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.

It is hard to run faster than a bear

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.

Embedded Lending

There has been a lot of interesting discussion in the fintech community in the last year around embedded finance. It typically refers to financial products being seamlessly integrated into any non-financial business or service, with a common example cited being how Uber and Lyft integrated payments into their ride-sharing and mobility offerings. Many industry insiders are now predicting that this trend represents the next stage of the evolution of fintech and will spread beyond payments into all of banking and finance.

Based on some of the new business models that have been emerging in the last couple of years, embedded lending represents a particularly interesting subsegment worth highlighting here. Embedded lending is by no means a new phenomenon, and to understand how powerful embedded lending can become in the future, it is worth doing a short recap of the journey thus far.

Early examples of embedded lending include products such as the credit card, which one can think of as a payments product with lending embedded into it. Likewise, Fintech 1.0 companies such as QED investments GreenSky and Klarna are both great examples of how lending can be seamlessly embedded into retail and e-commerce, enabling a very smooth point of sale finance experience for end users.

Today’s embedded lending builds on this rich heritage of financial innovation and uses modern technology to take the product and user experience one step further. These new and innovative companies are characterized by three key features that they have in common.

The first key feature is seamless operational integration. With the APIfication of data, cloud computing, and all other innovations taking place in the info tech and data tech space, financial products can now be integrated with operational processes so seamlessly that much like the invisible man of H.G. Wells’ famous book of the same name, they are very much there but one cannot notice them. This integration is in turn supported by the rich ecosystem that has emerged in financial infrastructure and reg tech, where modular business processes can be used like Lego blocks to build new business structures, all connected to each other with API calls. Needless to say, it took decades of innovation in information technology to get here, from the emergence of C++ and Java as modular coding languages to today’s academic work on how to create abstraction layers from any computer language.

The Invisible Man of H.G. Wells – he’s there but you cannot see him

The second key feature is a realignment of business relationships and incentives to enable better commercial outcomes for all parties involved. Because legacies weigh heavy on institutions (and societies too in many cases!) some of the business and commercial relationships of today are shaped by the technological constraints of the past that no longer exist. There are countless examples of this, but just to pick a random one, consider the signature. In today’s world of Face ID and digital documents, why do I need a wet signature to prove my identity? Or why do I even have to show up in person to prove who I am in the first case? Undoubtedly, the list can go on, but the point here is that the new models that are emerging not only use new technology, but use that new technology to challenge the business logic and conventions of the past, especially where these were driven by constraints that no longer exist.

The third key feature, which is a direct consequence of the two prior ones, is that losses tend to be an order of magnitude lower compared to a legacy lending product. Both because of the closer integration as well as the new alignment of incentives, where in many cases significant credit risk existed, this credit risk starts to approach levels close to zero, instead being replaced by various levels of operational or systemic risk. Alternatively, in some cases an entity’s high credit risk gets replaced by another entity’s significantly lower credit risk. Again, there are countless examples one could give here, but just to consider a fairly common example, one can think of supply chain finance, where a small business is selling products to a large corporation, for example a small supplier selling tomatoes to Tesco. In the legacy world, when the small supplier would go to the bank to request a loan for working capital to fund its tomatoes, it would be charged a high rate of interest because it would be seen as a risky business due to being small and having little capital. But with the right kind of integration and fintech product (such as an invoice finance solution), the risk of the small supplier can be substituted with the risk of the big client (Tesco in this example), so the supplier can now borrow close to the low rates that Tesco can borrow at.

Another interesting example of embedded lending is Wayflyer, which provides e-commerce companies with software to optimize their marketing spend on platforms such as Google and Facebook. Wayflyer’s software is fully integrated with the e-commerce companies, and it helps them allocate their marketing spend online to best capture new customers and grow online sales. However, as a function of doing this, Wayflyer also sees where these e-commerce companies hit pay dirt when for example a new segment of customers that are very hungry for that particular product is uncovered. In this particular case, Wayflyer, which is integrated with the company can seamlessly offer its client extra marketing dollars, which quickly get converted into sales, and then get paid from the proceeds of this incremental sales automatically when the sale happens. From the e-commerce company’s perspective the financing is almost invisible – they just think of Wayflyer as a partner who helps them grow faster by making better marketing decisions and serving as an extra pocket to boost their marketing spend where needed. There are certainly many more examples of this, and some of these businesses are yet to emerge at scale, though they will undoubtedly do so in the not too distant future.  Two particular areas that are very interesting are student finance and property purchases. In the case of the former, Student Finance is working on a model where anybody can go to a vocational training program with no upfront payment, and only pay back the cost of the education when they get a higher paying job as a result of that training. While this is technically a student loan it is very much embedded into the education process itself, and from the perspective of the student they are investing in their intellectual capital and getting a better job in the process – the loan itself is invisible!