AI in Finance Summit London 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. 

• Learn about the best practices and strategies for successfully implementing AI in financial services companies 
• Understand the common pain points and challenges encountered when implementing AI in finance, such as data quality and security, regulatory compliance, and organizational change management 
• Discover how to overcome resistance to change and shift legacy mindsets around AI in order to fully leverage its potential benefits 
• Explore case studies and real-world examples of successful AI implementation in financial services 
  

 

Adam McMurchie is the Lead Cloud Data Engineer at NatWest. He was previously the leader in DevOps and an AI expert working in the bank's SAO platform at the forefront of technology development in finance. With broad exposure to a range of technologies, Adam drives an ethos of simplification, Cloud agnosticism and specialises in spotting the next trends in fintech.  Additionally, Adam also has a background in science with a physics degree specialising in NeuroComputing and is a polyglot linguist & seasoned translator. Adam has pooled these skills to deliver full-stack novel solutions from tensor flow-driven mobile apps, to personalized banking chatbots. Adam also develops apps designed around the ethos of Social Utility, including Flood/Storm reporting, EV Vehicle bay monitoring and preservation of endangered languages. 

• How to leverage AI to improve efficiency and cut costs for your business 
• Use Cases of AI that can help your company thrive in the current economic climate 
• How to apply AI while also controlling spending 
• How AI can help improve efficiency and minimize mistakes

• 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.   

There is a strong business case for developing a strategic explainability approach – across compliance, competitive advantage and model robustness. This talk will centre on one of the most important areas of responsible AI and data ethics – explainability and transparency, demonstrating that:

• Explainability is critical in obtaining promised benefits of big data use
• The definition of explainability and transparency is still emerging
• Explainability can underpin new conversations with clients

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

• How can AutoML be applied to automate the decision-making process? 
• How can these frameworks be created and deployed? 
• What considerations need to be taken on board when automating such processes? 

• 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 

• Discussion of the advantages of using low-code and no-code platforms, including faster development times and increased efficiency 
• Overview of the challenges that may arise when implementing these technologies 
• How low-code and no-code platforms can improve collaboration and communication within a financial organization 
• Examining the security and compliance considerations that must be taken into account when using low-code and no-code platforms 

• Learn how advanced AI techniques can be used to create personalized consumer profiles for risk assessment and targeted marketing 
• Using AI to create consumer profiles 
• How consumer profiles can improve consumer loyalty 
• Understand the benefits of using machine learning and deep learning methods for personalized risk assessment 
• Discover how to use AI-based techniques for targeted marketing strategies to improve customer engagement and increase revenue 

• 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 financial sector around AI? 
• What challenges have arisen recently in fintech? 

• 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
  
  
  

• Overview of the current landscape of card and payments fraud, including common types of fraud and their impact on businesses and consumers 
• Discussion of the potential of machine learning and other AI-based techniques for detecting and preventing fraud 
• Examination of the challenges and limitations of using machine learning for fraud detection and ways to overcome them 
• Explore Use-Cases of how banks and financial services are combatting card and payment fraud