Course Outline
Session 1: Preparing the Ground for AI in Risk Management for Government
1. Strategic Diagnosis for AI Adoption
- Identification of knowledge and capability gaps in AI within the organization.
- Mapping of current fraud prevention processes: Opportunities for AI to optimize and transform.
- Common challenges in AI adoption in banking and strategies to overcome them.
- Defining realistic expectations and impact metrics for executive leadership.
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: Best practices for data collection, cleaning, and preparation.
- Data architectures for AI: Infrastructure requirements for processing large volumes of information in real time.
- Risk management and mitigation: Data governance, security, and privacy considerations 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.
- Strategies for presenting the business case to key stakeholders and 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 versus hybrid teams with external partners.
- Effective communication and management of expectations between technical and business teams.
- Designing a scalable and adaptable implementation roadmap.
2. Tools and Methodologies for AI Projects
- Key concepts in AI and ML Platforms (MLOps) for managers: Automation, monitoring, deployment.
- Utilizing visualization and analysis tools for data-driven decision-making.
- Applying agile methodologies (Scrum, Kanban) 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: Detecting performance degradation and data drift.
- Strategies for retraining and redeploying models to maintain effectiveness against 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: Interpreting results without technical expertise.
- 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 countermeasures using AI.
- Interbank collaboration and fraud intelligence sharing.
- AI in anti-money laundering (AML) and organized crime prevention.
- Building a pro-AI and data-driven organizational culture.
3. AI Capability Acquisition Strategies: Optimizing the Path
- Internal development versus strategic alliances: Key decisions for speed and efficiency.
- Challenges of building AI capabilities from scratch: Time, cost, and talent scarcity.
- Benefits of partnering with specialized platform providers: Instant access to cutting-edge technology, pre-trained models, extensive experience in banking fraud, lower risk, reduced 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: Maximizing the value of AI for comprehensive and continuous protection of your institution and customers.
Requirements
- Familiarity with financial risk management and fraud prevention processes for government
- Basic understanding of digital transformation in the banking sector
- Experience in overseeing technology-driven initiatives
Audience
- Banking executives and decision-makers for government
- Operational risk and compliance leaders for government
- Digital transformation and innovation managers for government
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.
