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Duration 9 hours
Course Outline
Session 1: Establishing the Foundation for Artificial Intelligence in Risk Management
1. Strategic Assessment for AI Deployment
- Identification of deficiencies in organizational AI knowledge and capabilities.
- Analysis of existing fraud mitigation workflows to determine areas suitable for optimization and transformation through AI.
- Addressing common barriers to AI integration in the banking sector and strategies for their resolution.
- Executive leadership perspective on AI: Setting realistic performance expectations and defining impact indicators.
2. Operational Prerequisites for AI in Banking
- Categories of AI applied to fraud identification: Supervised and unsupervised machine learning, and Natural Language Processing (NLP).
- Critical role of data integrity and scale: Procedures for gathering, refining, and preparing data for AI models.
- Architectural requirements for AI: Infrastructure necessary to handle high-volume information processing in real-time environments.
- Risk management and mitigation: Data governance, security, and privacy standards in the context of AI implementation.
3. Formulating the Operational Business Justification
- Establishing key operational indicators for AI performance, such as the reduction of false positives and improved response times.
- Assessment of operational and financial return on investment (ROI) for AI applications in crime prevention.
- Communicating the business case to key stakeholders: Tactics for securing internal support and buy-in.
- Leveraging AI to enhance operational efficiency and organizational resilience.
Session 2: Governing and Executing AI Initiatives
1. Organizational Structure and Roles in AI Projects
- Defining key professional profiles: Data scientists, machine learning engineers, business subject matter experts, and risk specialists.
- Models for AI teams: Comparing internal units with hybrid teams involving external partners.
- Aligning expectations and fostering effective communication between technical and business divisions.
- Developing a scalable and flexible implementation roadmap.
2. Tools and Methodologies for AI Development
- AI and ML Platforms (MLOps): Core concepts for managers regarding automation, monitoring, and deployment.
- Utilizing visualization and analytical tools to support data-driven decision-making.
- Application of agile methodologies (such as Scrum and Kanban) to the development and release of AI models.
- Strategic considerations for integrating AI systems with existing legacy infrastructure.
3. Ongoing Monitoring and Optimization of AI Models
- The full lifecycle of an AI model: From initial development through production deployment to maintenance.
- Automated model monitoring: Identifying performance degradation and data drift.
- Strategies for retraining and redeployment to sustain AI effectiveness against evolving threats.
- The necessity of a robust AI governance framework.
Session 3: Optimization and Long-Range Vision for AI in Banking
1. Evaluating Outcomes and Measuring Impact
- Key AI performance indicators: Accuracy, recall, loss mitigation, and false positive rates.
- Executive dashboards: Interpreting results effectively without requiring technical expertise.
- Model auditing and validation: Ensuring the robustness and reliability of AI-driven decisions.
- Reporting to senior leadership and regulatory bodies: Maintaining transparency and justifying AI performance.
2. Advanced Challenges and the Future of AI in Crime Prevention
- Generative AI and deepfakes: Emerging threats and countermeasures utilizing AI.
- Cross-institutional collaboration and the sharing of fraud intelligence.
- Application of AI in anti-money laundering (AML) and combating organized crime.
- Cultivating an organization that is AI-ready and driven by data.
3. Strategies for Acquiring AI Capabilities: Optimizing the Approach
- Internal development versus strategic alliances: Critical decisions impacting speed and efficiency.
- Challenges associated with building AI capabilities from the ground up: Time constraints, costs, and talent scarcity.
- Advantages of partnering with specialized platform providers: Immediate access to advanced technology, pre-trained models, extensive experience in banking fraud, reduced risk and implementation timelines, and a focus on tangible outcomes that conserves internal resources for core initiatives.
- Agility and adaptability: How external platforms facilitate rapid response to emerging threats and regulatory changes.
- Long-term strategy: Maximizing the value of AI for the comprehensive and continuous protection of the institution and its 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
Testimonials (1)
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