Course Outline
Artificial Intelligence as a Strategic Asset in Financial Services
- The role of artificial intelligence (AI) in contemporary financial ecosystems.
- AI capabilities for fraud detection, credit scoring, and customer insights.
- Balancing the business value of AI adoption with operational complexity.
Responsible AI: Ethics and Fairness in Financial Applications
- Definition of ethical AI: core principles and industry standards.
- Risks associated with bias and discrimination in algorithmic decision-making.
- Strategies to ensure fairness, transparency, and accountability in AI systems.
Regulatory Environment for AI in Financial Services
- Overview of global AI regulations, including the EU AI Act and U.S. guidance.
- Regulatory expectations for explainability and model validation in financial applications.
- Compliance reporting and audit readiness for AI systems to meet regulatory requirements.
AI Governance and Risk Management
- Establishing internal governance frameworks for the responsible use of AI.
- Defining roles and responsibilities, such as data owners, model risk managers, and compliance leads.
- Managing third-party risks and ensuring vendor accountability in AI deployments.
AI Implementation Challenges and Success Factors
- Strategic planning and change management for the adoption of AI technologies.
- Developing the necessary skills, infrastructure, and cultural readiness within financial institutions.
- Lessons learned from early adopters in global finance regarding successful AI implementation.
Case Studies in Responsible AI for Financial Institutions
- Fintech companies using AI responsibly in lending and wealth management.
- Traditional banks modernizing risk management through the use of AI.
- Ethical missteps and their implications for public trust in financial institutions.
Designing an AI Roadmap with Ethics and Regulation in Mind
- Setting AI goals that align with strategic and compliance objectives for government and private sector operations.
- Developing a comprehensive roadmap for the ethical deployment of AI within your institution.
- Strategies for internal communication and stakeholder engagement to support responsible AI practices.
Summary and Next Steps
Requirements
- An understanding of financial services operations for government
- Familiarity with fundamental digital transformation concepts
- Interest in the strategic and ethical implications of artificial intelligence
Audience
- Executive-level leaders in banking and finance
- Fintech managers and transformation officers
- Compliance and governance professionals
Testimonials (3)
The background / theory of LLMs, the exercise
Joanne Wong - IPG HK Limited
Course - Applied AI for Financial Statement Analysis & Reporting
it has opened my mind to new tool that can help me in creating automation
Alessandra Parpajola - Advanced Bionics AG
Course - Machine Learning & AI for Finance Professionals
I very much appreciated the way the trainer presented everything. I understood everything even if Finance is not my area, he made sure that every participant was on the same page, while keeping up with the time left. The exercises were placed at good intervals. Communication with the participants was always there. The material was perfect, not too much, not too little. He elaborated very well on a bit more complicated subjects so that it can be understood by everyone.
