Relative to the days when a singular lending officer would subjectively review applications to grant or deny credit, today’s algorithmic underwriting and credit scoring have helped to create a more level playing field. However, discrimination remains a sensitive topic in the lending industry, given implicit biases and a history of explicit redlining.
Indeed, persistent differences in access to credit and the enduring nature of wealth inequity show that more work is needed.
Risk and portfolio managers can help expand opportunities for minority-owned small businesses – as well as strengthen their own bottom lines – by improving data collection and reporting. Closing the information gap goes hand in hand with closing the wealth gap.
Getting to the CORE
Recently, I attended the inaugural Solutions Summit of the Moody’s Creating Opportunities for Racial Equality (CORE) initiative, which brought together minority-owned depository institutions, community development financial institutions, credit unions and other stakeholders to identify opportunities to increase lending and investment in underserved communities.
Particularly insightful for the risk manager is the lack of public information on the return on investment (ROI) from investing in or lending to minority-owned businesses. While there are many studies quantifying the disproportionately limited amount of capital that goes to minority entrepreneurs, along with the wealth gap across racial and ethnic groups, there is a dearth of standardized, comparable data on the performance of investments in minority-owned companies.
Risk managers can and should address this lack of information, because it is a critical barrier against expanding opportunity. Corporate pledges to increase diversity in hiring, lending and investment are important, necessary steps toward achieving this goal. These programs not only open doors for borrowers but also broadly highlight the benefits that socioeconomic diversity can have on decision-making, product development and risk management.
However, while well meaning, these initiatives are susceptible to backtracking in an environment where business revenues slow – especially without the hard data to clearly demonstrate their ongoing value. Faced with an information deficit, even the best-intentioned CEO may be hard pressed to defend programs from cost cutting.
Quantifying the returns on equity in minority-owned businesses can ensure investments become engrained into the operating model of a lender or investment fund. While a sense of duty can jump-start action, quantification of financial success is needed for that action to be sustained.
Part of the problem is that it’s difficult to find timely data on investment and loan performance across sociodemographic groups. We have seen, however, some evidence of the positive ROI for minority-owned businesses over the past five years.
In 2017, for example, an article published in Small Business Economics provided compelling proof that despite being underfinanced, minority-owned small businesses tend to overperform their counterparts. Consistent with this finding, a 2019 Knight Foundation study of ownership diversity and performance in the asset management industry found “no statistical difference in performance between diverse-owned firms and their peers, even after adjusting for risk.”
Studies such as these cast doubt on the popular suggestion that poorer performance is the reason why minority-owned firms receive less investment than their non-minority peers. Given this evidence, it's natural to ask why more capital isn't flowing to these entrepreneurs. Work by Gary Becker, the 1992 Nobel Laureate in economics, provides both an answer and a solution.
A Theory of Discrimination
Dr. Becker constructed a mathematical framework for understanding the economics of discrimination. Under the strong assumptions of perfect information and perfect competition, he showed that racial discrimination in hiring would not persist in the long run, as non-discriminating employers would earn outsized profits relative to their discriminating peers, thereby driving them out of business. Similarly, non-discriminating lenders could earn higher profits simply by making loans to borrowers with equivalent risk profiles that discriminating lenders avoid.
Of course, the idealized conditions theorized in this mathematical model don’t exist in the real world. Competition is far from perfect, even with laws such as the Equal Credit Opportunity Act and the Fair Housing Act outlawing discriminatory practices.
While differences in market structure persist, technology has reduced the size of these barriers to a large degree. Less visible is the issue of imperfect information.
Wanted: Better Data
Even with a level playing field in terms of equal access, minorities and other groups can be penalized by a lack of data on prior performance. With many minority entrepreneurs obtaining their initial financing from their own personal savings, or from the personal savings of family and friends, their creditworthiness may be invisible to institutions that rely on more formal records and databases.
One of the key takeaways from the CORE summit was the urgent need for better information to demonstrate that minority borrowers and businesses are not only creditworthy, but perform as well as – if not better than – their peers on a risk-adjusted, apples-to-apples basis.
Given the size of the opportunity to profitably invest in and lend to minority small businesses, risk managers should prioritize data collection and analysis. Indeed, facing increased pressure to improve both topline and bottom-line performance in a slowing economy, financial institutions now have more incentive than ever to identify new markets for expanded investment.
Mortgage lending provides a concrete example. With refinancing volumes declining precipitously due to rising interest rates, loan originators have large incentives to work with first-time homebuyers. This may involve greater effort to walk borrowers through the process, as well as consideration of non-traditional measures, such as potential obligors’ history of rent and utility bill payment.
The opportunity to earn a favorable return, while still managing default risk in an expanding market, exists. Improving data collection and reporting is integral to leveraging this opportunity, but risk managers can also improve on the information deficit by reviewing their lending and risk assessment models for potential blind spots.
Central to any regression exercise is the notion that past performance is a reasonable forecaster of future events. However, while useful and comforting for model validation, history is an imperfect predictor. The failure of many credit-loss models to estimate losses accurately during the pandemic is a recent example of the flaw in this assumption.
Similarly, credit models trained on a history that includes very little minority lending, while failing to consider other mitigating factors, may incorrectly assign higher risk to minority-owned businesses and borrowers.
The opportunities to better serve minority-owned small businesses and borrowers are great.
Despite all the challenges facing small businesses – including inflation, lack of workers, and the threat of recession – U.S. Internal Revenue Service applications for new businesses remain well above their pre-pandemic levels. Moreover, surveys demonstrate that immigrant and minority populations remain committed to starting new businesses that are critical for job growth and innovation in their communities and the broader economy.
Community banks and credit unions often lament the fact that they lack the capital to meet all the need and opportunity they see in small business owners looking for loans and lines of credit. These “on the ground” organizations have an edge in assessing risk relative to larger institutions, given long-standing relationships that transcend traditional data. As a senior financial services executive at the CORE Summit eloquently put it, their "return on relationship” is larger.
A great opportunity – what economists refer to as “a coincidence of wants” – exists to bridge the gap between the capital held by large institutions and the connections that smaller institutions have with borrowers.
Risk managers are uniquely positioned to fill this gap through data and models. Those who are successful will not only improve their own institutions’ bottom line but also help their business partners grow, scale and better serve their communities.
Cristian deRitis is the Deputy Chief Economist at Moody's Analytics. As the head of model research and development, he specializes in the analysis of current and future economic conditions, consumer credit markets and housing. Before joining Moody's Analytics, he worked for Fannie Mae. In addition to his published research, Cristian is named on two U.S. patents for credit modeling techniques. He can be reached at firstname.lastname@example.org.