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Podcast: How AI is generating and digitizing documentation across financial services, with Arteria AI CEO Shelby Austin
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Podcast: How AI is generating and digitizing documentation across financial services, with Arteria AI CEO Shelby Austin

Transcript

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Hi Fintech Futurists —

Welcome back to our podcast series! For those that want to subscribe in your app of choice, you can now find us at Apple, Spotify, or on RSS.

In this conversation, we chat with Shelby Austin - CEO and co-founder of Arteria AI. Prior to this, Shelby was a Global AI Council member with the World Economic Forum, and before that, a partner at Davies Ward Phillips & Vineberg LLP. Shelby has also been Managing Partner of Growth & Investments and Omnia AI at Deloitte Canada.

A well-respected business leader, Shelby has been recognized for her work in driving significant growth through innovation and technology. Arteria AI was named one of Canada’s Hot 50 companies by Profit Magazine and Shelby, one of Canada’s Most Powerful Women: Top 100 from WXN.

Shelby is a passionate advocate for the adoption of artificial intelligence in government and is committed to helping businesses harness the power of data-driven decision-making.

Shelby has a BA in Government from American University, a High School diploma from Branksome Hall, and an LLB in Law from the University of Western Ontario.

Artificial Intelligence at Goldman Sachs - Emerj Artificial Intelligence  Research - Banking

Topics: AI, Artificial Intelligence, ChatGPT, documentation, finance, analytics, contract processing

Tags: Arteria AI, Arteria, Deloitte, OpenAI


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Timestamp

  • 1’23: From Law to Innovation: Shelby Austin's Journey Through Legal Grind to Tech Entrepreneurship

  • 6’41: Digitizing Diligence: Pioneering Data Management in Legal Workflows

  • 9’10: From Legal Documents to AI Frontiers: Navigating the Transition into Technological Leadership at Deloitte

  • 17’27: Reimagining Documentation: The Genesis of Arteria and the Evolution of Financial Services Solutions

  • 22’32: Simplifying Complexity: Arteria's Solutions for Streamlining Documentation and Compliance Processes

  • 26’44: Transforming Documentation: Arteria's Multimodal Approach to Financial Services Paperwork

  • 32’03: Crafting AI Solutions in Finance: Navigating the Landscape of Models Beyond the Hype

  • 36’16: Building Bridges in Finance: Exploring the Complex Landscape of AI Integration with Major Financial Firms

  • 39’25: The channels used to connect with Shelby & learn more about Arteria AI


Sneak Peek:

Shelby Austin:
… think of it as almost like a tiramisu of models, Lex. So, it's like stacks, and layers, and layers, and layers, and layers, and layers of models. Some are certainly open source, some are trained at Arteria, there's a whole stack of them.

So, for example, Arteria is very focused on how to use the most effective multimodal techniques to ensure that we are not just looking at things one way. The best example of this is... And it's a trite example because they're not hard to process, and so you shouldn't think of invoices being a core functionality of Arteria. It's an example to demonstrate what multimodal means. You know when you look at an invoice that it is an invoice because you just know the format. And so, your eyes are doing that work, in addition to reading the word invoice at the top and maybe the number at the bottom-right, which is the total. By combining the reading techniques of natural language with the vision techniques of being able to sort of look at a document and understand the context of that document, those are the types of models we're primarily focused on.

And to your point about financial services, data specific almost always trumps the general no matter how large the model in AI. And so, really, we tend to stay focused on things where we will have a competitive advantage because we'll take what's open source, and then we'll fine tune it for the purposes that our customers have. And so, our major advantage is that we are very specific in terms of what we focus on, documentation of banks. And so have tried to solve a whole series of challenges that are unique to financial services, like the documents tend to be long in a lot of cases, and as a result, certain models perform very poorly on them. And so, we try and really get specific in terms of the techniques we're using.

That said, most importantly, which I think we haven't quite gotten to, is Arteria not just a series of models. We also have…

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