Fintech, Small Business & the American Dream

by Karen G Mills

  In the early days, the lack of federal regulatory oversight of non-bank lenders was perceived to be an advantage. Banks were reeling from increased compliance costs caused by Dodd-Frank, leading many to predict that the disruptors would see large benefits from “regulatory arbitrage.” However, the lack of federal oversight was a double-edged sword. The inability of non-banks to obtain a federal charter may have actually inhibited the national growth of online lenders, as they were forced to charter themselves state by state, use a bank partner, or create products that did not qualify as loans.

  The early fintechs had clearly initiated innovations that reduced some of the barriers to a smoother matching of borrowers and lenders in the small business marketplace. They created an advantage by using technology to deliver an easier, faster digital customer experience, but it was unclear whether that advantage was sustainable. Although fintechs were the first to catch on, there was nothing preventing incumbent banks from imitating or even beating them at their own game.

  By 2018, the market was consolidating, as peer-to-peer lending stopped growing, and traditional banks increasingly incorporated fintech innovations via acquisition, imitation, or partnership. Perhaps that is why the mood at LendIt 2018 was less exuberant than when it started in 2013, and why the conference had diversified by adding an entire track dedicated to blockchain technology.

  * * *

  Despite the stops and starts, the fintech innovation cycle was underway. The early movers had shown that it was no longer acceptable for banks and other lenders to provide the same small business products and service levels they had for the past several decades. Small businesses had gotten a taste of a new level of service and were in search of more. The stage was set to see how technology might change the game—how additional innovations from current players or future entities might serve the financial needs of small businesses in novel ways that were affordable, integrated, and intelligent.

  © The Author(s) 2018

  Karen G. MillsFintech, Small Business & the American Dreamhttps://doi.org/10.1007/978-3-030-03620-1_8

  8. Technology Changes the Game: Small Business Utopia

  Karen G. Mills1

  (1)Harvard Business School, Harvard University, Boston, MA, USA

  Karen G. Mills

  Email: kmills@hbs.edu

  On a Thursday morning at 5:30 AM, Alex sipped her latte, her elbows atop the service counter. Each day at this time, the sunlight through the front window blanketed her coffee shop and she enjoyed a few moments of peace and quiet before the morning rush began. With 30 minutes to spare before she corralled her baristas for their morning pep talk (and shot of espresso), she unlocked her iPad and pulled up her most valuable assistant: her small business dashboard. A graph on the upper right predicted her cash position at the end of the week. After payroll expenses, she would have $5,000 left over. In seconds, Alex’s supply advisor scoured her accounts, sales and expense histories, local weather forecasts, event information, and past tourism data, and told her she would need five new sets of filters and 1,000 plastic cups for the coming week. She ordered them from Amazon with a single touch. She also knew the shop needed a new espresso machine, but she had been putting it off for over a month. With the savings in her account, she could either order the new machine now or make a payment on the term loan she had taken out two years ago to start the business. If she continued to put off a replacement, the machine could break at any moment, and espresso was the second-best selling item on the menu after iced coffee. On the other hand, she was almost done paying off her loan, and procrastinating another month would add interest.

  Alex asked her robo-adviser for advice. “You can do both,” it reported. “Given your expected sales for the month, it looks like you’ll be able to use your savings to pay down the loan and put the espresso machine on your credit card, which has available credit of $3,500. When the credit card payment comes due in 30 days, you will have the cash to pay it off, based on current sales projections.”

  Alex ordered the espresso machine and paid down the loan, and for good measure, she delayed paying herself for a week, knowing she had enough money in her savings and that sales would jump next week, when the school year ended and summer vacation began. Just to make sure there were no mistakes, she ran an instant credit check on herself, in which her bank bot confirmed she had $3,500 of available credit, and then she double-checked her projected sales based on prior years. Remembering that Dunkin’ Donuts had recently opened down the street, Alex asked her bot for sales ideas to ensure they met their goals for the first week of summer vacation.

  “It is going to be above 75 degrees next week, so iced coffee, which has a profit margin of 53 percent will likely sell more than usual. Dunkin’ Donuts is running a sale on iced coffee next week. When they have run similar promotions in the past, you have lost an average of seven of your daily customers. If you send your regular customers a coupon for $1 off iced coffee, I estimate you will increase your margin for next week by 3 percent. Would you like me to send an e-coupon to your regular customers now?” With one tap, the coupons were sent. After the morning pep talk with her staff, Alex opened the doors for the day, confident in where her small business was headed.

  At the end of the day, as Alex was closing up, her bot reminded her that it was June 1, and that quarterly taxes would soon be due. She momentarily worried that she had overlooked her tax payments when buying the new espresso machine, but then the bot said, “Don’t worry. Your estimated tax payments have already been accounted for in your cash projections for June.” Finally, with a few more taps and swipes, Friday’s payroll was set, healthcare deductions were taken from her employees’ paychecks, and taxes were ready to file.

