Recent disruption in financial services has been driven by several factors – technologically savvy and connected customers, tighter government regulations, aggressive new Fintech market entrants and more. However, another potentially huge disruptor has been bubbling below the surface for several years now and is finally coming to ahead – the transformation of the American worker from the traditional ‘9 to 5’ work model to freelance or independent contractor work, known as the “gig economy.”
According to Intuit, the gig economy – everything from consultants, to drivers, to caregivers – currently represents 34 percent of the American workforce and is expected to grow to 42 percent by 2020. Upwork predicts the majority of the U.S. workforce will be freelance by 2027 (2).
Ultimately, lenders must adapt to address a growing base of borrowers seeking funds for everything from personal life needs (mortgages, children’s college educations and more) to investments in their own enterprises.
There are significant challenges ahead, namely assessing and controlling the risk that comes with less traditional, more unpredictable income flows. But as an industry, we cannot afford to sit idle.
Lenders will miss out on an increasingly large and significant borrower segment, while freelancers – even those making good incomes – will be shut out of banking services. How must banking and lending services align?
Careful Analysis of Individual Financial Patterns
Lending to freelancers will require close assessment of individual financial patterns – the timing and frequency of inflows as well as outflows. Instantaneous access to, and analysis of, borrowers’ banks account details will be critical, along with tight integrations with leading consumer credit scoring services.
These links will make it possible to give out loans to qualified applicants in the least amount of time – on par with the speed of leading P2P lending platforms and satisfying borrowers’ expectations for speed and convenience which have been set for many aspects of our daily lives by industry giants like Amazon, Google, Facebook, and others.
Unfortunately, to date, many freelancers applying for credit on such platforms find themselves redirected to much more extensive information intake forms than those in more traditional employment roles. Freelancers applying for credit are also expected to submit much more paperwork, leading to the scenario of these borrowers abandoning the process and resorting to predatory lenders.
This real-time analysis empowers lenders to “de-risk” freelance loan applicants, by satisfying two key points of borrower evaluation – “capacity to pay” (a borrower’s ability to pay back) and “willingness to pay” (a borrower’s inclination to pay, acknowledging that even people who make excellent money aren’t always responsible and creditworthy).
Even if freelance income intervals are less regular than traditional employment, longer-term analysis can provide a broader, longer-term sense of income predictability. When combined with credit scores, this helps produce an informed risk profile while guiding reasonable loan amounts based on the borrower’s financial position and the lender’s risk tolerance.
Personalization and Customization
A recent survey of banking customers found that 86 percent would be more likely to do business with a bank offering personalized experiences – which includes understanding consumers’ unique financial circumstances and tailoring products and services accordingly. Another survey found 90 percent of consumers (4) believe personalization in financial services is very, or at least somewhat appealing.
Freelancers prize flexibility – in their lives (it’s a primary reason they chose to freelance in the first place), and the services they opt in to use. As personalization emerges as a major competitive differentiator in financial services, lenders that can accommodate freelancers’ irregular income schedules will have a natural advantage.
For lenders, this means offering tailored, customized loan payback options based on the analysis of individual financial patterns. Approximately how much income is this individual generating monthly? How frequently are they paid and approximately when throughout the year are these deposits made? How much should each payback payment be, based on size/frequency of deposits and this individual’s other ongoing financial obligations?
Flexibility can also take the form of freelancers being given multiple options for paying back – including not just cash remittances, but hours worked. Even if a borrower finds him- or herself in a cash drought, he/she can compensate by working a certain number of hours in their gig position (like ridesharing). There are tremendous opportunities for collaboration between forward-thinking lending institutions and gig economy leaders.
Artificial Intelligence (AI)
The more data you collect and analyze, the smarter you become. Across the financial services industry, firms are adopting AI to streamline operations, conduct risk modeling and fraud detection and underwrite loans.
However, the power and potential of AI must always be balanced with the need to avoid bias and adhere with compliance regulations. The federal government closely regulates certain types of loans; borrowers have many rights and protections and underwriters suspected of lending discrimination can face serious consequences.
While AI can be very beneficial, helping reduce costs and improve services, it carries some dangers because it can be inherently biased, lumping individuals based on categories and making assumptions. Consider the simple example of a loan applicant identifying himself or herself as a professional painter when applying for credit in January. Summer is traditionally the busiest season for painters, and due to the time of year (winter), this applicant would be classified as a near-term default risk.
What this analysis may fail to take into account is the unique circumstances of this painter. Perhaps he/she also does other types of indoor home improvement work throughout the winter months, to help bridge the gap to summer. Selecting what features to incorporate in the AI model and what features to not include is critical. AI should not also be used alone, but rather, in conjunction with the unique individual’s financial patterns and the overall picture.
For example, once the lender evaluates this painter as an individual and assures his or her ability to pay, the supplemental knowledge that he/she is a painter directs the lender to offer tailored payment terms – like paying back in smaller amounts through the winter months, and increasing the size of these payments in the summer. This represents a positive use of AI that helps the lender maintain compliance while delivering better, more customized customer service.
Conclusion
History provides many examples of traditional banks and lenders taking reactive positions when realizing the clear need to modify their approaches. One recent example is the 2008 sub-prime mortgage crisis – lenders learned a painful lesson and mortgage lending practices tightened up accordingly, including stricter laws around adjustable rate mortgages.
As the gig economy continues to build momentum, lenders’ inability to service and accommodate freelancers could have very negative consequences, including lack of access to capital for millions of Americans who will find themselves in the underbanked category or even worse slip into poverty just because of an emergency that was not accounted for. Forward-thinking lenders that embed gig workers into their practices will position both themselves and freelancers to best succeed within the changing U.S. labor landscape.
Hussein Ahmed is founder & CEO of Oxygen a San Francisco based startup and digital bank offering free banking and fair lines of credit to freelancers. Oxygen’s aim is to give the growing U.S. freelance economy access to modern banking services with no fees, as well as underwrite credit and loans to freelancers, in a manner that takes into account freelancers’ sometimes volatile cash flows. Hussein is a Haas Venture Fellow, University of California, Berkeley, Haas School of Business.