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AI Governance in Financial Services

AI Governance in Financial Services

Starting October 30th

PAT Business School in partnership with i13Ventures & the Analytics Institute
A comprehensive 6 week course which provides financial services professionals with the knowledge and skills necessary to lead and manage AI initiatives effectively within their organizations.

 

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10% DISCOUNT AVAILABLE FOR:

  • PAT Business School Graduates
  • Group Bookings (2 to 5 ppl)

*Get in touch to receive your discount code – email: [email protected]

Payment Options
  • (3 interest-free payments)
    • 3 interest-free payments €272 (€272 first payment now and second payment of €267 due in 1 calendar month with final payment of €267 due in 2 calendar months)

Description

Description

The “AI Governance in Financial Services” course provides financial services professionals with the knowledge and skills necessary to lead and manage AI initiatives effectively within their organizations. As AI becomes increasingly integral to financial services, leaders must be equipped to navigate its complexities, align AI strategies with business objectives, and ensure compliance with evolving regulatory frameworks.

This course offers a comprehensive exploration of AI governance, focusing on the strategic, ethical, legal, and operational aspects of AI deployment. Participants will learn how to integrate AI into their business strategies, address ethical challenges, mitigate risks, and maintain AI systems’ transparency and accountability. The course also emphasizes the importance of AI literacy among leadership teams, ensuring informed decision-making and effective oversight of AI initiatives.

Throughout the course, participants will engage with real-world case studies, explore emerging trends in AI, and develop a practical AI governance strategy for a hypothetical financial institution. By the end of the course, participants will be well-prepared to lead AI initiatives that drive business value while adhering to best practices in AI governance.

 

Start Date:

Wednesday October 30th, 2024

 

Course Duration:

6 Weeks of 4 hours per week.

Each week will be blend of one hour of asynchronous (pre-prepared/recorded) content and a 3 hour ‘live on line’ class

 

Course Fee:

€800

(payment in installments available).

 

Course Delivery

The course will be delivered over 6 weeks, with each week consisting of 1 hour of asynchronous (pre-prepared/recorded) content and 3 hours of live online classes. The strategy focuses on maximizing the learning experience by combining self-paced study with interactive live sessions.

 

Assessment and Certification:

  • Assessments:
    • Weekly quizzes to reinforce learning.
    • A capstone project where participants develop an AI governance strategy for a hypothetical financial institution.
  • Certification:
    Participants will receive a certificate upon successful completion of the course, demonstrating their expertise in AI governance for financial services.

 

Course Learning Outcomes:

  1. Comprehend AI’s Role in Financial Services:
    • Understand the transformative potential of AI in financial services, including its applications in customer experience, fraud detection, business process automation, and regulatory compliance.
  2. Develop and Implement AI Strategies:
    • Formulate AI strategies that align with overall business objectives, ensuring AI initiatives drive competitive advantage and organizational value.
  3. Promote Ethical and Responsible AI Use:
    • Identify and address ethical challenges in AI, such as bias and transparency, ensuring that AI systems are fair, accountable, and align with corporate social responsibility goals.
  4. Navigate Legal and Regulatory Frameworks:
    • Apply relevant AI risk management frameworks (e.g., EU AI Act, NIST AI RMF) and ensure AI initiatives comply with legal and regulatory requirements specific to the financial services industry.
  5. Operationalize AI Governance:
    • Design and implement robust AI governance structures within financial institutions, ensuring seamless integration of AI systems with existing processes and continuous monitoring for compliance and performance.
  6. Prepare for Emerging Trends and Challenges:
    • Stay informed about emerging AI trends and evolving legal requirements, ensuring continuous adaptation and strategic foresight to anticipate and address future AI-related challenges.
  7. Enhance AI Literacy among Leadership:
    • Foster AI literacy among leadership teams, equipping them with the knowledge needed to make informed decisions about AI initiatives and their alignment with business goals.
  8. Execute AI Governance and Risk Mitigation:
    • Develop and apply effective risk mitigation strategies to manage AI-related risks, ensuring the responsible deployment of AI systems in a complex regulatory environment
  9. Drive Continuous Improvement in AI Governance:
    • Establish mechanisms for continuous learning and improvement in AI governance, ensuring that AI systems remain effective, compliant, and aligned with evolving business needs and regulatory standards.
  10. Capstone Project Completion:
    • Successfully develop an AI governance strategy for a hypothetical financial institution, demonstrating the ability to apply course concepts to real-world scenarios.

