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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
7 Hours
Testimonials (1)
It was fairly interactive, quite well focused on our areas of interest. It gives a good base for further development in the area
