The $2.6T Problem Nobody Has Solved – The overlooked credit gap in SME finance

 

A Thought Experiment…

Let’s start with a thought experiment: A stranger comes up to you on the street and asks for a loan — would you do it? What would you need to know? How would you get that information? Personally, I would start by looking through their phone.

There are upwards of 2B unbanked people, akin to our stranger on the street, in the world today, who have no financial footprint. Without proper financial data, traditional lending institutions have to rely on manual processes enabled by legions of loan officers to provide credit to this capital-starved market. However, the proliferation of mobile is changing the game. Although the unbanked don’t have a financial footprint, 90% of them do have a cell phone full of data.

 

Finding the Needle(s) in the Haystack

So what exactly is the value of mobile data? There are many promising mobile-focused consumer lending startups that are actively answering that question and as a result, our thought experiment. Whether it’s Tala in Nigeria, First Access in Tanzania, or Cignifi in Uganda and Ghana, it has been proven out that there is a market for lending based on your “mobile credit score”.

Instead of relying on traditional financial documents, these startups get creative when thinking about credit risk by diving into SMS, contacts, and even your phone’s metadata. For example, what if I told you that you are more creditworthy if you add last names to your contacts on your phone? Or maybe that if you exclusively use upper or lowercase when texting that you are less creditworthy? Needless to say, the mobile age has opened the door to a treasure trove of data that can be mashed together to provide a proxy to traditional credit scoring like the FICO score.

Although it’d be tough to claim that any of these models would be as effective as a “FICO”, by diversifying across hundreds of predictive data points, these startups can get close enough. In emerging markets where financial and transactional data is hard to come by, this is a game changer. After all, loan officers need to sleep — software doesn’t.

 

What Valuable Company is Nobody Building?

Although the opportunity in consumer lending is exciting by itself, there is another — often overlooked — market that has fewer unique users, but is similar when looking at the underlying problem, the size of the opportunity, and the scope of impact. That market is SME financing. To quote the World Bank:

“…access to finance is a key constraint to SME growth…SMEs are less likely to be able to secure bank loans than large firms; instead, they rely on internal or “personal” funds…”

In fact, here are three numbers that will give you an idea of exactly how big this market is:

365M – 445M: number of unique SMEs in emerging markets

70%: percentage of those same SMEs that lack access to credit

$2.6T: total value of estimated credit gap

Needless to say, SME financing is a lucrative opportunity for any startup that can solve the puzzle of credit scoring and mobile data could be the key.

 

Credit Risk Starts from the Top

How does mobile apply to SMEs? Well, let’s take the United States as an example. In order to get a credit rating, businesses rely on agencies like Moody’s, Standard & Poor’s, or Fitch Ratings. Unfortunately, getting a credit rating for a small business can be prohibitively costly. As a result, banks rely on good ol’ fashioned, read: manual, due diligence when evaluating a small business loan. These lenders look at the owners’ background, resume, business plan, personal credit report, financial statements, collateral, and so on.

Yet, there’s something peculiar about that laundry list of documentation. Even though you need the loan for your company, bankers ask for quite a few personal details. The reason is simple: if your business is just you, Joe and Sarah, then your personal credit profile is going to be just as, if not more, predictive of credit risk as your business’s balance sheet.

Why is this important? Well, because that means you can apply consumer-inspired mobile credit scoring strategies to SME financing.

 

Current State of SME Financing

Unfortunately, most startups attempting to break into the SME financing market continue to default to traditional methods of risk assessment. For example, companies like Ondeck and Lending Club rely on FICO scores and partnerships with eCommerce marketplaces to power their predictive risk models. Even SME financing startups outside of the US, like Lendingkart, default to similar approaches. Ignoring the fact that FICO only exists in the US, by relying on partnerships, these companies open themselves up to getting cut of the value chain by the same eCommerce players that were originally their brothers in arms.

In fact, companies like Square and Alibaba (Ant Financial) are doing just that, by tapping into the hidden value of the transactional data that runs through their platforms to dramatically scale their lending operations while keeping default rates low. For example, Square Capital has historically only lent money to businesses already using Square for their operations. By tapping into the transactional data for each company, Square has invaluable data on each borrower’s annual revenue, growth, and seasonality, which can then be used to accurately estimate credit risk. The result: 4% default rates while growing 100% Y/Y.

That being said, players like Square and Alibaba also face a natural obstacle to growth: their market share is limited by the reach of their platform. Moreover, it would be hard to scale faster than the underlying business. For example, if there isn’t any market penetration in Sudan for Square, then it would be impossible to capture the value of the SME financing market in the country. By focusing on their proprietary transactional data, these players are effectively leaving a majority of the money on the table.

 

Finding the Greenfield

In conclusion, there is a significant market need for a scalable solution to SME financing, but a clear winner has yet to emerge. Currently, there seem to be two archetypes of disruptors in the field, the middlemen, and the platforms. Both archetypes face significant challenges to viral and scalable growth, which opens the door for the third archetype of disruptor, hybrids, that can incorporate the lessons learned by consumer lending startups in emerging markets to SME financing. By tapping into the wealth of data in mobile phones, these hybrids can channel knowledge that traditional financial institutions already utilize to assess SME credit risk, but through a scalable model.

 

 

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