Course Outline
Session 1: AI as a Strategic Pillar in Risk Management
1. Current landscape of financial risk and the role of AI.
- The evolution of fraud and financial crime: challenges for modern banking, particularly in Latin America.
- Why AI is essential today: beyond automation — detecting complex patterns and anomalies to enhance risk management.
- Success cases and lessons learned from early AI adoption in global banking institutions.
2. Foundations of AI for Executives: Key Concepts and Applications
- Artificial Intelligence and Machine Learning: definitions and their transformative impact on risk detection.
- Real-time data processing: leveraging speed as a competitive advantage in combating fraud.
- The value of data: identifying and preparing critical data sources for AI applications in banking.
- Responsible and ethical AI: ensuring fairness, transparency, and regulatory compliance in model deployment.
3. Starting AI Adoption: Strategies and Critical Steps
- Identifying problems and opportunities: pinpointing areas where AI can have the greatest impact within your organization for government.
- Assessing institutional data and technology maturity to ensure readiness for AI implementation.
- Defining clear objectives and success metrics for AI risk management projects.
- The importance of a 360° view of risk: integrating data from multiple channels and dimensions to enhance decision-making.
Session 2: Generating Value and Leading Transformation with AI
1. Building the business case for AI in risk management.
- Cost-benefit analysis: measuring ROI from AI in fraud prevention, including loss reduction, fewer false positives, and resource optimization.
- Impact on customer experience: balancing enhanced security with seamless transaction processes.
- Strategic benefits: improving agility, scalability, and institutional reputation through AI adoption for government.
- How to quantify intangible value: protecting brand integrity and ensuring regulatory compliance.
2. Leadership of AI Projects and Outcome Evaluation
- Multifunctional teams: key roles and profiles, including business, data, and technology experts.
- Agile methodologies for effective AI implementation in banking environments.
- Continuous monitoring and adjustment: tools and processes for evaluating AI model performance post-deployment.
- Governance reporting and explainability (XAI): ensuring non-technical stakeholders understand AI decisions.
3. Optimizing AI Adoption: Advanced Implementation Strategies
- Build or Buy: strategic evaluation of AI solution implementation options for government.
- Advantages of internal capability development: full control and tailored adaptation to specific needs.
- Benefits of external expert partnerships: proven experience, rapid deployment, continuous innovation, and reduced operational burden.
- Agility as a pillar: how specialized platforms accelerate responses to new fraud typologies and emerging threats, such as generative AI in fraud.
- Beyond fraud: the multidimensional potential of AI to prevent financial crime and ensure regulatory compliance for government.
- Next steps: developing a roadmap for AI-driven risk transformation within your institution.
Summary and Next Steps
Requirements
- Knowledge of banking risk management frameworks for government and private sectors
- Understanding of digital transformation concepts in financial services for government operations
- Interest in the strategic applications of emerging technologies for government and industry
Audience
- Banking executives responsible for regulatory compliance and innovation
- Risk and compliance managers overseeing financial integrity and security
- Decision-makers involved in fraud prevention and digital transformation initiatives for government and corporate entities
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.
