Track 2 schedule

We will briefly explore creating effective AI/ML teams – a brief look back at the past and current trends in AI/ML teams, what drives change, getting to a lovable product, focus on outcomes, teams’ function & structure, outliving the team “hype cycle”, evolving and promoting team flow, talking to some of the pitfalls for AI/ML teams.

- Understanding the unique challenges of building and managing AI/ML teams
- Recruiting and retaining top AI/ML talent: where to find them and how to make them stay
- Fostering cross-functional collaboration between data scientists, engineers, and business stakeholders
- Developing a clear AI/ML strategy aligned with business goals and objectives
- Establishing effective project management methodologies and processes for AI/ML projects

In today's fast-paced and competitive financial services landscape, machine learning models play a pivotal role in decision-making, risk management, and customer experience. As organizations increasingly rely on models to drive critical business functions, ensuring the robustness and stability of these models in production is even more important.

We will explore the key challenges faced by enterprises in deploying and maintaining models, including monitoring data drift, model decay, and regulatory compliance. In addition, we will examine various monitoring techniques and custom metrics that can provide valuable insights into model behavior and inform decision-making processes. The session will illustrate how these insights can be applied to real-world examples such as enhancing customer segmentation, improving fraud detection, and optimizing trading strategies.

Finally, we will address the role of collaboration and communication between data scientists, risk managers, and business stakeholders in fostering a culture of continuous improvement and ensuring the successful deployment and management of models in production.

- What does open banking mean for data and analytics? 
- How can AI improve the customer experience within open banking? 
- What can we expect to see from FinTech in the near future and what impacts will this have? 

Natesh is a Lead Data Scientist at Finicity where he creates Machine Learning products leveraging open banking data. Prior to this, he was a core member of the Machine Learning CoE at JPMChase and specialized in lending, fraud and marketing models.

- Rapidly validate the business value of ML models without in-house technical expertise
- Automate compliance and data security in the cloud 
- Optimise the process of taking models from prototype to production
- Ensure a good ROI from prototyping and deploying ML
- Plan and approach MLOps and recruitment

Paul Hetherington is the CEO of Mystic. He is an engineer, software developer and entrepreneur, with a passion for making the transformative power of AI a reality for enterprises of all sizes. Paul is the architect of the Mystic platform (Pipeline.ai), an end-to-end toolset and infrastructure that empowers data scientists to fast-track their machine learning deployments. This automation of ML operations is key to enabling financial institutions to harness the latest advancements in deep learning and AI. 

- How Generative AI can help financial institutions automate routine tasks, create personalized customer experiences, and improve risk management.
- What are the ethical and regulatory concerns around Generative AI in financial services?
- Exploring the Future possibilities of generative AI in financial modelling, including improving fraud detection, creating new financial products, and predicting market trends.
- What are the potential risks of generative AI and what steps can be taken to mitigate these risks?

Harry Mendell is a computer scientist/inventor. He invented the first digital sampling synthesizer and collaborated with Stevie Wonder among others . His university thesis was on computer vision and then joined the team at Bell Labs designing the first Unix and microprocessor-based workstation, developing the first memory management co-processor. He then joined the financial sector, creating algorithms for trading options and managing risk. Following that developed algorithms for trading with alternative data including social media that using machine learning and natural language processing. In 2017 Harry joined the Innovation Group at the New York Federal Reserve applying natural language processing and machine learning to bank supervision. Currently Harry is investigating the use of Large Language Models to advance the effectiveness of natural language processing and to meet the needs of the Federal Reserve.

Nan Li is the VP of AI/ML and Statistical Practice at Nationwide in Columbus, Ohio. Nationwide is one of the largest and strongest diversified insurance and financial services organizations in the United States. Nan is a passionate, versatile, and human-centric data and analytics executive with over 20 years of experience in the insurance, financial services, and healthcare industries. A creative and pragmatic business problem solver, innovator, and communicator, Nan is skilled at setting up data & analytics strategy and roadmap, bringing business, analytics, and IT together to achieve business outcomes, and operationalizing data & analytics solutions to deliver scalability and ROI.

