!-- Global site tag (gtag.js) - Google Analytics --> Marijuana Business Magazine March 2020

Marijuana Business Magazine March 2020

Marijuana Business Magazine | March 2020 20 Good Tree Capital’s mathematical model can predict whether a borrower will default Loans Without Bias S eke Ballard is the founder and CEO of Seattle-based Good Tree Capital, an online platform that raises capital from accredited investors and uses those funds to underwrite small-business loans to vetted, licensed cannabis operators nationwide. Good Tree Capital’s average loan is $75,000, and its biggest financing so far has been $250,000. The company issued its first loan in 2017 and has since built a portfolio of borrowers in California, Colorado, Massachusetts, Oregon, Washington state and Illinois, where it is particularly active in the social equity space. How does Good Tree Capital do things differently, and how does your business model help affect the changes you want to see in the cannabis industry? Good Tree Capital’s mission is to reimagine how traditional banks lend money, using more data and less human bias. The current system of underwriting small- business loans—with its heavy reliance on the loan officer—is inefficient and ripe for disruption. The first issue is cost. Traditional banks employ a network of expensive loan officers working in brick-and-mortar branches to evaluate applicants’ creditworthiness. To preserve profitability in a high- cost environment, banks have created minimum loan thresholds (typically less than $250,000) under which they will not lend. The problem is that 68% of all small business loan applications are under $250,000, which means these applicants are largely overlooked by banks and forced to pursue higher-interest-rate, unsuitable alternatives, such as credit cards. The second issue is bias. Imagine for a moment that you have two loan applicants with the exact same profile. They have the same credit score, income, outstanding debt and assets—they are essentially identical except for one attribute: race. If one of those applicants is black and the other is white, the black applicant is 2.7 times more likely to be rejected for the loan. And if the black applicant succeeds in getting the loan, he or she will pay on average 180 basis points more in interest. This same basic imbalance exists generally between men and women as well. How does your company try to avoid such unequal treatment? Good Tree Capital’s mission sits at the intersection of these two market inefficiencies, both of which trace their origin to the loan officer. After analyzing over 1.2 million loan records from the Small Business Administration, we built a mathematical model that uses only the financial and operating data about a business to predict, with 98.2% accuracy, whether that business will default on a loan. And unlike a loan officer, our technology can evaluate thousands of loans simultaneously with zero incremental cost. But most important is the fact that we built our algorithm with intentionality, avoiding factors that have no predictive value such as race and gender. Our output is a more equitable distribution of capital with borrowers who more closely reflect the diversity of our society. MoneyMatters | Nick Thomas We built our algorithm with intentionality, avoiding factors that have no predictive value such as race and gender.” —Seke Ballard CEO of Good Tree Capital