In this Leading with Data episode, Eleni Verteouri, AI Tech Lead and Director at UBS, shares her invaluable insights on the transformative journey of AI in finance. With over a decade of experience in model development and a prestigious Forbes Cyprus 20 Women in Tech Award 2024 recognition, Eleni has been at the forefront of shaping modern financial technologies. In this podcast, we delve into her expertise, exploring the evolution of AI, from traditional mathematical models to innovative solutions. Join us as we uncover the key insights and learn from Eleni’s remarkable career.
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Key Insights from our Conversation with Eleni Verteouri
- The integration of AI in finance has evolved from strict mathematical frameworks to innovative and efficient applications.
- Generative AI models offer new solutions to thin data problems, reducing reliance on historical data.
- Responsible AI requires a multifaceted approach, including education, discussions on ethical standards, and partnerships with startups.
- The future of AI includes generative AI, considerations around artificial general intelligence, and the integration of AI into personal life.
- Addressing biases in AI, particularly with LLMs, is challenging and requires innovative solutions like AI agents and human oversight.
- Empowering women in tech through technology can lead to a more diverse and inclusive industry.
Let’s look into the details of our conversation with Eleni Verteouri!
How did your journey into AI and technology begin?
I’ve always leaned towards technical subjects, but it wasn’t clear what direction to take. During my first engineering degree, I explored GPUs and distributed computing. At that time, cloud computing wasn’t as prevalent, and finance seemed to demand high computational power for complex calculations. This curiosity led me to financial mathematics, which eventually took me to Switzerland and into the banking sector. It was a pivotal moment in my journey into technology and AI.
What inspired you to pursue a Master’s in Quantitative Finance?
After completing my engineering degree with a focus on computers, I wanted to delve deeper into modeling. I pursued quantitative finance, a blend of mathematics and finance, at ETH Zurich and the University of Zurich. This education, combined with my master thesis at an investment bank, paved the way for my internship and subsequent roles in the industry.
How has AI evolved in the banking industry over the past decade?
AI has transformed the banking industry significantly. Initially, the focus was on traditional financial theories and mathematical constraints. Over time, AI has been integrated into personalized services, prototype building by end-users, and risk estimation. It’s been a journey from operating within a strict framework to adopting AI for efficiency and innovation.
How do you approach thin data problems, like those posed by the COVID-19 crisis?
Thin data problems were traditionally tackled by individuals using statistical models and rule-based approaches. Today, we have a more robust data infrastructure and governance, and generative models that don’t necessarily require historical data. They can generate synthetic data and assist with metadata, offering new angles to address these challenges.
Can you describe your current role as an AI Tech Lead and the impact of generative AI?
My role involves working on an internal generative AI platform designed to support various use cases, integrate with other tools, and foster internal collaboration. My focus is on safety and security, ensuring responsible AI and user-friendly data science practices. I also contribute to setting development guidelines and fostering academic partnerships.
What are the key focus areas for responsible AI in your work?
Education is crucial, both for users and developers, as roles are blending with engineers working with AI and data scientists driving adoption. Discussions about what “responsible” means are vital, as are partnerships with startups filling gaps in safe and responsible AI adoption.
What trends do you foresee in AI and responsible AI in the coming years?
We’ll see a rethinking of business and product perspectives, governance, and how we work. Generative AI, considerations around artificial general intelligence, and the integration of AI into personal life are key trends. Safety, security, and sustainability will remain paramount.
What challenges do you anticipate as AI becomes more prevalent in society?
Ensuring that AI innovation benefits a fair portion of the population is a challenge. It’s about making sure that new knowledge and skills are accessible and attainable for most people, which may require more coordination and communication.
How do you address biases in AI, especially with large language models (LLMs)?
With traditional machine learning, it was easier to identify and control biases. LLMs present a challenge in explainability. Agents could be part of the solution, helping simulate scenarios and providing a bandwidth of safe and acceptable operation. Human oversight remains an essential part of the process.
What are your personal dream projects or problems you’d like to see solved with AI?
I’m excited to see problems traditionally solved by AI being addressed with LLMs, such as document parsing and insight generation. I’m also interested in AI’s potential to improve mental health, provide empathy, and help neurodivergent individuals integrate better into society.
How do you balance the use of AI in your personal and professional workflows?
As a model developer and AI product manager, I find the boundaries between manual work and AI assistance are blurring. I use AI for coding, troubleshooting, proofreading, and brainstorming. It has made me more independent and efficient in building solutions.
What are your thoughts on the role of women in tech and how to encourage more diversity?
Empowerment through technology is an equalizer. It’s important to judge individuals based on capabilities rather than appearances. We should ensure inclusivity, provide equal opportunities, and encourage women to be outspoken, confident, and assertive in their careers.
Summing Up
Eleni Verteouri’s journey and expertise offer a unique perspective on the future of AI in finance. Her contributions, marked by her Forbes award and academic achievements, provide a roadmap for the industry. As we navigate the complexities of AI, Eleni’s emphasis on responsible practices, education, and ethical considerations serves as a guiding light. Stay tuned to Leading with Data for more inspiring sessions and insights into the world of AI and data science.
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