Argyle is an infrastructure-as-a-service company that makes workforce data (everything from UBER to Fiverr) accessible through a single API. Argyle provides the infrastructure that enables businesses to effectively access and use workforce data. The gig economy is made up of at least 60 million workers, and according to economists, the majority of workers in the United States will be contract workers by 2027.
Whether to be their own boss and set their own schedule; to earn more income to support themselves and their family; to have more flexibility to balance life, family and other commitments; or to maintain a steady cumulative income because other sources are unpredictable or unstable, there are many reasons why people are increasingly attracted to a non-traditional, “gig-based” career comprised of various projects and assignments, rather than a 9-5 office job.
We recently had the opportunity to interview the founders of Argyle: Shmulik Fishman, Chris Hansen, AudriusZujus for actionable insights on how business owners are now able to leverage the workforce data of the gig economy to transform their own business.
Can you explain how lending companies are using Argyle?
Lenders use Argyle to provide fast and secure access to capital while more efficiently accounting for borrower risk. There is an emerging opportunity to issue loans to non-traditional borrowers with irregular income patterns which is traditionally very difficult to do. Our client API allows lenders to access data that was previously difficult to obtain in order to better understand an individual’s work and income patterns concisely.
Can you explain how banking plays into the Argyle platform?
As banks (both digital and traditional) work to cater toward non-traditional workers as well as credit-thin individuals, there is an opportunity to more efficiently service and evaluate these customers by using workforce data as a proxy for risk. Our bank customers now have a more accurate method to verify employment and verify income at a level of transparency even deeper than end-user account balances.
Can you explain how insurance works with the Argyle platform?
Insuring the new workforce can be tricky. Individuals are working as contractors, as entities, and taking on more transactional work assignments making risk-exposure and profiling difficult. Argyle’s Client API allows the insured to provide their real-time risk exposure while offering a more complete picture of their identity.
Can you explain how benefit companies interact with the Argyle platform?
There are a host of companies looking to bring benefits to non-traditional workers. The future of work which will include multiple jobs at once, side-gigs, and transactions careers is poorly served by existing models. By leveraging Argyle’s Client API, benefit companies are able to gain a better understanding of their end-users resulting in more personalized offerings and advice.
How does Argyle help companies manage worker taxes and tax regulatory filing obligations?
Argyle helps work services companies by provided source-based work data for better work optimization and opportunity. With work rapidly changing and becoming more fragmented an increasing amount of products and services rely on self-reported data to help optimize an individual’s work life from recruitment to earnings maximization. Argyle’s Client API provides direct data access to more efficiently evaluate opportunities for workers.
What has been Argyle’s biggest success/ breakthrough in the past 12 months?
Our biggest breakthrough is the speed at which we are moving. We have gone through the testing of ideas, building a beta version implementing 10 largest US gig-economy companies and launching a production level system in the last 10 months alone.
What game-changing discovery/ project/ technology are you most excited about?
Machine learning is empowered by data and as a data infrastructure company, we are very excited about the current innovations in the Machine Learning space. We think that by building our product, we will enable countless innovations driven by new machine learning techniques in the fields of background checks, scheduling and finance.