 

Personas of Learners

  1. Senior Executives (e.g., CEOs, COOs, CFOs)
  • Motivation: To understand how AI can be strategically integrated into the business, drive innovation, and maintain a competitive edge in the financial services industry.
  • Goals: Ensure AI initiatives align with overall business strategy, manage organizational risks, and leverage AI for sustainable growth.
  1. Compliance Officers
  • Motivation: To stay ahead of evolving regulations and ensure that AI systems deployed within the organization comply with legal standards.
  • Goals: Develop a deep understanding of AI governance frameworks, mitigate regulatory risks, and implement robust compliance monitoring systems.
  1. Risk Management Professionals
  • Motivation: To learn how to identify, assess, and mitigate risks associated with AI systems in financial services.
  • Goals: Implement risk management strategies that address the unique challenges posed by AI, including ethical risks and bias in AI models.
  1. IT and Data Governance Leaders
  • Motivation: To gain insights into how to operationalize AI within the organization, ensuring data quality, security, and governance.
  • Goals: Design and implement AI governance structures, integrate AI systems with existing IT infrastructure, and ensure data governance standards are upheld.
  1. AI and Data Science Managers
  • Motivation: To bridge the gap between technical AI development and strategic business objectives, ensuring that AI projects deliver value while being ethically sound.
  • Goals: Align AI projects with business goals, manage cross-functional AI teams, and ensure ethical AI practices are followed.
  1. Strategic Planners and Business Analysts
  • Motivation: To understand the potential of AI in transforming financial services and to help shape the future direction of AI initiatives within their organization.
  • Goals: Conduct market analysis, identify AI opportunities, and develop strategic plans that incorporate AI-driven innovation.
  1. Legal Advisors and Corporate Counsel
  • Motivation: To navigate the complex legal landscape surrounding AI in financial services, ensuring the organization’s AI practices are legally sound.
  • Goals: Stay informed about AI regulations, provide legal guidance on AI initiatives, and manage legal risks associated with AI deployment.
  1. Product Managers and Innovation Leads
  • Motivation: To explore how AI can be leveraged to create innovative financial products and services that meet customer needs.
  • Goals: Develop AI-driven products, ensure they align with governance and compliance requirements, and drive innovation within the organization.
  1. Human Resources (HR) Professionals
  • Motivation: To understand the impact of AI on the workforce, including ethical considerations related to AI in hiring and employee management.
  • Goals: Develop policies and practices that ensure responsible AI use in HR, manage the change brought by AI adoption, and enhance AI literacy within the workforce.
  1. Consultants and Advisors
  • Motivation: To advise clients in the financial services sector on best practices for AI governance, strategy, and risk management.
  • Goals: Gain expertise in AI governance to provide informed recommendations, help clients navigate regulatory landscapes, and support AI strategy development.

These personas represent a diverse range of professionals who would benefit from understanding AI governance, especially within the context of financial services. Each brings a unique perspective and set of objectives, contributing to the holistic adoption and management of AI within their organizations.

 

Course Delivery Breakdown

  1. Pre-Class Preparation (Asynchronous Learning)

Objective: Equip participants with foundational knowledge before the live session.

Approach:

  • Content Delivery: Each week, provide a 1-hour video or set of micro-learning modules that cover the core concepts, definitions, frameworks, and key theories related to that week’s topics.
  • Interactive Elements: Include quizzes, reflection prompts, or short assignments within the asynchronous content to ensure engagement and retention.
  • Pre-reading Materials: Provide additional reading materials (articles, case studies, or reports) that participants should review before the live session. These materials will complement the video content and offer deeper insights into the topics.
  • Discussion Forum: Set up a discussion board where participants can post questions or comments about the asynchronous content. Encourage participants to engage with each other’s posts to foster a sense of community.
  1. Live Online Classes

Objective: Deepen understanding through interactive discussions, case studies, and practical application of concepts.

