Note: Underwriting simply refers to the process of reviewing an application for credit.  In our case, it’s how we review loan applications



Magic Algorithms?

There’s no such thing as a “magic” underwriting algorithm–one that, using data alone, accurately predicts the likelihood of an applicant paying back a loan.  In fact, one could argue that so-called “data driven risk models” do more harm than good (think about the financial collapse and all the statistical geniuses who were behind Collateralized Debt Obligations and other weapons of financial mass destruction).

Still, there’s no shortage of people trying to convince investors and the public that they have just such an algorithm.  An especially poignant example is a company called Zest Cash, the founder of which boldly proclaims that “all data is credit data…” Wow, that sounds fantastic–data mining, algorithms…powerful stuff.  Except that when you go to their website, you see that the Annual Percentage Rate on their loans is 390%.  Let me make this extremely clear: if you are charging 390%, your algorithm is worthless.  You could close your eyes and make loan decisions based on the roll of a dice and still make money at that rate.  It’s laughable.

A Great Offer?

One of my employees recently received a seemingly exciting offer: pre-selection for a loan of up to $450 from Check ‘n Go, one of the largest payday lenders in America!  Given that she works for a company keen on putting them out of business, it seems like they did a poor job with their marketing strategy, no?  This got me thinking, though.  Have you ever noticed that middle to upper-middle income individuals are bombarded with credit card offers in the mail, not to mention the constant barrage of TV and radio ads asking them what’s in their wallet and shouting, “Cash back rewards,” and “Bonus miles”?  Or that if you are lower-income you can’t take a step without seeing bright neon signs, flyers, mailers, and radio and online ads practically thrusting “Instant Cash!” into your hands?  Those least able to afford it are encouraged to make use of the most expensive products, often delivered by companies all-too-happy to hide fees, engage in predatory or illegal practices and wring every ounce of profit from the financially insecure.

Nowadays you can hardly throw a rock at the nonprofit sector without hitting the words “data driven,” “outcomes based,” or “evidence based,” or some other terminology referring to the desire to understand the efficacy of a given intervention.  And it makes perfect sense: given the roughly $30 billion / year that is donated to social service organization, it’s critical that we know whether those funds are making a difference.  In the rush to quantify impact, however, something extremely fundamental has been forgotten: it is shockingly expensive to collect, analyze and report this type of data, and the vast majority of nonprofits lack the funding to do so.

The simple fact of the matter is that when we talk about impact, we are in truth usually referring to outputs: ‘X’ number of people receiving loans or ‘Y’ number of budgets built.  We may go a step further and say that the average increase in FICO score of our clients is 75 points, which is true (and we are proud of that), but it’s also misleading because, as far as we know, that’s only true of the clients we are able to reach for follow up surveys.  In other words, selection bias–the people most motivated to improve their credit are also the most likely to stay in touch long enough to do a survey–skews the numbers in our favor.

I’m going to come right out and say it: I’m incensed.  The reason for my ire?  You may have heard that the Congressional Budget Office (CBO) just came out with a report looking at the impact an increase in the minimum wage would have on the economy.  Well, you may have also seen the headlines about this, which tend to read as follows: ‘Raising Minimum Wage Reduces Jobs, Poverty, Study Says,’ (WSJ) or ‘U.S. minimum wage hike would kill jobs but alleviate poverty: CBO’ (Reuters)

What Are We About?

I’ve thought long and hard about what Capital Good Fund is really about.  Yes, we provide financial services to low-income families and we often say that we are about financial empowerment.  But that’s about as imaginative as saying that Apple is about selling software and hardware. No, if you really want to explain why Apple is the most profitable company in the world, you have to understand that, in essence, they are in the business of delivering a magical experience to its customers.

Does that sound hyperbolic?  Like I’m just another Apple fanboy?  Maybe, but have you ever looked at the eyes of a child the first time she picks up an iPad and starts playing with it?  Have you ever considered why so many people spend so much money just to have a beautiful and functional object they can keep in their pocket or their backpack, especially when there are equally good and cheaper alternatives out there?  Of course it’s because of advertising and consumerism—of course, but that’s missing the point.  And actually, irrespective of your opinion of the company and its products, the main point still remains: what Apple, or any other enterprise, produces and sells is not necessarily the same thing as what it’s about. As Simon Sinek puts it, people don’t buy what you do, they buy why you do it.

Here are are the lessons I’ve learned after 5 years of running a non-profit, illustrated in a simple formula:

X(scale + innovation + implementation + luck) = social change, where X = money

Here are those lessons put another way: the math of social change should be algebraic but rather resembles a calculus problem

Why So Hard?

Let’s consider the non-profit paradigm.  Non-profit begs for money from individuals, foundations, corporations and government.  Money dribbles in.  Money is predominantly spent on programs, because funders don’t like their donations to go toward “overhead” (read that: infrastructure, personnel, marketing, etc.).  Programs result in some good stories that touch the hearstrings of funders.  Money dribbles in again.  Rinse and repeat.

Notice that scale and social impact were left out of that equation.