Over the past few years, we have engaged with more than 70 credit platforms. We have drawn many learnings from these onboarding processes. Since all of these platforms have gone through some form of due diligence process with us and since we maintain close relationships to their teams, we have been able to see similarities and differences between them. More interestingly, we have been able to follow platforms’ journeys and how they have changed over the years.
Of course, every credit platform’s journey starts with the founding team. Over time, we have, in general, experienced a strong increase in the quality of founding teams in terms of execution, speed, and quality. Several years back, one could roughly say that there were two types of founding teams:
- Manual: former credit specialists from banks;
- Technology: entrepreneurs with a tech background.
Accordingly, there were two major types of platforms:
- The slow, manual originators which had very good credit quality right from the start but limited origination capacity and slow growth;
- The automated platforms which struggled to keep credit quality stable under growth conditions.
After an evolutionary process, those which survived were both the manual platforms that were able to introduce new technology and the tech platforms which were reasonable enough to slow down growth in order to keep credit quality stable and build manual operations alongside for credit servicing purposes.
Irrespective of the degree of technological sophistication, the main target group of customers for credit platforms used to be the unbanked, the underbanked, or the unsatisfied bank customer. The challenge of this model is that oftentimes there is a reason why a customer is unbanked, underbanked, or unsatisfied, and this reason has to do with credit quality.
The obvious hypothesis for the success of these models is that there are enough of these unserved customers. But the hidden (and more important) hypothesis is that the combination of risk model and customer acquisition strategy results in strong enough discrimination power to separate the relatively few good customers from the relatively many bad customers in an environment with a difficult signal-to-noise ratio.
Or in other words, due to the more difficult hunting ground, the risk models of such platforms need to be superior compared to bank models, which generally have, on average, a higher quality lead inflow.
Not all platforms we have met have understood that this game is won by the strength of the customer acquisition strategy. As an example: if you advertise “credit without credit bureau” vs. “digital credit application”, not only will you have two different unit costs but your leads will also be disparate, putting your risk model under stress.
Now, what does that mean for growth? It is practically impossible to forecast the behaviour of customer quality if you, for example, double your marketing expenses in such models. On top of that, the costs of goods sold go through the roof in online marketing models surprisingly quickly. The only way to scale with constant quality and acceptable customer acquisition costs is by establishing a strong brand.
Accordingly, the refinancing options for such models were limited to debt with significant skin in the game until the brand was strong enough to justify trust in stable quality and therefore, capital market structures became an option.
Over the years, we have seen a lot of innovation in the lending space. To summarise this trend: lending models have become much smarter. Instead of establishing alternatives to plain vanilla bank loans and trying to win the customer acquisition arbitrage game, the current generation of lenders uses technology to offer a superior and integrated lending product, reducing risks through smart product design.
What this means in practice can range from ecosystem lending (providing loans to vendors on a platform) and flow of fund lending (e.g. invoice factoring), to revenue-generating lending (e.g. pay-per-use of machines, the financing of customer acquisition costs) and convenient purchase lending (buy-now-pay-later or renting / leasing electronics).The full service rental of cars (with embedded finance) is another example. Of course, the list goes on in the B2B and SME world where we see, for example, used car purchase platforms with financing needs or embedded factoring and financing in accounting systems for corporates.
The universe of smart lending is large. And it is the smartness (which translates into superior products and services) that leads to a willingness to pay for this convenience. Consequently, the margins for smart lending fall into a range that banks with a commodity product stopped dreaming of years ago.
Ironically, the act of competing with banks for “the good customer” has turned around. While we heard earlier that banks used to have the upper hand in terms of the quality of customer inflow while the leftovers were being processed by the lending platforms, these new types of lenders have since turned the tables. Only if you cannot finance your TV upon checkout when shopping online will you go through the pain of applying for an unsecured loan with your local bank. Only if you cannot refinance your inventory through the embedded solution in the ERP system will you go to the bank and endure a paper process (which ironically the bank will claim is “digital” since the paper is scanned and exchanged via email or uploaded) .
Again, what does all this mean for growth? Since the implementation of technology is suddenly no longer focused on the digital processing of a traditional credit application but instead, the technology itself is introducing value, there are several key advantages to consider:
- The costs of goods sold (COGS) do not increase under growth due to product-linked or hardwired customer inflow channels;
- Credit quality remains constant under scale;
- Market size can be clearly analysed.
All in all, smart lending platforms can handle scale much earlier, while needing scalable refinancing much earlier.
Accordingly, once the customer-facing technology product has been set up, the decisive factor for success is the fast establishment of a scalable funding structure so as to not risk a competitor coming in with a similar product. The capabilities on the capital markets side for such smart lenders become important much earlier in the life cycle.
Also, for investors, the acceptable length of a track record becomes shorter due to the fact that smart lenders are not addressing new segments of borrowers but existing segments of borrowers with superior technology.
CrossLend provides capital market technology for smart lenders, linking their funding streams into their investor community. Simpler deal making, faster growth, better returns.