Law, Regulation and Ethics of Advanced Data Analytics in Financial Services

Foundations of Advanced Data Analytics

You will establish a foundation in the data analytics technologies commonly used in financial services in Canada. You will also unpack the hype around advanced analytics by discussing the opportunities, challenges and myths associated with the use of technologies such as artificial intelligence (AI) and machine learning (ML).

Risks of Using Personal Information in an Evolving Regulatory Landscape

• What’s the problem? Why is it important to discuss this topic now? Key risks and challenges
• What’s happening in Canada and around the world?
• Overview of key principles – what do the new/evolving laws, regulations and policies seek to achieve?
• What are other regulated markets doing?
• How can the industry innovate while meeting regulators’ expectations and new laws?

Foundations in the Technology and Terminology

• Fundamentals of data analytics (demystifying the terminology, the technology)
• Data analysis vs. advanced analytics
• How is AI different from ML?
• How are models built? What is the role of data scientists?
• What is predictive modeling? What are other types of models?
• Risks and benefits of self‑learning AI. What are other types of AI and how are they used?
• Transparency challenge: using plain language to describe how the technologies work and how the organization is using them and managing the risks

Application of Advanced Analytics and AI in the Financial Industry

• Application of AI and other technologies – including use cases and real‑world examples
• Review of notable emerging technologies
• What is the value brought by advanced analytics such as AI and ML?
• What is driving the financial institution’s business case to use/invest in these technologies?
• Overview of the broader landscape – blockchain, open banking, metaverse, etc – how they’re coming together

Interactive Activity: Working Through the Risks of Using Data Analytics Technology*

Using a realistic scenario, you will breakout into small groups to work through risk and issues, brainstorm solutions and share findings with the class. Faculty will provide guidance/support, and the session will conclude with a group debrief.

*Note: breakouts will not be recorded.

The Ethics and Privacy of Advanced Analytics Activities

You will identify key ethics and privacy principles contributing to the changing regulatory environment and answer the questions: Are these new principles or old principles in new clothing? What are the new ethical issues introduced by the use of large amounts of collected, combined and derived data for advanced analytics? How are privacy interests at play, and what can organizations do to augment their privacy management programs with advanced analytics governance?

Understanding Ethical Concerns

• Overview of the key ethics principles contributing to challenges in financial services
• Monitoring and anticipating changes in the regulatory environment
• Identifying, assessing, documenting and managing ethical risk
• Insights and top tips for developing frameworks and assessment tools

Key Factors and Impacts Involving Data Use

• Unintended bias, unjustified discrimination and unfair treatment
• Ethical concerns with profiling
• Adverse impacts of automated decision‑making
• Ethical concerns with the use of derived data for decision‑making
• How to approach the ethical use of AI and data

Cost‑Benefit Analysis and Using Ethics as a Competitive Advantage

• Understanding the use of ethics in AI governance as a competitive advantage
• Cost‑benefit analysis for leading use cases of AI in financial institutions
• Practical considerations and intersections involving ethics, transparent practices, and client confidence/trust
• Examples of how organizations have done this well
• Strategies for avoiding common pitfalls and for augmenting ethics to enhance your business

Choice, Transparency and Disclosure When Using Data

• Difference between data ethics and privacy – and data ethics and AI ethics
• New privacy risks from the use of data in AI:
  • Large, centralized datasets and the use of third‑party data
  • Basis for consent and individual choice
  • Disclosure requirements
• Transparency requirements
• Would the AI product be considered PI/client?
• Practical approaches for managing the risks

Using Data in a Privacy Respectful Way

• What does it mean to use data in advanced analytics in a privacy respectful way?
• Reviewing the spectrum of identifiability, Privacy Enhancing Technologies and other key considerations
• Methods for identifying, assessing, documenting and managing privacy risks – including augmenting personal information assessments (PIAs) to capture advanced analytics risks and compliance requirements

Interactive Activity: Working Through Ethical and Privacy Issues*

You will engage in a mini‑scenario analysis and breakout into small groups to work through issues and brainstorm solutions and share findings with the class. Faculty will provide guidance/support, and the session will conclude with a group debrief.

*Note: breakouts will not be recorded.

Regulating Advanced Analytics Activities

You will delve into the specifics of the law, regulation and policies applicable to advanced analytics and how financial institutions are currently navigating them. You will also examine current and emerging standards and guidance for financial institutions, nationally and globally.

