Jack Dorsey transformed payments with the introduction of Square in 2009 , after launching social media giant Twitter. Square turned convoluted, outdated payments systems into simple, smart payment processing. As the value of its underlying transaction data increased, Square was presented with a unique opportunity to leverage the data for a new line of business: merchant loans.
Using Machine Learning to Generate Revenue
Why Square Capital Works
Square’s core payment processing is a competitive and low margin business that requires economies of scale to grow. The success of their payment terminals generated a large set of transaction data, which can be used to diversify their offerings to merchants and grow their revenue base.
One of Square’s most profitable endeavors has been Square Capital, a new approach to small business lending . Traditionally, small business owners would seek out banks or family & friends to provide growth and working capital loans. Square’s bet is to use machine learning to predict which businesses are most likely to pay back loans, make those loans, then close the loop by automatically collecting payments from the merchants through the Square platform.
How Square Capital Works
Square’s former Head of Data Science, Thomson Nguyen, says the company’s advantage is their ability to provide a fast, flexible, and seamless funding experience . Square’s team of data scientists use their proprietary set of transaction data as a leading indicator of credit worthiness, generating models to surface potential borrowers .
Using Joe’s Pizza as an example:
- Joe’s Pizza uses Square to collect payments for pizzas
- Square sees steady revenue growth and makes a pre-approved offer
- Joe takes a $120 loan at a 10% rate
- Square automatically deducts 2.75% payment processing fee & $10 from every $100 in sales
- Process continues until Joe has paid back the $120 loan
Square stands to make safer loans than traditional lenders who have far less visibility into Joe’s business. This creates a positive feedback loop which further enhances Square’s learning model for future loans.
Square Capital lending platform. (Source: https://squareup.com/)
Continuous Improvement Using Networks
Beyond direct loans, Square also adds value to merchants and potential borrowers by providing insights on how to improve pricing and operations . Using their large database of aggregated knowledge, Square could suggest to Joe the pricing benchmark for his neighborhood is $3.50/slice. This knowledge-share is a win-win proposition, as a growing merchant base will result in a bigger set of potential borrowers.
The company is also seeking to leverage network effects within their community. Similar to lending, machine learning can be used to identify efficiency opportunities by matching up businesses within the Square ecosystem (Joe’s Pizza can get a discount from Sarah’s Sauces). The ability for Square to facilitate collaboration among its businesses can be very powerful at scale.
In 2017, Square filed to be a bank with the FDIC to be able to fund loans using deposits . However, they withdrew their application earlier this year in an attempt to strengthen the application , indicating machine learning alone is perhaps not enough to be the foundation of a sustainable banking system.
In the short term, management should consider augmenting their dataset with data available elsewhere. For instance, they can bring in external weather, holiday, trend, and census data to further improve the accuracy of their model. Thinking ahead, if Square’s goal is to eventually be a bank, they need to figure out how to loan to merchants outside of their ecosystem. As fellow payment competitors PayPal and Stripe move into the lending space , Square needs a strong and defensible way to protect their lending product. While Square holds an obvious advantage with their base of 2 million sellers , they need to think about how to form strategic partnerships to serve the broader multi hundred-billion-dollar small business lending market .
The final point of consideration is learning models are susceptible to real world swings, which can be incredibly difficult to predict. Square, like many financial technology companies, was founded after the 2008 recession and its models were not built to include macroeconomic trends. Large economic fluctuations could heavily affect discretionary spending, particularly at restaurants, bars, and small businesses, which make up a large portion of Square’s customer base. They should look to build in contingencies in case of system wide changes.
Future of Lending
Machine learning can help companies create entirely new lines of business. Square has strong potential to grow their business offerings through continuously expanding data sets. In Square’s pursuit of becoming a bank, can data alone be enough to reliably judge the future potential of a business? As a leader in the space, how should Square thinking about lending, capital, and credit outside of Square?
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