Figthing the Bad Actors with Machine Learning: Startup Interview with Harsh Pandya, CEO Giant Oak

HackerNoon Reporter: Please tell us briefly about your background.

I’m a data scientist. My focus is causal inference. Thematically, I’m most interested in political violence and organized crime. I had always been interested in the intersection of money, crime, and politics. I received a solid qualitative and quantitative foundation at Georgetown University’s Center for Peace and Security Studies, and NYU’s Department of Politics in their doctoral program, respectively.

Somewhere in between, I spent time consulting at the Defense Advanced Research Projects Agency (DARPA) on the disciplined use of data during active wars. These experiences led me to where I am today, serving as president of a cutting-edge and innovative company that builds machine learning software for intelligence, compliance, and security professionals.

What's your startup called? And in a sentence or two, what does it do?

Our startup is called Giant Oak. At Giant Oak, we use an understanding of economics, behavioral science, and machine learning technology to enhance screening and vetting processes and stop illicit behavior like money laundering, human trafficking, and fraud. Our flagship product, GOST® (Giant Oak Search Technology), is a trusted tool that enables businesses in any industry to screen and continuously monitor large sets of data and companies quickly and efficiently.

What is the origin story?

While working in the financial and government spaces, we saw a need across industries for enhanced entity screening processes that use AI and machine learning to identify bad actors. In particular, we noticed a dearth of tools that make use of publicly available information; this just did not make sense to us.

The largest trove of information out there in the world is sitting there underutilized, and most providers in the space were (and still are) just reselling databases. With GOST, we quickly and empirically proved that prior processes were inefficient and ineffective. Every day, human traffickers, fraudsters, and money launderers were getting away with crimes that GOST could help users identify.

What do you love about your team, and why are you the ones to solve this problem?

Our Giant Oak team is driven by the mission to make the world safer by building trusted, world-changing tools that reduce crime, fraud, and violence. That allows us to step back, scan how the market is doing things, make critical assessments about what is and is not working, and fix glaring problems. We’re not bound by any particular data or technology. We do, however, hold ourselves accountable to five core values that set our culture apart - be kind, be respectful, be smart, be bold, and be scientific.

If you weren’t building your startup, what would you be doing?

I teach data science to security professionals, and I’m eager to build and grow that curriculum. There is a great need today for policymakers to be more numerate to make data-driven decisions and ask the right questions of analytic products. I’d love to be instrumental in training and mentoring a generation of brilliant decision-makers who can engage critically and thoughtfully with data.

At the moment, how do you measure success? What are your core metrics?

Like most organizations, we measure success through revenue, growth, and real-world impact. We do, however, put a considerable amount of emphasis on real-world impact.

What’s most exciting about your traction to date?

Our tool is more than an idea; it’s a proven solution that has a real-life impact. Every day our clients, both financial and government, send us notes about the bad actors they’ve identified. It’s amazing to see that these organizations can use GOST and stop human traffickers, fraudsters, money launderers, terrorists, and other criminals every day. There’s nothing more rewarding than knowing that your tool is actually helping the world become a safer place.

What technologies are you currently most excited about, and most worried about? And why?

I am most excited about aspects of machine learning that enable increased performance while preserving privacy. Right now, those technologies are enabled by federated learning and transfer learning. What I’m most concerned about is the aggregation of databases, which risks undermining privacy. If we don’t embrace new technologies that do preserve privacy as technology advances, that is worrying.

What drew you to get published on HackerNoon? What do you like most about our platform?

We were thrilled to be nominated for the 2021 Startup of the Year by Hacker Noon. It means a lot when platforms support and recognize a community of startups that are striving to make a difference in the world.

What advice would you give to the 21-year-old version of yourself?

Walkthrough and explore the space behind every open door instead of waiting for particular doors to open.

What is something surprising you've learned this year that your contemporaries would benefit from knowing?

The biggest lesson I learned this year, as president of a company and a team of dedicated employees, is that having a nice office isn’t the perk that it used to be, even just a year ago.

Working from anywhere is now the perk that employees prefer; the thing to focus on now is not where your employees sit but rather building cohesion and culture remotely.