Law, Policies and Regulation of AI in Financial Services

• Overview of key principles that the law and policies are seeking to align with
• Current regulatory environment specific to AI and why it’s an area of focus for regulators
• Challenges to operating in an environment with few laws specific to advanced analytics and AI
• How can financial institutions prepare compliance frameworks where there is a lack of defined regulatory obligations?
• How are other financial institutions handling these risks and challenges?
• Tips for creating adaptive frameworks and standards

Regulatory Frameworks and Current Practices

• Existing regulatory frameworks relevant to advanced analytics activities:
  • Data protection and privacy laws
  • Human rights and anti‑discrimination legislation and policies
  • Consumer protection and competition laws
• How are financial institutions navigating these in practice?
• Current practices for dealing with risk and strategies for avoiding common pitfalls
• Key considerations/takeaways as the landscape evolves, and to support compliance efforts

Evolving Global Regulatory Landscape

• Global trends – including white papers, regulators’ expectations, findings, new laws, academic research, think tank publications
• EU AIA, US AIA, etc, and China
• EU draft Product Liability Directive for AI
• NIST AI Risk Management Framework
• Singapore AI Verify
• Regulation of AI in China and Brazil
• UK strategy and guidance on AI in financial services
• Important learnings and takeaways

What to Expect in Canada

• Key impacts of global trends and developments on Canadian law makers, policy makers and regulators
• Bill C‑27
• TBS AI Directive
• QC Bill 25
• AIA – impact assessment
• Practical considerations and strategies to adapt to changes

Legal Developments and Trends: Recent Cases and Decisions

• Overview of important legal developments and trends
• Key takeaways from the latest lawsuits and class actions in Canada and globally
• Re‑examining the concepts of liability and responsibility for damages caused by the use of AI – with reference the OECD paper
• What does this mean for financial institutions? Tips for getting prepared

Interactive Class Discussion: What Would You Do?

You will engage in a brief review and class discussion involving multiple short fact scenarios to reinforce key concepts and develop analytical skills and problem‑solving approaches for working through evolving AI law and standards. Faculty will provide guidance/support during the discussion, as well as additional ideas and resources to consider.

Legal Issues and Third Party Risks Related to Advanced Analytics

You will focus on the risks specific to the use of third parties for AI development, including the sharing of large amounts of data, understanding the third party methodology and assessing whether it complies with the privacy and ethical principles defined by the financial institution. You will also review important IP rules and other factors to help ensure continued accountability and data confidentiality.

Third‑Party Models and Vendor Management Issues

• Vendor assessments and associated risks
• Contracting
• Third party risk management and oversight
• Strategies for developing frameworks to manage third party and vendor risk
• Insights and tips for successful implementation of third party and vendor risk management efforts and programs within the organization
• Emerging risks and issues to watch out for

Managing Third Party Accountability and Data Confidentiality Risks

• Risks of sharing large amounts of data with a third party (to build AI) – disclosure and risks associated with the third party’s own use
• Risks and benefits of allowing downstream data use (aggregation, sharing/selling insights)
• Contractual means to define: consent, ownership and liability when things go wrong
• Overcoming issues involving monitoring and auditing compliance with contractual obligations

Understanding IP Rules in the AI World

• Understanding IP and IP rights
• How to demonstrate compliance with regulatory and ethical requirements, considering limited sharing of proprietary information about the AI build
• Impacts and intersections with AI
• Key considerations and issues for financial services
• Important trends and takeaways

Interactive Activity: Working Through Legal Issues and Third Party Risks*

You will engage in a mini‑scenario analysis and breakout into small groups to work through issues and brainstorm solutions and share findings with the class. Faculty will provide guidance/support, and the session will conclude with a group debrief.

*Note: breakouts will not be recorded.

Designing a Responsible AI Program for Financial Services

You will synthesize learnings and get a ‘hands‑on’ opportunity to work through issues, apply practical solutions and to develop frameworks for augmenting responsible AI programs and associated processes to align with emerging regulations and expectations.

Balancing the Benefits and the Challenges of AI Use

• Thinking beyond business profit – socially beneficial use of data
• Importance of recognizing, assessing and documenting potential adverse impacts from the use of AI, and whether the benefits outweigh the risks
• Challenge of “reasonable person” test when assessing individual expectations for impacts from use of AI
• Bridging the gaps and discrepancies in education, understanding, comfort and technology

Keeping Up with New and Emerging Technology

• How can regulators, lawyers, compliance and risk officers catch up? Continuous education challenges
• How to set a foundation for various emerging technologies including blockchain, the metaverse, and others
• What about generative AI?
  • Key risks, challenges, liabilities, limitations involving platforms such as OpenAI’s ChatGPT and Microsoft/ Github’s assisted coding tool CoPilot
  • Risk management considerations and cautions
• Putting emerging technologies into practice – the ISO work/standard

Interactive Activity: Building an AI Responsible Process*

You will analyze the risks to build a process that aligns with responsible AI principles and practices. You will be able to choose or create a brief problem scenario and you will be able to work individually or in small groups. Faculty will provide guidance/support, and the session will conclude with a group debrief.

*Note: breakouts will not be recorded.

Online Final Exam

Will be distributed after Module 5 ends and it requires a passing grade of 80% by November 27, 2023.