AI in Finance Summit schedule

• Overview of how AI/ML can help optimize processes and drive new revenue for financial services organizations 
• Discussion of strategies for working closely with the business to reveal transformative AI solutions 
• Examination of strategies for retaining and attracting diverse talent in the field of AI/ML 
• Examples of AI/ML use cases in financial services, such as risk management, fraud detection, and customer service 
• Discussion of the ethical considerations and best practices for implementing AI/ML in the financial services industry, such as transparency and data governance. 

• Deploying machine learning systems in the finance sector presents a significant challenge due to the risk of inadequate performance once operational, which could lead to substantial losses or missed profits. This issue currently hampers the widespread use of potentially valuable ML-based algorithms and systems, many of which, as presently realised, are likely to suffer from suboptimal performance and inherent vulnerabilities.

   

  The root of this problem lies in the absence of effective methods and tools for assuring the robustness of machine learning models such as decision trees and neural networks. While traditional software validation processes are well-established, ensuring the reliability of AI systems has so far remained problematic.

   

  In this presentation, we will explore newly developed techniques in the realm of formal robustness verification for neural networks and decision trees. These innovative methods not only automatically detect vulnerabilities but also enhance the robustness of these systems, offering much-needed assurance in fields as critical as aviation.

   

  Despite the unique complexities and challenges of the financial sector, adopting these robustness verification methods could be crucial. This approach would facilitate the rigorous validation of ML-based systems prior to their deployment, enhancing their reliability and performance in real-world financial environments, as well as enabling the rollout of innovative and effective solutions.

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In this talk, I will discuss AI research conducted at JPMorgan Chase, focusing on the application of Large Language Models (LLMs) within the financial sector. We will explore a specific case study on the utilization of LLMs as annotators for financial data, discussing both the challenges encountered and the benefits realized. Through this examination, attendees will gain a comprehensive understanding of the potential impacts and practical implementations of AI technologies in finance.

• Understand the European Commission's AI strategy and initiatives, as well as the regulatory framework for AI 
• Compare and contrast the EU's AI regulations with the UK's regulatory AI framework and its potential implications on companies operating in both jurisdictions 
• Discover how the European Commission's approach to AI in finance compares to other jurisdictions and the potential implications of these differences for the industry 
• The EU AI act and its consequences on the AI sector 
• What is the difference between the EU AI Act and the UK Position Papers? 

 

As senior counsel in Mastercard's Privacy & Data Protection team, Jasmien supports their Cyber & Intelligence Solutions business globally with a focus on AI and machine learning.   

• Navigating stakeholders and business requirements
• Best practice for LLMs & GenAI
• Making use of the full ML/AI toolbox

What’s the purpose of an “AI Maturity Assessment”, and how does it work? 

• Objectives of an AI Maturity Assessment
• How does it work?
• AI Action Plan and Focus for the year

• What should be the focus of your AI strategy? 
• Redefining your AI Strategy in the current climate 
• Learn from industry experts what to do and not to do during your AI Journey 

• What should be the focus of your AI strategy? 
• Redefining your AI Strategy in the current climate 
• Learn from industry experts what to do and not to do during your AI Journey 

• What should be the focus of your AI strategy? 
• Redefining your AI Strategy in the current climate 
• Learn from industry experts what to do and not to do during your AI Journey 

1)Understanding AI Maturity

2) Challenges in AI implementation

3) AI Journey- Key considerations

- Thinking through the human context. 

- Terminology, methodology & looking beyond the obvious.

- Building human connection and confidence in the world of AI.

• What are the new technologies to be aware of? 
• Tools & techniques to help plan for a stable future & gain a competitive advantage 
• What changes are happening in the insurance around AI? 
• What challenges have arisen recently in insurance that AI can help solve? 

• Learn about the latest developments in generative AI and its applications in the banking sector 
• Understand how generative AI can be used to improve financial forecasting, risk assessment and decision-making in banking 
• Discover how generative AI can be used to create new financial products and services such as synthetic data, virtual assistants and personalization 
• Explore the use of generative AI in various banking applications, such as fraud detection, customer service and compliance management 
• What is NLG and its uses in the financial sector 

-AI is taking the world by storm and the pace of development is eye watering
- The potential of the technology is clear to see, but in order for AI to really fulfill its promise it has to solve real problems and be user centered
- We’ve seen how past technologies e.g. blockchain, VR etc. have struggled to cement their place in everyday life and business
- Through showing real case studies, the audience will take away how to think about applying AI in a way that serves both its intended users as well as the business
- How to maximise the potential of what AI has to offer by deeply understanding the problems your trying to solve with tried and tested design thinking techniques
- What the most exciting version of the future could look like in the financial services industry

 

• Shift Towards B2B Embedded Finance: While Buy Now Pay Later (BNPL) initially sparked discussions around Embedded Finance, the focus is shifting towards Business-to-Business (B2B) applications. Specifically, Embedded Finance is poised to address the significant gap in SME trade financing. This highlights a strategic pivot from consumer-centric to business-centric applications within the Embedded Finance landscape.
  
  Opportunities and Risks: Embedded Finance presents substantial opportunities for innovation and market disruption, particularly in addressing financial gaps for SMEs. However, it also brings significant risks. As such, stakeholders must carefully navigate these risks while capitalizing on the opportunities presented by Embedded Finance initiatives.
  
  Continued Evolution: The Embedded Finance narrative is far from static;it is continually evolving. As we move into 2024 and beyond, we can expect further developments and expansions within the Embedded Finance ecosystem. This underscores the importance of staying abreast of emerging trends and adapting strategies accordingly to leverage the full potential of Embedded Finance