  Small Business Utopia

  Alex’s story allows us to imagine a potential golden age of small business financial services that fintech innovations could deliver for small business owners. We call this future state “Small Business Utopia.” Alex has access to the capital she needs to operate and grow her business, she can easily understand her cash flow, and she has real-time insight into customer acquisition and sales techniques that can help her business prosper. In this story, a machine augments Alex’s ability to run her business through artificial intelligence that collects a range of data, knows how to assess and learn from it, and can answer our protagonist’s questions about her business’s financial situation.

  In consumer lending, it is easy to imagine this world. We already have mobile banking apps that tell us our FICO (Fair Isaac Corporation) scores, credit availability, and how much we are spending each month. A platform that integrates all these capabilities is likely to emerge in the future, and might even include robo-advice about taking out a mortgage or when to refinance student debt.

  The future for small business will not look quite like the consumer environment because the needs of small business owners are different. A “smart” environment of the future will integrate the disparate sources of information a small business owner currently has to wade through manually. Accounting software, bank balances, credit cards, tax payments, and bank loans all exist today in their own information streams. It is left to the small business owner, or her advisor or accountant, to integrate them and draw out the implications for cash balances and business decisions. The technology exists, or will soon be available, to meld this information onto a single platform. Imagine an intelligent virtual assistant that relies on a range of automated features and predictive formulas, all serving to compile and sort through the vast array of available data and anticipate a small business’s future sales and cash requirements.

  Reaching this state of Small Business Utopia will involve getting three factors right. First, technology will need to make information streams about small businesses more readily available and integrate them in ways that illuminate the small business’s financial health and future needs. Second, credit or other appropriate loan products need to be easily available to the small business borrower. This requires lender
s to refine their expertise in determining who is creditworthy. Third, to be successful, the new environment must be built around the needs of small businesses, rather than a consumer concept that is simply modified for small businesses. In the past, all three conditions were hard to meet. Today, they may be within our reach.

  A Platform to Rule All Others

  In the story of Alex and her coffee shop, she comes to work and logs into one system, her small business dashboard. This dashboard does not exist for small businesses today. Instead, a business owner has one system, perhaps QuickBooks or Xero, for their accounting software, one portal for bank transactions, another like HubSpot for marketing, and a separate payroll system such as ADP or Gusto. In addition, there is a separate healthcare or benefits portal and taxes are often paid offline.

  Ask small businesses about their concerns and they often mention their worries about forgetting to make a quarterly payroll tax payment or coming up short because they neglected to put away the cash that they will owe. They fear that they have not planned well for seasonal cash needs, when they have to pay for a big order of inventory, or when a large customer pays late. In a large business, enterprise resource planning (ERP) systems take care of cash forecasting, based on an integrated platform that draws on sales systems, supply chain systems, and manufacturing and product data.

  A similar system for small businesses would combine at least four key activities: banking and payments, loans and credit, accounting, and tax. The key to the dashboard’s value would be to give more visibility into a business’s future cash flows. One can see the value of knowing more precisely when future lean periods or shortfalls might be coming up, and having the opportunity to set aside a rainy day fund. This transparency into future cash flows could benefit a growing business by giving it the confidence to make a large investment decision, such as expanding or buying new equipment. In this “utopian” world, fewer good businesses might fail, and more businesses would have the confidence and financial resources to grow successfully.

  A cash flow dashboard would not just benefit the small business owner. It would also create valuable information flow for a lender. Today, lenders such as Amazon, American Express, and Square rely on transaction data from their platforms. But for small businesses that do not sell at retail, lenders do not yet have the equivalent real-time data on their prospects. A platform that provides an intelligent combination of revenue, receipts, orders, payments to suppliers, and other expenses would help a lender provide credit at the push of a button. Businesses could proceed more securely, knowing they had greater cash buffers, and lenders would have the benefits that cash flow transparency lends to the underwriting and risk assessment process.

  The basic technology to create a connected dashboard exists today. Why, if it is what small businesses want, has it not been developed? Today, each data stream lives within the purview of a different provider (e.g. TurboTax or Visa), each of which may or may not be inclined to provide access. Some of the data, such as banking information, is not controlled by the business owner. This is why Open Banking initiatives in Europe and the United Kingdom, which gave ownership of banking data to consumers and small businesses, were so momentous. (Open Banking and its implications are discussed further in Chapter 11.)

  There is no question, however, that small business intelligence will develop quickly on the coattails of other areas of big data and artificial intelligence. Combining data sources and using analytic techniques to understand patterns and create predictions is happening already in numerous areas such as marketing and customer acquisition. These same capacities will be the foundational elements for creating an intelligent small business financial platform or dashboard.

