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 create synthetic data and integrate it into your ML models
• An overview of synthetic data and its benefits for machine learning
• Best practices for creating high-quality synthetic data that accurately represents the real-world data distribution
•  What are the ethical considerations of using synthetic data?
•  Avoiding privacy issues with synthetic data

• 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.   

• Understand the current regulatory landscape for AI in finance and how it is shaping the industry 
• Learn about the regulatory challenges and opportunities presented by AI, such as data quality and security, bias, and interpretability 
• Discover the steps that are being taken by financial regulators to mitigate potential risks and ensure the safety and soundness of the financial system 
• Engage in discussions with financial regulators on specific regulatory issues and concerns related to AI in finance 
• Explore how regulations are evolving to adapt to the fast-paced development of AI in finance 
• Understand the importance of collaboration between regulators and the industry to ensure that AI is used responsibly and ethically 
• Gain insights into the future of regulation for AI in finance and its potential impact on the industry. 

Frans is a Policy Officer working at DNB, his areas of focus include Artificial intelligence, Blockchain, cryptocurrencies, DeFi and CBDC, Cybersecurity, Cloud computing and data storage, FinTech and RegTech and Quantum computing. He is also a PhD Candidate.

• Understand the current regulatory landscape for AI in finance and how it is shaping the industry 
• Learn about the regulatory challenges and opportunities presented by AI, such as data quality and security, bias, and interpretability 
• Discover the steps that are being taken by financial regulators to mitigate potential risks and ensure the safety and soundness of the financial system 
• Engage in discussions with financial regulators on specific regulatory issues and concerns related to AI in finance 
• Explore how regulations are evolving to adapt to the fast-paced development of AI in finance 
• Understand the importance of collaboration between regulators and the industry to ensure that AI is used responsibly and ethically 
• Gain insights into the future of regulation for AI in finance and its potential impact on the industry. 

Dr Henrike Mueller is a Technical Specialist in the Innovation Department at the Financial Conduct Authority (FCA). Henrike has been leading the FCA’s external policy approach to machine learning / artificial intelligence (AI) since 2017.

• Understand the current regulatory landscape for AI in finance and how it is shaping the industry 
• Learn about the regulatory challenges and opportunities presented by AI, such as data quality and security, bias, and interpretability 
• Discover the steps that are being taken by financial regulators to mitigate potential risks and ensure the safety and soundness of the financial system 
• Engage in discussions with financial regulators on specific regulatory issues and concerns related to AI in finance 
• Explore how regulations are evolving to adapt to the fast-paced development of AI in finance 
• Understand the importance of collaboration between regulators and the industry to ensure that AI is used responsibly and ethically 
• Gain insights into the future of regulation for AI in finance and its potential impact on the industry. 

Working in the Bank of England's Fintech Hub, Mohammed acts as the Bank's Artificial Intelligence specialist responsible for its AI strategy and its cornerstone project, the AI Public-Private Forum. Prior to joining the Fintech Hub, Mohammed worked at the Bank's Prudential Regulation Authority in several areas across banking regulation and supervision.

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? Dr. Ulrike Dowie will answer these questions and share Lessons Learned from the assessment, as well as its greatest benefits, such as an AI Action Plan for her company.

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

• Understand the benefits of using synthetic data in reducing bias and increasing model robustness 
• Learn how to evaluate the quality and diversity of synthetic data to ensure it is representative of the real-world distribution 
• Explore the use of synthetic data in various financial applications, and why it can help with bias 
• Discover how to integrate synthetic data with other techniques such as transfer learning and domain adaptation to improve model performance 

• Overview of current challenges and limitations in interpreting and understanding machine learning models 
• Discussion of various techniques and methods for improving model explainability 
• Examination of real-world case studies and demonstrations of explainability in action 

• 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? 

• Improving BFSI processes with AI & automation 
• Transforming KYC with Computer Vision 
• Learn about the various applications of computer vision technology in the finance industry 
• How computer vision can be used to analyse images, videos and other visual data  
• Learn about the specific techniques used in computer vision such as object detection, image recognition, and facial recognition and how they are used in the finance industry. 
• What are the challenges and limitations of computer vision technology, and how can these improve the accuracy? 

• 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 

• Are we heading to a more automated future? 
• Overview of the latest advancements and trends in AI and data and their applications in the financial services industry. 
• Discussion of the benefits of using AI and data in financial services, such as increased efficiency, improved decision-making, and cost savings. 
• Examination of the challenges and limitations of using AI and data in financial services, such as data privacy and regulatory compliance 

• How AI can be used to automate various aspects of the payment process, such as verification, validation, and reconciliation. 
• What are the benefits of using AI in the payment process?  
• Learn about the specific techniques and technologies used in AI-enabled payment systems, such as machine learning, natural language processing, and robotics process automation. 
• How AI can be integrated with other technologies, such as blockchain, to further enhance the payment process's security, speed and scalability. 

• Introduction to Identity Verification and its importance in financial transactions 
• Current methods of identity verification and their limitations 
• Introduction to Artificial Intelligence and its potential in identity verification 
• How AI can improve the accuracy and efficiency of identity verification 
• Use cases of AI in identity verification in the financial industry 
• Biometric authentication methods using AI (facial recognition, fingerprint, voice recognition, etc.) 
• The impact of AI on fraud detection and prevention in financial transactions 

This presentation explores how organizations of all sizes can leverage AI to meet AML Compliance. While traditional rule-based systems are easily explainable, quick, and easy to implement, they result in low true positive rates, operational overhead, and failure to adapt to changing environments. The adoption of an AI-based approach allows companies and government institutions to achieve six-fold improvement in suspicious transaction activity detection while achieving scalable operational efficiency. This presentation also explores how AML operations will be transformed into analytics-driven MLOps that will continuously provide insights and improvements to the AI system, uncovering even the most hidden patterns. With recent advances and more widespread access to machine learning tools, AI-based AML systems will become a dominant force in the future of Financial Compliance. 

In this presentation you will:
•    Learn how to translate money laundering topologies into machine learning data.
•    Discover how to transition from a rule-based to an AI-based AML transaction monitoring system.
•    Explore the pros and cons of unsupervised learning to spot suspicious activity.
•    Establish a continuous AI improvement loop with the help of AML specialists.
•    Gain insights on how to implement an AI system while meeting AML regulatory requirements.

As a Principal Product Manager in SumUp, Yev is driving the vision and strategy for the Machine Learning Platform in SumUp for financial use cases in close collaboration with data science, engineering, and product teams to deliver ML-powered products that customers love.

• 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