To Lend or Not To Lend

Over the last decade many fintechs have contemplated this very question, with some choosing to lend, some choosing not to, and yet many others ending up in the undiscovered country from whose bourn no traveler returns as a result of doing lending badly.

When dealing with such existential questions, a simple framework can be of great use, so let’s examine one here, with some real-life examples of each instance where it makes sense or not to embark upon a lending journey.

In the simplest of terms, the economic equation from lending can be described as follows:

Π = NPV(Y) – X  where

Π = Profit from lending

NPV = Net Present Value of future cash flows at the firm’s cost of capital

Y = ( [Interest + Fee Income] – [Interest + Fee Expense] – OpExCost – Losses)

Χ = Customer Acquisition Cost (CAC)

So in simple terms, a firm can expect positive economic profits from lending if it is able to charge its customers interest and fees that exceed all its expenses which include cost of funding, operational expenses, losses from credit and fraud exposure, and customer acquisition costs including all marketing expenses.

Now, it is very important to keep in mind that this equation exists in the context of a very competitive market, and in striving to generate a positive profit from lending, a firm will be competing with others that are trying to do the very same thing. Much like the story in which two campers come across a bear outside their tent, the key to survival is being able to run faster than their fellow camper even if one cannot outrun the bear. 

In other words, to embark upon lending successfully one needs to have a competitive advantage in ideally all (being able to outrun the bear), or at the very least one (being able to outrun the other camper) of the key areas outlined in the equation above.

We can now look at some real-life examples from each of the categories.

Traditional players like banks have a clear competitive advantage when it comes to cost of capital given their vast branch networks where they hoover up deposits at close to zero marginal funding cost. A tech enabled fintech may have an advantage when it comes to operational cost given state of the art infrastructure that automates many manual processes. Players like Capital One used vast databases and superior analytics to keep their losses low relative to their interest income, in other words data mining pockets where the risk reward equation was superior. Yet other players may have found superior customer acquisition tools by virtue of a unique partnerships or value propositions to their customers.

Interestingly, while having a competitive advantage in some of these areas is a prerequisite for not being eaten by the proverbial bear, it does not mean that going down the path of lending is the best decision for that particular firm. Playing in just one aspect of the value chain may in fact provide a higher return on equity than lending which is capital intensive.

Let’s take the prior example of a fintech company that has the infrastructure that enables it to originate and service loans at a lower operational cost compared to anybody else. It may in fact be more profitable for that company to outsource its origination and servicing technology to other players, thus being an infrastructure provider vs an actual lender itself. QED’s portfolio company Amount is a great example of this very case.

Another example would be a company that has a clear advantage in customer acquisition costs. Rather than using this to become a lender, the company could become a loan originator or broker, and originate loans for lots of other lenders. Again, from CreditKarma to Capitalise, there are many examples in the QED portfolio of companies that have adapted this approach.

So, to summarize, founders that are thinking about taking their company down the path of lending first need to ask themselves if they have a competitive advantage in any of the key areas needed for success. If the answer is affirmative, then the next question is to determine if the company is better off selling its unique skills to the broader market, or becoming a lender itself. The answer to this question is to a great extent determined by how tradable those skills are: licensing tech infrastructure, or a loan originator selling leads to a lender is relatively easy. On the other hand for Capital One to transfer the complex analytical framework it has developed over the years is somewhat harder and likewise it is hard for a big banks to “sell” their cheap cost of funding (though they can monetize their advantage in other ways, by for example becoming a wholesale funder of other smaller lenders).

Finally, let’s look at a few examples where it does not make sense to become a lender. One example that we have often seen is when founders mistake having good data scientists as a sufficient condition for being able to create a good risk reward equation (interest income relative to credit losses). While having good data scientists (or statisticians as we used to call them back at Capital One) is undoubtedly a crucial ingredient, it is by no means enough in and of itself. The models and scores that are produced by the data scientists need to be placed in a wholistic business context, where the business analyst takes the model output and determines where to make the right cut offs and tradeoffs. For example, a data scientist with a model can produce an expected fraud probability score for a credit card transaction, but a business analyst needs to determine the optimal threshold where a transaction is declined or approved taking into account the business impact of false positives as well as false negatives. In other words, credit is a culture, not a model. Building the right organizational structure to get it right is not easy.

Another example that comes to mind are companies trying to engage in what could be described as “multiple arbitrage”. The classic example of this is a fintech start up (for example a mortgage broker) that gets allured by the high revenues that lending can provide, and starts lending, hoping that it can attract a tech multiple on its lending revenues. While this may work with some investors initially, if the company does not have a competitive advantage for lending in at least one of the key areas, the bear will catch up with them eventually.