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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 in Latin America.
- Why AI is essential today: beyond automation — detecting complex patterns and anomalies.
- Success cases and lessons learned from early AI adoption in global banking.
2. Foundations of AI for Executives: Key Concepts and Applications
- Artificial Intelligence and Machine Learning: what they are and how they transform risk detection.
- Real-time data processing: speed as a competitive advantage in fighting fraud.
- The value of data: identifying and preparing critical data sources for banking AI applications.
- 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: where AI can have the greatest impact in your organization.
- Assessing institutional data and technology maturity.
- Defining clear objectives and success metrics for AI risk projects.
- The importance of a 360° view of risk: integrating data from multiple channels and dimensions.
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 (loss reduction, fewer false positives, resource optimization).
- Impact on customer experience: balancing security and transaction fluidity.
- Strategic benefits: enhancing agility, scalability, and institutional reputation.
- How to quantify intangible value: brand protection and regulatory compliance.
2. Leadership of AI Projects and Outcome Evaluation
- Multifunctional teams: key roles and profiles (business, data, technology).
- Agile methodologies for AI implementation in banking environments.
- Continuous monitoring and adjustment: tools and processes for evaluating AI model performance post-deployment.
- Governance reporting and explainability (XAI): understanding AI decisions without technical expertise.
3. Optimizing AI Adoption: Advanced Implementation Strategies
- Build or Buy: strategic evaluation of AI solution implementation options.
- Advantages of internal capability development (full control, tailored adaptation).
- Benefits of external expert partnerships (proven experience, fast implementation, continuous innovation, reduced operational burden).
- Agility as a pillar: how specialized platforms accelerate responses to new fraud typologies and emerging threats (e.g. generative AI in fraud).
- Beyond fraud: the multidimensional potential of AI to prevent financial crime and ensure regulatory compliance.
- Next steps: developing a roadmap for AI-driven risk transformation in your institution.
Summary and Next Steps
Requirements
- Familiarity with banking risk management frameworks
- Understanding of digital transformation concepts in finance
- Interest in strategic applications of emerging technologies
Audience
- Banking executives
- Risk and compliance managers
- Decision-makers involved in fraud prevention and digital transformation
7 Hours
Testimonials (3)
The strategy to work on for each element
Edgar Gonzalez - Feedzai - Consultadoria e Inovacao Tecnologica, S.A.
Course - IA estratégica en prevención de riesgos bancarios
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The topic and how to communicate it
David Herrerias - Feedzai - Consultadoria e Inovacao Tecnologica, S.A.
Course - IA estratégica en prevención de riesgos bancarios
Machine Translated
the clarity and knowledge of the instructor
Martha Patricia Morales Torres Morales - Feedzai - Consultadoria e Inovacao Tecnologica, S.A.
Course - IA estratégica en prevención de riesgos bancarios
Machine Translated
