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Generative artificial intelligence has been a hot topic lately, but how does this emerging technology translate from conceptual and abstract to concrete and tangible when it comes to market surveillance? ?

This question was explored in a recent Nasdaq webinar, “Practical Applications of Generational AI in Surveillance.” Tony Sio, head of regulatory strategy and innovation at Nasdaq, and Reuben Falk, head of generative AI and machine learning for financial services at Amazon Web Services, discuss what genetic AI is and its practical uses in surveillance. applications, and how AI can improve and drive efficiency. Mission-critical functionality for brokers and market operators.

Ruben Falk, AWS

Falk set the stage by providing a standard definition of Gen AI. This is a model that can be trained on unstructured data or other media and learn from that data in a way that can generate new artifacts. What makes it cost effective is the huge amount of data available. Large-scale language models (LLMs) have the same knowledge as humans who have been reading “everything under the sun” for 20,000 years, Falk said.

“But the limitation is that even though these models have a lot of knowledge and some reasoning power, they are ultimately probabilistic in nature, and even if the answer is similar to the correct answer, “It means it can actually be wrong. This is also known as a hallucination,” Falk said. “This is an area of ​​focus for us with Nasdaq and other customers: how to prevent hallucinations and optimize accuracy.”

It was noted that some traditional machine learning use cases are turning into artificial intelligence use cases.

“These types of algorithms, for example, algorithms that detect potential fraud or non-compliance, are moving from rule-based systems to traditional machine learning systems, but not to true generative AI systems. No,” Faulk said. “The idea of ​​making decisions or making predictions is not really the essence of generative AI. But generative AI is used to generate input to traditional machine learning algorithms.”

Falk and Sio agreed that surveillance is fertile ground for generational AI because it greatly expands the world of potential inputs for detecting market fraud. Applying generative AI to surveillance can improve both effectiveness and efficiency, especially when navigating growing unstructured data sets, evolving behaviors, and complex trading activity across markets and asset classes. can.

However, this technology also offers broader enterprise-level utility.

Tony Sio, Nasdaq
Tony Sio, Nasdaq

Nasdaq frames AI use cases in two different ways. “One is how we can improve our products, including monitoring tools, compliance tools, and trading with AI capabilities,” Sio said. “And we also look at it from a business perspective: How can Nasdaq improve its business itself? How is he using AI within the company?”

Nasdaq is bringing Gen AI internally for coding companions, content creation, discovery and workflow automation, Sio said. There are also initiatives around AI education for employees.

“We’re actually going through a pretty significant upskilling process right now, and we’re asking almost all of our Nasdaq staff to undergo training on AI this year,” Sio said. “We hope that many people choose generative AI as part of their training.”

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