Tuesday 4 March 2025
08:00 - 08:50
Registration, Coffee and Breakfast
08:50 - 09:00
Chair’s Opening Remarks
Day 1 Chair:
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 - 10:20
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
GOVERNANCE
Governance of AI Applications, including Gen and Model
- Building blocks of AI governance
- Defining a governance structure
- Building a robust inventory
- Risk assessment methods
- Testing for bias
- Validation approach
11:25 - 12:35
ENHANCING METHODS & PRACTICE - EXTENDED JOINT PRESENTATION
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
RISKS IN AI - HOW TO CONTROL YOUR ALGORITHMS
Effective AI MRM – Understanding and Managing Algorithms
- Ensuring strong engineering and effective AI
- Overcoming engineering challenges to ensure reliability
- Traditional MRM and AI MRM
- Use cases and practical examples
15:30 - 16:00
Afternoon refreshment break and networking
16:00 - 16:35
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
16:35 - 17:10
Implementing and validating GenAI: Strategies and lessons learned
- Enabling GenAI in customer-facing applications
- Effectively defining, implementing and validating
- Use cases for effective consistency and quality-control
- Common challenges faced and effective solutions
17:10 - 17:45
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
17:45 - 17:50
Chair’s closing remarks
17:50 - 17:55
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
Leveraging Technology to Automate and Strengthen the Model Risk Ecosystem
- Strategic insights: scaling MRM processes through advanced automation, especially in the GenAI era
- Industrializing model validation and testing for greater speed, efficiency, effectiveness, and stronger compliance guardrails
- Demonstrating how our MRM ecosystem — NIMBUS Uno, MoDeVa, and MRM Vault — is transforming model development, validation, testing, and governance
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 - 13:00
Lunch break and networking
13:00 - 13:35
STRENGTHENING MRM FRAMEWORKS
Navigating the Evolving Landscape of Regulatory Expectations and Industry Practices
- Identifying challenges and potential vulnerabilities within MRM frameworks.
- Insights into evolving regulatory expectations, model validation, and risk mitigation strategies.
- Key trends and emerging best practices shaping the future of MRM.
- Strategic approaches to strengthening MRM governance and resilience.
- Actionable takeaways to enhance MRM frameworks and ensure compliance.
13:35 - 14:20
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.
14:20 - 14:55
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
14:55 - 15:40
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.
15:40 - 15:50
Chair’s closing remarks
15:50 - 16:00