast, small banks may have relied on humans to verify each recipient’s driver’s license as part of the identification verifying process, and people’s personal prejudices against people of color could have crept into the overall decision making.
The simplicity of fintech’s might give it an advantage in being less biased than deep learning software that attempts to deduce whether a person’s face belongs to a white male or a black female, among other tasks. Research shows commercial deep learning software systems tend to have higher error rates for women and Black people. Some facial recognition systems would only confuse light-skin men 0.8% of the time and would have an error rate of 34.7% for dark-skin women. A recent IDC report noted it expects worldwide spending on cognitive and A.I. systems to reach $77.6 billion in 2022.
Artificial Action:
Remember artificial intelligence systems inherently learn what they are being “taught”. The NYU researchers found evidence that “when small banks automate their lending processes, and thus reduce human involvement in the loan origination process, their rate of PPP lending to Black-owned businesses increases, with larger effects in places with more racial animus.” So, if lenders were to discriminate in the accept/reject decision, it would imply that money is left on the table. …(s)uch unprofitable discrimination must reflect a human bias by loan officers.
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