Delivered in partnership between Pinsent Masons, the Edinburgh Futures Institute and the University of Edinburgh Business School, this 1.5-day course for Senior Managers in Financial Services focuses on developing competence and providing training in understanding and addressing AI legal and regulatory risks.

Photo of the Edinburgh Finance Institute exterior building

AI presents immense opportunity and risk for Financial Services. Regulated financial services providers need to ensure that they have appropriate accountability and governance structures in place to meet the requirements of the UK regulators, including those of the Senior Managers Regime. Senior Managers must take steps to ensure that they address the impact of AI when considering whether the business is controlled effectively, relevant legal and regulatory requirements are complied with, responsibilities are appropriately delegated and required regulatory disclosures are understood and made.

This course is tailored to the needs of Senior Managers in Financial Services. Course participants will learn from experienced instructors and their fellow participants who face similar challenges in addressing AI risk in the regulated context.

Before attending, participants will receive a study guide outlining the key course materials. Lunch will be provided on both days and a participant’s dinner has been arranged for the first night of the course. A follow up Q&A session one month after completion will also be provided.

On successful completion Senior Managers will receive a certificate and digital badge that may be attached to online profiles confirming the training received and competence acquired.

Location

Edinburgh Futures Institute, 1 Lauriston Place, Edinburgh EH3 9EF

Course dates

Tuesday 4 & Wednesday 5 June 2024

Fees

£2475

Booking deadline

Wednesday 22nd May at 4pm UK time

Agenda

Tuesday 4 June 2024 | AI for Senior Managers in Financial Services
Time Session What you will learn
12:00-13:15 Coffee, lunch and registration An opportunity to network.
13:15-13:45 Introduction
  • Course overview
  • Meet the course instructors and fellow participants.
13:45-14:45 Understanding AI
  • An overview of AI technology, providing competence in understanding data, algorithms, models, and infrastructure at a level expected of Senior Managers.
  • Reviewing the reasons why Financial Services providers should identify where AI is used in the business.
  • An introduction to AI classification systems and approaches to inventories.
  • Legal and regulatory definitions of AI and related key concepts considered.
  • Examples of Financial Services use cases.
15:45-15:00 Coffee break
15:00-16:00 Financial Services legal and regulatory framework
  • An overview of the existing UK legal and regulatory requirements to comply with when developing, procuring, deploying and using AI.
  • Comparisons with approaches in other jurisdictions including the EU and US.
  • An overview of key relevant international governance and technical standards.
  • A consideration of current and forthcoming regulatory developments.
16:00-17:00 Data management
  • Review of key data risks.
  • Understanding the implications of using proprietary data in the context of AI.
  • Review of legal and regulatory requirements and guidance.
17:00-17:30 End day one. Questions and overview of day two.
18:00 Dinner at the Royal College of Physicians
Wednesday 5 June 2024
Time Session What you will learn
09:00-09:15 Welcome Day 2 welcome
09:15-10:15 Accuracy – validity – reliability
  • Review of key risks relating to reliability of AI systems, statistical accuracy, data accuracy and the validity of data inputs and generated outputs.
  • Examples of hallucinations, reliability metrics and benchmarks.
  • Review of legal and regulatory requirements and guidance.
10:15-10:30 Coffee break
10:30-11:30 Transparency – explainability – disclosures
  • Understanding transparency. A deep dive into transparency in context (audiences as clients, financial consumers, regulators, suppliers, other stakeholders).
  • Best practice approaches to explainability and a consideration of explainability frameworks.
  • A consideration of expectations forming around disclosure of confidential information relating to the development and use of AI systems.
  • Regulatory disclosure requirements in the context of AI.
11:30-12:30 Bias and discrimination
  • Understanding bias risks and concepts.
  • Consideration of the differences between bias, discrimination, unintended outcomes, and consequences for vulnerable customers.
  • The context of the consumer duty.
  • Legal and regulatory requirements and guidance.
  • Relevance and state of standards relating to bias and other risk controls.
12:30-13:15 Lunch
13:15-14:15 Monitoring and (human) oversight
  • Understanding monitoring and oversight throughout the AI use lifecycle. A review of expectations at each stage of the AI lifecycle.
  • Review of legal and regulatory requirements and guidance.
  • Implementing effective risk controls.
14:15-15:15 Intellectual property
  • Review of key risks relating to IP: ownership and infringement issues and approaches to licensing.
  • Review of legal and regulatory requirements and guidance.
  • Discussion of recent AI case law in various jurisdictions and the impact it may have on IP protection.
15:15-15:30 Coffee break
15:30-16:30 Liability
  • Review of harms, attribution of fault and consequences.
  • Discussion around the basis for transferring risk, which party is best placed to control the risk when procuring AI and emerging frameworks.
16:30-17:30 Governance frameworks and ethics
  • Discussion of Senior Managers and Certification Regime (SM&CR) and Senior Management Functions (SMFs) implications, key governance structures and issues.
  • Review of different approaches the organisation can take from a governance perspective. Who should be involved internally? When should they be involved? How to report to the Board on AI risk?
  • Review of AI ethics frameworks and how to adapt for and implement in Financial Services.
17:30-17:45 Close

Speakers

Luke Scanlon

Luke Scanlon

Luke Scanlon, Head of Fintech Propositions and Legal Director at Pinsent Masons has advised some of the world’s leading technology providers and financial institutions on AI legal and regulatory risk. Recognised by Chambers and Legal 500, he regularly provides training sessions and workshops on AI for Financial Services providers. He has also spoken at several conferences on AI in financial services, including the AI Scottish Summit, and has published extensively on the topic for publications including FT Adviser, The Sunday Times (Scotland) and The Banker magazine.

Mike Gregory

Mike Gregory

Mike is Postdoctoral Researcher in the Edinburgh School of Law and Edinburgh Futures Institute, where he teaches the postgraduate course on Ethics of Artificial Intelligence. He holds a PhD in Philosophy from the University of Groningen where he graduated with the highest distinction. Mike's research is currently on the legal and ethical considerations surrounding automated decision-making in governmental and financial institutions. He has published articles on legitimacy, the nature of rights, legal decision-making and democracy. His current research is a part of the project "Democracy, Rights and the Rule of Law in the Data-Driven Society".

Adam Ntakaris

Adam Ntakaris

Adam Ntakaris is a Lecturer in Financial Technology and Programme Director of the MSc in Finance, Technology, and Policy at the University of Edinburgh Business School with broad experience in consulting and managing industrial quant projects. He is also an investor in AI-focused companies. He received his PhD in High-Frequency Trading and Artificial Intelligence from Tampere University under the BigDataFinance Marie Sklodowska-Curie training network in 2019, the MSc degree in Financial Modeling and Optimization from the University of Edinburgh in 2014, and the BA degree in Mathematics from the Aristotle University of Thessaloniki, in 2009. He also completed a quant placement at Aberdeen Standard Investments in 2014. From 2014 to 2016, he was an Effective Interest Rate Analyst with CitiGroup Investment Bank, and from 2010 to 2013, a Mathematics Olympiad Coach at Systima.