Structure:

  • Hour 1: Review and Q&A
    • Recap: Start with a 10-15 minute recap of the key points from the asynchronous content.
    • Q&A Session: Address questions from participants based on the pre-class content and discussion board. This will clarify any doubts and ensure everyone is on the same page.
  • Hour 2: Interactive Case Study/Scenario Analysis
    • Case Study Discussion: Introduce a case study or a real-world scenario that relates to the week’s topics.
    • Breakout Groups: Divide participants into small groups to analyze the case study and discuss solutions or strategies.
    • Group Presentations: Each group presents their analysis, followed by a class discussion on the key takeaways.
  • Hour 3: Practical Application and Wrap-Up
    • Hands-on Exercise: Engage participants in a practical exercise, such as developing a mini-strategy, creating a governance framework, or analyzing risks related to AI deployment.
    • Feedback Loop: Provide immediate feedback on their work, highlighting best practices and areas for improvement.
    • Summary and Next Steps: Conclude the session with a summary of key learnings, linking them to the upcoming week’s topics. Assign any follow-up tasks or readings.
  1. Post-Class Engagement

Objective: Reinforce learning and ensure continuous engagement throughout the week.

Approach:

  • Discussion Forums: Keep the discussion boards active for post-class reflections. Encourage participants to share insights from the live session and discuss any challenges they encountered.
  • Office Hours: Offer optional office hours or one-on-one sessions for participants who need further clarification or wish to discuss specific topics in more depth.
  • Weekly Quiz: Provide a short quiz or reflection activity to be completed after the live session, helping to reinforce the week’s learning objectives.
  1. Continuous Improvement

Objective: Ensure the course remains dynamic and responsive to participant needs.

Approach:

  • Feedback Collection: At the end of each week, collect feedback from participants on the asynchronous content, live sessions, and overall learning experience.
  • Adapt and Adjust: Use the feedback to make real-time adjustments to the course delivery, such as tweaking content, adjusting the pace, or offering additional resources.

Summary

By blending asynchronous learning with interactive live sessions, this strategy aims to provide a well-rounded, engaging, and effective learning experience. The asynchronous content builds foundational knowledge, while the live sessions offer opportunities for deep discussion, practical application, and real-time feedback. This combination not only maximizes learning outcomes but also keeps participants actively engaged throughout the course.

 

Course Content

Week 1: Introduction to AI Governance in Financial Services

Overview:
This week introduces the fundamentals of AI governance, emphasizing the importance of AI in transforming the financial services sector and the critical role of governance in managing AI deployment effectively.

Learning Outcomes:

  • Understand the transformative potential of AI in financial services.
  • Grasp the foundational concepts and frameworks of AI governance.
  • Identify and analyze the key stakeholders involved in AI governance.

Key Topics:

  1. AI’s Role in Financial Services:
    • In-depth exploration of AI applications in areas like customer experience, fraud detection, business process automation, and regulatory compliance.
    • Discussion on how AI is reshaping financial services and its implications for the industry.
  2. Understanding AI Governance:
    • Detailed analysis of AI governance definitions, scope, and frameworks.
    • Examination of global AI governance frameworks, including the EU AI Act and NIST AI RMF, and their relevance to financial services.
  3. The Need for AI Literacy:
    • Exploration of the necessity for AI literacy among financial services leaders.
    • Understanding AI’s capabilities, limitations, and the importance of informed decision-making.
  4. Stakeholders in AI Governance:
    • Identification of key stakeholders such as regulators, tech vendors, internal governance teams, and their roles in AI governance.
    • Case studies illustrating stakeholder interactions in AI governance.

 

Week 2: AI Strategy and Leadership in Financial Services

Overview:
This week focuses on aligning AI strategies with business objectives and the critical role of leadership in steering AI initiatives.

Learning Outcomes:

  • Develop an AI strategy that aligns with and drives business objectives.
  • Apply the AI Blueprint framework for effective AI project management.
  • Recognize the role of leadership in fostering a governance culture that supports AI innovation.

Key Topics:

  1. Aligning AI with Business Goals:
    • Strategies for embedding AI into business strategy to enhance competitive advantage.
    • Discussion on the importance of strategic coherence in AI initiatives across business units.
  2. Developing an AI Blueprint:
    • A comprehensive guide to planning, implementing, and scaling AI projects.
    • Case studies of successful AI Blueprint applications in financial institutions.
  3. Leadership and AI Governance:
    • Examination of leadership’s role in promoting ethical AI practices.
    • Strategies for fostering a culture of AI innovation while maintaining governance.
  4. Data as a Strategic Asset:
    • The role of data governance in AI strategy.
    • Ensuring data quality, security, and governance in AI-driven projects.

 

Week 3: Ethical and Responsible AI in Financial Services

Overview:
This week addresses the ethical challenges of AI, focusing on bias, transparency, and fairness in AI systems.