Olga is a passionate AI/ML leader. She has been recognized as top 25 women in AI in Canada and top 100 women globally advancing AI in 2023 by Re:Work. She has spent the last 15 years in various senior roles in technology, specifically in data science, big data, data engineering, analytics, and data warehousing. She is a Director of Advanced Analytics and Data Science at the Royal Bank of Canada. Olga brings data to life through machine learning, analytics, and visualization. Outside of her work at RBC, she has worked directly with global organizations such as the UN Environment World Conservation Monitoring Centre, World Resources Institute, and prominent Canadian non-profits such as War Child Canada and Rainbow Railroad on various data science and analytics challenges.

George Samakovitis is Professor of FinTech and Deputy Head of School of Computing & Mathematical Sciences at the University of Greenwich, UK. George specialises in banking and payment systems technologies, Enterprise Architectures and AI for FinTech. His present research focuses on the deployment and governance of technologies for Anti-Money Laundering and Financial Crime, with particular emphasis on the use of DLT agents and development of blockchain infrastructure to deliver Collective Intelligence capabilities in FinTech networks. 

George is presently a member of the Counter Fraud & Data Analytics Advisory Group of the HMG Cabinet Office and has served as a member of the FinCrime Working Group at the UK Payments Strategy Forum (2015-18), particularly working on KYC and Transaction Data Sharing and Analytics strategies and solutions for UK Financial Services. Most recently, he joined the BSI UK Data Standards Expert Panel, a diverse cross-sector panel of senior data executives, aiming to coordinate data standards interoperability across UK industry sectors. 

George’s past work focused, among other, on banking technology investment decisions in economic booms and downturns, addressing, among other issues, the banking sector’s attitudes to uncertainty and risk under the disparate decision-making paradigms dictated by economic climate.

-    How can you evolve your conversational agents for customer service?
- Improving the customer experience with Chatbots
- How Self-service allows for quicker resolutions

As Vice President and Group Product Manager over Conversational AI at KeyBank, Robbi is responsible for leading a cross-functional team of product managers, engineers, designers, data scientists and analyst to develop Conversational AI capabilities across the organization.   Robbi and her team are transforming the client experience through the introduction of an omni-channel platform across voice and chat.  She has over 20 years’ experience leading technology initiatives in the financial sector with a proven track record of driving customer value and business growth.   Robbi is passionate about exceeding client expectations by augmenting human interaction with the right technology. 

- Are you making the most of your NLP programmes?
- Different Use Cases of NLP in Financial Services
- Learn how to harness the power of NLP to extract valuable insights from unstructured data
- Discover new ways to improve the accuracy of predictive models using NLP techniques such as sentiment analysis and topic modelling

-    What are the Conversational AI systems that are worth your time?
-     Would Conversational AI improve your customer’s experience?
-    Increasing engagement and loyalty with Conversational AI

- How to adopt MLOps in your company and the ML lifecycle
- Understand the technical aspects of MLOps (Machine Learning Operations) and its importance in the financial services industry
- Learn about the latest MLOps practices and tools such as automated model selection, versioning, deployment, and monitoring
- Discover how to use MLOps to improve the development and deployment of machine learning models

-    How can Quantum Finance help with portfolios?
-    How Quantum Finance can speed up risk scenarios
-    Improving the identification and management of risk and compliance with Quantum Finance

- Introduction to Identity Verification and its importance in financial transactions
- Current methods of identity verification and their limitations
- How AI can improve the accuracy and efficiency of identity verification
- Biometric authentication methods using AI (facial recognition, fingerprint, voice recognition, etc.)

- How advancements in the AI sector will change Self-Service 
- NLP and speech recognition: the future of Self-Service 
- What to expect in the coming years?