  Big Data, Predictive Modeling, and Artificial Intelligence

  During our interviews, we heard a story about a man in Shanghai who used Alipay, a payment application developed by the technology platform Alibaba, to buy his coffee. As he sat down to drink his coffee, he received a notification on his smartphone, providing him with a map of his predicted route (based on his previous travel to the area), and notifying him that he would receive 10 percent discounts at two small businesses along his path. In the United States, iPhones have begun to more subtly provide this kind of information, directing you “home” using the route with the least traffic and predicting locations where you may want to stop along the way. Facebook has a feature that provides users with a list of nearby restaurants they might like whenever they arrive in a new city. We have become accustomed to seeing ads on Google and Amazon based upon our search history.

  One large U.K. bank told us the story of a client, a small seaside hotel looking to understand its customers. The bank had extensive information on a large share of the hotel’s customer base, because many of those customers had used the bank’s credit card to pay for their stay. The bank could provide anonymized data and intelligence about the hotel’s clientele—how far they had traveled or what other food or activities they preferred—which helped the hotel develop better customer-focused services and marketing plans.

  The ultimate small business dashboard of the future will combine intelligence from a business’s past activity (i.e. sales, purchases, etc.) with predictions and marketing advice, producing a business-savvy bot like the one in Alex’s coffee shop. If the projected business trends show a cash need for investment or routine cash fluctuations, the owner may wish to seek a loan or line of credit. This intelligent bot would be able to help the small business owner access credit more seamlessly, comparing available options and recommending drawdowns on credit as needed.

  For the bot we have imagined to work, the market for small business credit needs to be much more transparent and efficient than it is today. The future state requires more standardized and clearly defined loan products, and crisp credit standards so a small business knows quickly and exactly how much credit might be available to them. Small business credit marketplaces such as Fundera and Lendio have made some strides in this direction. In addition, the emergence of standardized, automated credit formulas is pushing small business lending toward a state where a small business might have a well of existing credit that they can draw from at their disposal, like a credit line on a business credit card. Eventually, this credit would be visible on an intelligent small business platform and accessible at the push of a button.

  The Role of Big Data in Establishing Creditworthiness

  The availability of new and large sources of data is not just helpful to the small business owner in managing their business and predicting their credit needs. Big data is also changing the way lenders make credit decisions. The use of data started slowly as fintechs emerged. One of the most important breakthroughs was actually fairly mundane: the idea that OnDeck pioneered of using current activity from a business’s bank account as a timelier indicator of whether a business was creditworthy. A business that was paying its rent and suppliers on time was likely a better loan prospect than one who was behind and missing payments. Other data streams, such as Yelp reviews, looked interesting, but initial algorithms struggled to find good results with these novel indicators. This may be changing, as the possibilities of using data from new sources, such as mobile phones, are nearly endless. (See box)

  Unlocking Credit with an Android App

  Tala Mobile, a company specializing in making micro-loans to individuals and small businesses in the Philippines, Kenya, Tanzania, and Mexico, pulls data from the mobile devices of its users, converts it into a scalable format, and uses it to analyze a business owner’s behavior and likelihood to repay. With the customer’s permission, Tala gains access to a massive amount of information through its Android application, including merchant transactions, call logs, receipts, and other predictive data. Tala uses the information not only to determine the creditworthiness of the business owner, but also to assist them in developing their business plan and managing cash flow.

  Focusing its work in underserved communities, where potential customers are often un
seen due to their lack of credit history, Tala has been able to succeed using this new data source and innovative approach to assessing credit. Since its founding in 2012, Tala has provided 7 million loans to more than 1.5 million people in five countries across three continents. The company loaned $350 million in just under three years while still maintaining a write-off rate of under 7.5 percent.1

  The story of Tala Mobile is not an isolated example. Many traditional and new lenders, from large banks to platform players like Amazon and Google, have large amounts of data that can be used to generate information about what kind of loan a small business needs and when, as well as what the business can do to increase its sales and otherwise improve its financial situation.

  The potential uses of big data, predictive algorithms, and other kinds of artificial intelligence are both exciting and scary. As with all advances, there is a lot of potential downside in the future world we have imagined for small businesses and their lenders. One significant risk is the possibility of unintended consequences, or even potential misuse of data, as a result of algorithm-driven decision-making.

  Imagine a car insurance company that sifted through its customer data and identified a single factor that consistently correlated with a 30 percent increase in car accidents. Now imagine that the factor was whether the driver of the car bought frozen pizza. This example may seem absurd, since there is no obvious causal link between frozen pizza-buying behavior and auto accidents, but it is based on a true story. The real insurance company in the example decided not to use the data to determine their insurance premiums for two reasons. First, if people found out that buying frozen pizza would hike their premiums, they would stop buying it without changing the other risk factors that actually caused accidents. Second, the company felt that its use of the information, if known, would likely provoke a public backlash.