We continue our series of conversations about Artificial Intelligence and how it's changing the world around us. Today’s episode focuses on the use of AI in the financial sector and both it’s potential strengths and weaknesses.

Joining the show to help us start making sense of it all is Jessica Ellett. Jessica is a senior manager and lead modeler at Cambridge Technology, a financial services technology startup. Jessica is also an MSBA graduate from the David Eccles School of Business.

Jessica shares her insights on developing AI-operationalized applications for large banks, highlighting tools like loan processing applications, financial report generation, and predictive trade outcome models. Frances and Jessica cover the concept of 'human in the middle' AI and the importance of human involvement in AI processes. Jessica also touches on concerns around AI bias, data privacy, and the critical need for business-led AI projects. Jessica also looks forward to what she predicts the landscape of her industry could look like in five years.

Eccles Business Buzz is a production of the David Eccles School of Business and is produced by University fm.

• Jessica Ellett | LinkedIn
• CTFSI - Cambridge Technology Financial Services Inc.
• Master of Science in Business Analytics - The David Eccles School of Business
• David Eccles School of Business (@ubusiness) • Instagram
• Eccles Alumni Network (@ecclesalumni) • Instagram
• Eccles Experience Magazine

How the MSBA programme equipped Jessica to be a voice in the AI space

[37:02]: When I first finished in 2018 with the MSBA program, I got multiple job offers afterwards when we moved here to Charlotte, North Carolina. And, often, it was kind of an undefined structure. Hey, what can you help us do? What can you help us know or ask? Help us do this in a better way, or help us ask the right questions. And so I think it's still young enough—I mean, the whole program, data science programs, MSBA programs—within the last 10 years, I would consider that a really young program. And so new talent coming out of those are going to be the thought leaders on how AI gets adopted and how these programs run at different companies. And think about that. So you come out of this program and you think, "Well, I don't know much. I just got this degree, or I don't have a lot of application." But people are willing to listen to you because you've had focused study on this topic more than they have, so for better or for worse, you're going to be a voice in that space.

How AI works best with people by having the human in the middle

[07:24]: Think of AI as the best assistant you've ever had, but you need the right systems or processes in place to interact with that assistant to get the most out of them, right? So, it's not just building or designing the perfect assistant. It's when am I going to use them? How am I going to use the output? How do I give it the right information to get out of it? What I want—all that interaction. That's human in the middle. You have to design that well to get the most out of it.

Why do AI projects need to align with business goals?

[41:18]: I'd say AI projects need to align with the business goals, not just tech advancements. You have a shiny new tool, and you want to use it but don't lose sight of what the business goal is. Using a shiny new tool could be expensive. If you suddenly have to build the whole machine that it fits with, that could get expensive. So, I'd say that transformation has to have a business-led approach because it has to align with the business goals. And if none of the business goals are ready or positioned to need AI—which is hard to believe—but it could be possible, then it doesn't fit in the plan or the goals.