Tuesday 4 March 2025
08:00 - 08:50
Registration, Coffee and Breakfast
08:50 - 09:00
Chair’s Opening Remarks
09:00 - 09:35
REGULATION
Optimizing model risk management: Key strategies for compliance, efficiency, and AI integration
- Analyze the current regulatory priorities related to MRM
- Identify optimal strategies to enhance audit procedures, ensuring greater efficiency while maintaining compliance
- Investigate the most effective set of tools and strategies for managing and validating complex models
- Evaluate the differences in MRM processes required for traditional models compared to AI-driven models
09:35 - 10:15
RISING SCRUTINY - PANEL DISCUSSION
Impact of new AI regulations on governance and model risk management
- Evaluating global, regional, and state-level AI/ML regulations and their effects on model risk management (e.g., GDPR, CCPA, IFRS, California, New York)
- Assessing regulatory expectations for model risk teams and how they must adapt to new requirements
- Analyzing increased regulatory scrutiny's influence on compliance, credit risk, and liquidity management
- Enhancing model governance to detect credit risk trends early and ensure board accountability
- Exploring alternative and customer data impacts on regulations in the EU/US and financial products like auto loans
- Aligning risk management practices with global, national, and state regulations
10:20 - 10:50
Morning refreshment break and networking
10:50 - 11:25
AI MODEL MANAGEMENT
Advancing AI model risk evaluation and validation practice in the era of advanced technology
- Model risk assessment, model risk appetite, model risk mitigation
- Digitalization of IV activity and use of GEN AI to enhance validation capabilities
- Example of validation of a credit risk model that apply ML/AI techniques
11:25 - 12:00
LLM RISK VALIDATION IN FINTECH
Adapting and Validating Risk Management Frameworks for Large Language Models
- Addressing challenges in successful LLM implementation
- Bank vs Fintech differentiation
- Accelerating performance evaluation beyond the 2–3-year standard
- Adjusting traditional frameworks for the specific needs of large language models
- Creating comprehensive testing and validation approaches to address LLM complexities
- Adopting risk mitigation strategies tailored for LLMs based on adapted frameworks
12:00 - 12:35
ENHANCING METHODS & PRACTICE
Adjusting practices to modern challenges and technologies
- Identifying gaps in frameworks for assessing risks in AI and Gen AI models
- Addressing new failure modes from model experimentation
- Determining tools and technologies for regulatory scrutiny and operational scaling
- Resolving collaboration challenges between model development and risk/compliance teams
- Incorporating AI tools into model development and validation without increasing risk
12:35 - 13:35
Lunch and networking break
13:35 - 14:20
TALENT MANAGEMENT - PANEL DISCUSSION
Futureproofing talent and training needs for AI models
- Identifying current and future training needs for AI and Generative AI
- Addressing skill gaps for effective AI model management
- Anticipating evolving training requirements in the next 1-2 years
- Comparing skill requirements for AI versus traditional model validation
- Reinforcing the need for continuous training to keep pace with AI advancements
- Examining successful training programs and comparing centralized versus decentralized solutions
- Training AI teams to identify and mitigate fraud risks from AI-powered bad actors
14:20 - 14:55
ADAPTING MRM FOR GROWTH
Integrating MRM programs for small banks with AI applications (crossing the $10b mark)
- Changes in oversight
- MRM as a strategy tool: Improving the effective challenge
- Typical new models for small banks
- Reducing the budget, bringing validations from external to internal
- Education with model owners and training staff
- Collaborating between departments
- Adjusting to AI developments
- Testing black-box AI applications (tools)
- Framework of MRM review for AI applications vs AI models
14:55 - 15:30
ETHICS AND BIAS
Addressing GEN AI ethical considerations and minimizing bias in model risk
- Developing strategies to mitigate risks such as model hallucinations
- Reducing hallucinations, bias, and toxicity in LLMs
- Analyzing implications in automated decision-making
- Achieving socially responsible outcomes through AI
- Promoting transparency and accountability in machine learning models
- Implementing AI governance frameworks to ensure fairness and accountability
- Minimizing ethical risks and biases in financial AI models
15:30 - 16:00
Afternoon refreshment break and networking
16:00 - 16:45
GEN AI VALIDATION – PANEL DISCUSSION
Ensuring model validation for Gen AI through governance and automation
- Reviewing governance practices for validating generative AI models
- Enhancing validation efficiency through automation and risk-tier categorization
- Exploring best practices for evaluating and validating AI models
- Addressing hallucinations to ensure reliable model outputs
- Establishing governance frameworks for generative AI use
- Managing new failure modes emerging from generative AI experimentation
16:45 - 17:20
AUTOMATED RISK MONITORING
Strategizing automation in monitoring for enhanced compliance and risk management
- Aligning with regulatory expectations and setting performance tolerance levels
- Integrating automation into risk identification and monitoring processes
- Formulating strategies for integrating automation into ongoing monitoring systems
- Optimizing model ops and efficiency gains
17:20 - 17:30
Chair’s closing remarks
17:30
End of day one and networking drinks reception
Wednesday 5 March 2025
08:00 - 08:50
Registration, Coffee and Breakfast
08:50 - 09:00
Chair’s Opening Remarks
09:00 - 09:35
MACROECONOMIC LANSCAPE
Assessing Macroeconomic Impacts on Model Sensitivity and Risk
- Assessing the impact of fed rates, inflation, and economic cycles on model sensitivity and risk.
