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Course Outline
Session 1: Preparing the Ground for AI in Risk
1. Strategic Diagnosis for AI Adoption
- Identification of knowledge and capability gaps in AI within the organization.
- Mapping of current fraud prevention processes: Where AI can optimize and transform.
- Common challenges in AI adoption in banking and how to overcome them.
- The executive vision of AI: Defining realistic expectations and impact metrics.
2. Operational Foundations of AI in Banking
- Types of AI applied to fraud detection: Supervised and unsupervised machine learning, Natural Language Processing (NLP).
- The importance of data quality and volume: Data collection, cleaning, and preparation for AI models.
- Data architectures for AI: Infrastructure required for processing large volumes of information in real time.
- Risk and Mitigation: Data Governance, Security, and Privacy in the Age of AI
3. Creating the Operational Business Case
- Definition of key operational metrics for AI (e.g., reduction of false positives, response time).
- Calculation of the operational and financial return on investment (ROI) of AI in crime prevention.
- Presentation of the business case to key stakeholders: Strategies for obtaining internal buy-in.
- AI as an enabler of operational efficiency and resilience.
Session 2: Leadership and Execution of AI Projects
1. Structure and Roles in an AI Project
- Identification of key profiles: Data scientists, ML engineers, business experts, risk specialists.
- AI team models: Internal teams vs. hybrid teams with external partners.
- Management of expectations and effective communication between technical and business teams.
- Design of a scalable and adaptable implementation roadmap.
2. Tools and Methodologies for AI Projects
- AI and ML Platforms (MLOps): Key concepts for managers (automation, monitoring, deployment).
- Use of visualization and analysis tools for data-driven decision-making.
- Agile methodologies (Scrum, Kanban) applied to the development and deployment of AI models.
- Considerations for integrating AI with existing legacy systems.
3. Continuous Monitoring and Tuning of AI Models
- The lifecycle of an AI model: From development to production and maintenance.
- Automated model monitoring: Detection of performance degradation and data drift.
- Retraining and redeployment strategies to maintain AI effectiveness in the face of new threats.
- The importance of a robust AI Governance framework.
Session 3: Optimization and Long-Term Vision of AI in Banking
1. Results Evaluation and Impact Measurement
- AI performance metrics: Accuracy, recall, loss reduction, false positive rate.
- Executive Dashboards: How to interpret results without being a technical expert.
- Model Audit and Validation: Ensuring the robustness and reliability of AI decisions.
- Reporting to senior management and regulators: Transparency and justification of AI performance.
2. Advanced Challenges and the Future of AI in Crime Prevention
- Generative AI and Deepfakes: New threats and how AI can combat them.
- Interbank collaboration and fraud intelligence sharing.
- AI in the context of anti-money laundering (AML) and organized crime.
- Building a pro-AI and data-driven organizational culture.
3. AI Capability Acquisition Strategies: Optimizing the Path
- Internal Development vs. Strategic Alliances: A key decision for speed and efficiency.
- Challenges of building AI capabilities from scratch: time, cost, scarce talent.
- Benefits of partnering with specialized platform providers: Instant access to cutting-edge technology, pre-trained models, extensive experience in banking fraud, lower risk and implementation time, and a focus on tangible results that free up internal resources for core initiatives.
- Agility and Adaptability: How external platforms enable rapid response to emerging threats and regulatory developments.
- Long-Term Strategy: Maximize the value of AI for comprehensive and continuous protection of your institution and your customers.
Requirements
- Familiarity with financial risk and fraud prevention processes
- Basic understanding of digital transformation in banking
- Experience in managing technology-driven initiatives
Audience
- Banking executives and decision-makers
- Operational risk and compliance leaders
- Digital transformation and innovation managers
9 Hours
