This episode examines the potential for misuse and fraud among those applying for the Paycheck Protection Program (PPP)—and how fintech and software provide overlooked tools to stop it.   On March 27th, the government enacted a $2.2 trillion dollar stimulus package called the CARES Act, the largest aid measure in history. The act provides more than $500 billion for the Paycheck Protection Program, or PPP, a low-interest, forgivable loan program designed to help small businesses and self-employed individuals retain workers and stay afloat during the pandemic. Since March, the Small Business Administration has approved billions of dollars in PPP loans. But it is also estimated that U.S. losses from coronavirus-related fraud and identity theft have reached almost $100 million. According to the New York Times, the Small Business Administration’s fraud hotline has received 42,000 reports about coronavirus-related cheating and misuse; by comparison, last year it had less than 800. To date, the Department of Justice has charged more than 40 cases of PPP-related schemes, from claiming non-existent employees or non-existent businesses to identity theft, kickback schemes, fake tax documents, and multi-state fraud rings. Most of those cases have alleged fraud of more than a $1 million. But what about the countless others that may be cheating taxpayers out of smaller—but not insignificant—sums? How does the government decide who should get money and who shouldn’t among millions of applications from businesses of all industries and sizes—and what role do banks play? How does the program then distribute that money quickly and accurately—or not, in many cases? And what tools are at our disposal to catch those who cheat the system?  Host Lauren Murrow is joined by Bharat Ramamurti, the original member of the COVID-19 Congressional Oversight Commission, which is tasked with evaluating the impact of coronavirus relief loans; Naftali Harris, the CEO of SentiLink, a software company that builds technology to detect synthetic fraud; and a16z fintech general partner Alex Rampell.

There is no spoon. Or rather, “There is no such thing as ‘data’, there’s just frozen models”, argues Peter Wang, the co-founder and CEO of Anaconda — who also created the PyData conferences and grew the early data science community there, while on the frontlines of trying to make Python useful for business analytics. He views both models and data as fluid, more like metaphysics than typical data management… Or perhaps it’s that when it comes to data, those with a physics background just better appreciate the mind-bending complexity and challenges of reining in the natural world, and therefore get the unique challenges of AI/ML development, observes a16z general partner Martin Casado — whose first job after college involved computational physics simulation and high-performance computing in Python at Lawrence Livermore National Laboratory. (Wang, meanwhile, graduated in physics.) But this not just a philosophical question — the answer has real implications for the margins, organizational structures, and building of AI/ML businesses. Especially as we’re in a tricky time of transition, where customers don’t even know what they’re asking for, yet are looking for AI/ML help or know it’s the future. So what does this all mean for the software value chain; for open source collaboration and commodification; and for the future of software businesses? After all, it’s not written in stone that “All information systems must be deconstructed into hardware, and software, and data” and that “software must have these margins”… Will there be a new type of company?   image: Pawel Loj / Wikimedia Commons