The S-Curve

A More Equitable Lending System Will Not Be Created by Accident

Andrew Davidson

Around 75% of white American families were homeowners in the first quarter of 2020, according to data from the United States Census Bureau. However, only 44% of Black American families owned their homes at the same time. This gap is larger than it was in 1960, when racial bias in mortgage lending was a matter of policy in many states. Similarly, insights from the Federal Housing Finance Agency demonstrate significant differences in mortgage application approval rates between white Americans and minority applicants. In 2020, for instance, white Americans had a more than 15% higher chance of being approved for a loan based on HMDA data.

Even as other deeply embedded social institutions have become more inclusive of people of color, equal access to housing is seemingly out of the country’s grasp. That is largely because the modern lending ecosystem is underpinned by the very technologies and processes that were originally designed to facilitate discrimination rather than prevent it. Fair lending laws alone have been unable to narrow the yawning gap in homeownership rates.

With this in mind, how do we prevent homeownership inequities from persisting well into this century?

For many, the answer to that question is simple: We would use artificial intelligence. After all, credit score algorithms and automated mortgage application reviewers do not see skin color. In practice, however, integrating unbiased AI-powered technologies into a biased lending system presents significant obstacles.

The Human Obstacle

Human lenders have both implicit and explicit biases that might affect what products they offer to prospective borrowers and how they view and evaluate loan applications. The promise of AI-driven advanced analytics is that it could remove those biases from application assessments and instead focus solely on the facts.

First, we must recognize that AI in general is more promising than reality in many fields, and it might take time before an AI system could reliably make credit determinations. Moreover, AI development often perpetuates current processes rather than creating a new way of thinking.

Traditional mortgage lending metrics (e.g., credit scores) are focused on identifying borrowers who are simply likely to default in the short term. With or without AI, new approaches to evaluating credit could deliver insights on underlying borrower characteristics that are more indicative of long-term creditworthiness. By evaluating a wider range of data (in addition to borrower credit history, income, property value, and other relevant information), we can begin to create a system that supports homeownership rather than discourages it. Nevertheless, that will not happen by accident.

The Power of Purpose

Although policy has struggled to create a fair and inclusive path toward homeownership, technology can still succeed — but only if its creators deliberately include the elimination of racial bias in mortgage lending as a key success metric. Industry innovation does not necessarily result in progress for all Americans; achieving greater equity will be difficult if we do not pursue it directly.

As such, the data scientists and developers building AI technologies today must use decidedly different approaches from those employed in the past. Specifically, they should be trained with data that ensures less consistency with “historical” approaches in pursuit of greater equity for all.

What does this mean? In short, we have centuries of data on loans and mortgages issued in the past, but given the inequities we are seeing, it is likely not representative of a system that enables equal access to housing. Rather than training algorithms using just historical credit data, we should envision a more just scenario and incorporate data that corresponds to a more equitable world. A reliance on flawed inputs will only exacerbate existing biases.

Of course, the surge in companies such as Rocket Mortgage and other online lending platforms has already reduced the need for in-person applications, which in turn has helped to reduce discrimination significantly. Nevertheless, as an industry, we cannot assume that technology alone will solve a problem that is centuries old.

The Human Solution

Today, there is a tremendous amount of data available to power algorithms that make the mortgage lending process more streamlined and efficient. The question, now, is this: Can that data also help create equal access to housing for Americans of color? 

Not without our help and our focus on addressing inequality.

The AI technologies and mortgage risk analytics that anchor tomorrow’s lending processes must be designed with the express goal of eliminating racial bias in mortgage lending. Misuse of data can promote additional discrimination, and failing to keep this in mind will result in new technologies that simply do not account for the struggles of marginalized communities. To avoid perpetuating systemic inequities that have spanned generations, actual intelligence is needed.

A magical solution does not exist — but by combining the power of human foresight with the power of emerging technology, we could certainly take many more steps forward.