In this episode of Kinectify Conversation, Ari Tuchman, CEO of Quantifind, joins Joseph Martin and Sean Topchi as they explore the world of AI and risk management.

Tuchman's journey, from atomic physics to utilizing AI for sanctions monitoring, offers a fresh perspective for gaming operators, focusing on refining watchlist, sanctions, PEP, and adverse media screening. The conversation covers the innovative methodology of "name science," natural language screening, and the reduction in false positives during sanction screening with the help of AI and word embeddings.

ABOUT KINECTIFY

Kinectify is an AML risk management technology company serving gaming operators both in the US and Canada. Our modern AML platform seamlessly integrates all of the organization's data into a single view and workflow empowering gaming companies to efficiently manage risk across their enterprise. In addition, Kinectify's advisory services enhance gaming operators' capacity with industry experts who can design and test programs, meet compliance deadlines, and even provide outsource services for the day-to-day administration of compliance programs.

To learn more about Kinectify and book a demo, click here.

Transcript

[00:00:00] Sean Topchi: Welcome to Kinectify Conversations. My name is Sean Topchi, and I'm the Director of Business Development at Kinectify. Our mission is to share the experience, expertise, and insights from gaming industry leaders that comes from years of navigating the complexities and impact of compliance. On this episode, I'm joined by special guest:

[00:00:20] Joseph Martin: My name is Joseph Martin, I'm the CEO of Kinectify.

[00:00:23] Ari Tuchman: My name is Ari Tuchman, I'm the CEO of Quantifind.

[00:00:26] Sean Topchi: Thanks for joining us today and remember to like and subscribe to the podcast. You can also learn more anytime at Kinectify.com. That's K I N E C T I F Y dot com.

Ari, thank you so much for joining us on Kinectify Conversations. Really excited to have you today. Would love for you to give us a bit of a background on yourself and kind of how you've arrived at where you.

[00:01:08] Ari Tuchman: Yeah, sure, Sean. And first off, thanks so much for the opportunity to speak with you guys today. Excited about the conversation and excited about the partnership as we do explore ways of being disruptive within the gaming industry. My background is a little bit random. I'm coming actually out of academia in atomic physics.

I was teaching at Stanford, working on quantum measurement. I ended up leaving academia to start a company called Entanglement Technologies, working on using Quantum sensors for landmine detection. And if you think about trying to find a buried explosive, and again, not usually how the conversation starts in an anti money laundering, KYC kind of application, but if you're thinking about trying to find a buried explosive, if you're lucky or unlucky, as you may have it, there's about one molecule that will float up and evaporate into the air out of about a trillion molecules of regular air.

So it's a proverbial needle in the haystack problem, and you're trying to find that one nitro aromatic explosive molecule among a trillion normal molecules. So what we found as we were building the company is that while we started off as kind of laser magnetic field, quantum machine guys, we very rapidly were becoming machine learning people.

And understanding how to do that signal processing and really find that needle in the haystack was what, you know, a little over 10 years ago was what I fully shifted into. Just about then we ended up spinning the artificial intelligence component of our quantum sensors into a standalone company.

And that was the birth of Quantified. And we decided to focus on doing these rigorous, predictive kind of needle in a haystack finding models specifically on unstructured data. So looking at massive forums and news feeds, billions of articles and really even before the gen AI hype of the last year, you know, has sort of swept the nation. We were using very precise language models to be able to find risk, and that was really the journey. So it has been a bit of a random walk, but incredibly fulfilling.

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