- Addressing changing economic environments and unpredictable global volatility.
- Reviewing AI-driven financial models
- Incorporating macroeconomic data into stress testing and financial forecasting.
09:35 - 10:10
ANTI-FRAUD AND FINANCIAL CRIME – CASE STUDY
Refining financial crime models for adaptability to fraud patterns and regulatory changes
- Utilizing biometrics to identify synthetic fraud and identity theft
- Leveraging data and analytics to enhance efficiency
- Optimizing performance metrics and sampling for AML and sanctions screening
- Ensuring explainability and interpretability of AI/ML models for regulatory compliance
- Deploying methods and strategies to detect and prevent financial crimes enabled by AI technology
- Examining case studies on how AI is used for KYC fraud prevention
- Presenting real-life case studies on fraud schemes and how AI can combat them
- Outlining best practices for deploying AI fraud detection systems within highly regulated environments
10:10 - 10:40
Morning refreshment break and networking
10:40 - 11:15
MODEL DOCUMENTATION EFFICIENCY – USE CASE
Optimizing model documentation and automation efficiency
- Outlining strategies for comprehensive model lifecycle documentation to support auditing and validation.
- Exploring how automation enhances the efficiency of managing model documentation.
- Emphasizing the benefits of automated documentation.
11:15 - 12:00
AUTOMATION - PANEL DISCUSSION
Enhancing efficiency through automation in model risk documentation, reporting, and validation
- Identifying strategies for managing different risk tiers in model validation.
- Reviewing tools and methods for automating and categorizing model validation processes.
- Streamlining model auditing and validation throughout their lifecycle.
- Adopting automation to improve efficiency in documentation and reporting processes.
- Managing and validating third-party models.
- Combining automation and best practices to achieve holistic model risk management.
12:00 - 12:35
QUALITATIVE AND HYBRID RISKS
Managing qualitative and hybrid risks in model lifecycle management
- Examining best practices for qualitative model risk management and adapting to evolving expectations.
- Ensuring governance frameworks align with qualitative risk management needs.
- Formulating strategies to manage and mitigate hybrid model risks throughout the lifecycle.
- Assessing the impact of changing expectations on qualitative and hybrid risk management practices.
12:35 - 13:35
Lunch break and networking
13:35 - 14:10
OVERFITTING IN AI/ML
Addressing Overfitting in AI/ML: Effective Detection, Mitigation, and Implementation Techniques
- Exploring the symptoms and root causes of overfitting in AI/ML models.
- Recognizing methods for identifying overfitting in model performance.
- Implementing approaches to effectively reduce and manage overfitting in AI/ML models.
- Applying practical steps to integrate detection and mitigation techniques into your model management processes.
- Reducing overfitting in AML models.
14:10 - 14:55
MODEL VALIDATION & UNCERTAINTY – PANEL DISCUSSION
Tackling Model Validation Challenges and Managing Uncertainty in Advanced Analytics
- Evaluating Covid's impact and incorporating 2020-2023 data into model validation.
- Integrating behavioral changes from the pandemic into validation processes.
- Designing inherently interpretable models and utilizing surrogate models.
- Implementing flexible testing and effective risk-tier categorization.
- Balancing validation with risk management for comprehensive control.
- Developing practical strategies for uncertainty management and performance monitoring.
- Managing interconnected portfolios and enhancing advanced risk measures.
14:55 - 15:25
Afternoon refreshments and networking
15:25 - 16:00
GEOPOLITICAL RISK IMPLICATIONS
Understanding and Managing Global Volatility Risks in MRM
- Managing data practices for consistent and accurate risk assessment, addressing stress testing gaps.
- Deploying proactive strategies for geopolitical risks using AI.
- Evaluating the impact of geopolitical events on financial sectors, including trade tensions and resource access.
- Overseeing MRM teams in multinational corporations: comparing EU and US approaches and regulatory impacts.
- Reviewing the impact of Basel III and IFRS 9 regulations on model development and validation.
- Strategizing to manage geopolitical risks impacting cross-border transactions and supply chain vulnerabilities.
16:00 - 16:45
AI RISK PRACTICE - PANEL DISCUSSION
AI Risk Management: Bridging Theory and Practice
- Measuring and assessing the specific risks associated with AI models.
- Applying theoretical concepts to real-world scenarios for effective AI risk management, with a focus on practical applications.
- Integrating technological solutions to ensure effective governance and reliability of AI models.
16:45 - 16:55
Chair’s closing remarks
16:55