Learning Outcomes:

  • Identify and mitigate ethical risks in AI deployment.
  • Ensure AI systems align with corporate social responsibility goals.
  • Promote a culture of ethical AI within the organization.

Key Topics:

  1. Ethical AI in Practice:
    • Real-world examples of ethical challenges in AI, such as bias in decision-making algorithms.
    • Techniques for addressing ethical dilemmas in AI deployment.
  2. AI and Corporate Social Responsibility:
    • Aligning AI governance with broader corporate values and societal responsibilities.
    • Discussion on AI’s impact on societal equity and fairness.
  3. Addressing Bias and Fairness:
    • Methods for detecting, assessing, and mitigating bias in AI systems.
    • Case studies of bias in AI and successful mitigation strategies.
  4. Transparency and Accountability:
    • Strategies for enhancing transparency and ensuring accountability in AI-driven decisions.
    • Tools and frameworks for auditing and monitoring AI systems for fairness and transparency.

 

Week 4: Risk Management and Legal Considerations

Overview:
This week delves into the legal and risk management aspects of AI governance, focusing on compliance and risk mitigation.

Learning Outcomes:

  • Apply AI risk management frameworks to mitigate AI-related risks.
  • Navigate the legal landscape surrounding AI in financial services.
  • Learn from case studies to avoid common pitfalls in AI risk management.

Key Topics:

  1. AI Risk Management Frameworks:
    • In-depth analysis of risk management frameworks like the EU AI Act and NIST AI RMF, and IS0 42001.
    • Techniques for assessing and mitigating AI risks specific to financial services.
  2. Legal Compliance in AI:
    • Navigating the complex legal requirements for AI deployment, including data protection laws and sector-specific regulations.
    • Discussion on the implications of the EU AI Act on financial institutions.
  3. Mitigating AI Risks:
    • Techniques for identifying, assessing, and mitigating various AI-related risks.
    • Case studies highlighting successes and failures in AI risk management.
  4. Case Studies in AI Risk Management:
    • Examination of real-world examples where AI risk management either succeeded or failed.
    • Lessons learned and best practices for future AI deployments.

 

Week 5: Operationalizing AI Governance

Overview:
This week focuses on the practical aspects of implementing and operationalizing AI governance within financial institutions.

Learning Outcomes:

  • Implement robust AI governance structures within financial institutions.
  • Establish effective monitoring and incident response plans for AI systems.
  • Integrate AI solutions seamlessly with existing business processes.

Key Topics:

  1. Operationalizing the AI Blueprint:
    • Steps to transition from AI strategy to execution, including setting up governance structures.
    • Practical examples of operationalizing AI governance in financial services.
  2. Continuous Monitoring and Improvement:
    • Establishing mechanisms for ongoing monitoring of AI systems to ensure compliance and effectiveness.
    • Tools for continuous improvement and adaptation in AI governance.
  3. Integrating AI with Existing Processes:
    • Strategies for ensuring AI solutions are compatible with and enhance existing business processes.
    • Case studies on successful AI integration.
  4. AI Incident Response Planning:
    • Developing and implementing AI-specific incident response plans.
    • Preparing for and managing AI-related incidents, including data breaches and system failures.

 

Week 6: Future Trends and Continuous Learning in AI Governance

Overview:
The final week examines emerging trends in AI and the importance of continuous learning and adaptation in AI governance.

Learning Outcomes:

  • Stay informed about emerging AI trends and their implications for financial services.
  • Adapt to evolving legal requirements and maintain AI literacy within leadership.
  • Develop strategic foresight to anticipate and prepare for future AI challenges.

Key Topics:

  1. Emerging AI Trends in Financial Services:
    • Exploration of future AI applications, such as predictive analytics and AI-driven compliance.
    • Discussion on the impact of emerging technologies on AI governance.
  2. The Evolving Legal Landscape:
    • Anticipating changes in AI regulations and their potential impact on financial services.
    • Strategies for staying ahead of regulatory developments.
  3. Continuous Learning and AI Literacy:
    • Importance of maintaining AI literacy among leadership and across the organization.
    • Resources and strategies for continuous learning in AI governance.
  4. Strategic Foresight:
    • Preparing for future AI challenges and opportunities, including new regulatory requirements and technological advancements.
    • Scenario planning and strategic foresight exercises to anticipate AI-related disruptions.