Exercise 1: Model Validation Framework Prompt Engineering
Objective:
Learn to craft effective prompts for GenAI to assist with developing comprehensive model validation frameworks.
Background:
Model Risk Managers are responsible for ensuring that models used in financial institutions are properly validated and monitored. A key challenge is developing robust validation frameworks that address all aspects of model risk.
Exercise:
1. Scenario:
You need to develop or enhance your institution's model validation framework to ensure it meets regulatory expectations and industry best practices.
2. Basic Prompt Example:
What should be included in a model validation framework?3. Prompt Improvement Activity:
- Identify the limitations of the basic prompt (lack of specificity, context, and structure)
- Add details about your institution's model landscape and regulatory requirements
- Include information about validation objectives and stakeholders
- Request a structured framework with specific validation components and processes
4. Advanced Prompt Template:
I am a Model Risk Manager at a [size/type] financial institution developing a comprehensive model validation framework. Our model landscape includes:
- Model inventory: Approximately [number] models across [categories]
- Model complexity: [range from simple to highly complex]
- Critical models: [number] models classified as high-risk/critical
- Model uses: [credit decisioning, pricing, stress testing, AML, etc.]
- Modeling techniques: [statistical, machine learning, AI, etc.]
- Regulatory requirements: [SR 11-7, OCC 2011-12, TRIM, etc.]
The objectives of our model validation framework are to:
- Ensure models are conceptually sound and performing as intended
- Identify and mitigate model limitations and weaknesses
- Meet regulatory expectations for model risk management
- Establish consistent validation standards across the organization
- Support effective challenge of model development and assumptions
- Provide clear guidance for model owners and validators
Please help me develop a comprehensive model validation framework by:
1. Outlining the key components of an effective framework:
- Governance structure and roles/responsibilities
- Model risk tiering methodology
- Validation scope and depth requirements by tier
- Independence requirements for validators
- Documentation standards
- Validation lifecycle management
- Validation tools and techniques
2. For each validation component, provide detailed guidance on:
- Conceptual soundness assessment
- Data quality and representativeness review
- Implementation verification
- Outcomes analysis and benchmarking
- Ongoing monitoring requirements
- Periodic review standards
- Issue management and remediation
3. Recommending specific validation approaches for:
- Traditional statistical models
- Machine learning models
- Vendor models
- Models using alternative data
- Models with limited data
- Qualitative models and frameworks
4. Suggesting a validation reporting framework:
- Executive summary components
- Technical assessment details
- Findings classification methodology
- Remediation planning and tracking
- Effective communication to stakeholders
- Regulatory documentation requirements
Format your response as a structured model validation framework that I can present to our Model Risk Committee and use to guide our validation program.5. Evaluation Criteria:
- Does the prompt clearly describe the institution's model landscape and regulatory context?
- Does it articulate the objectives of the validation framework?
- Does it request comprehensive components covering all aspects of validation?
- Does it ask for specific approaches for different model types?
6. Practice Activity:
Create your own advanced prompt for model validation framework development related to:
- AI/ML models with specific focus on explainability
- Vendor models with limited transparency
- Models used for regulatory stress testing
Exercise 2: Model Risk Assessment Prompt Engineering
Objective:
Develop skills to craft prompts that help create effective model risk assessment methodologies.
Background:
Model Risk Managers must assess and quantify risks associated with models to prioritize validation efforts and mitigation strategies. A key challenge is developing a comprehensive risk assessment approach that considers all relevant risk factors.
Exercise:
1. Scenario:
You need to develop or enhance your institution's model risk assessment methodology to better identify, measure, and prioritize model risks.
2. Basic Prompt Example:
How do we assess model risk?3. Prompt Improvement Activity:
- Identify the weaknesses in the basic prompt
- Add specificity about your model ecosystem and risk factors
- Include context about stakeholder expectations and constraints
- Request a structured methodology with specific assessment criteria and processes
4. Advanced Prompt Template:
I am a Model Risk Manager at a [size/type] financial institution developing/enhancing our model risk assessment methodology. Our current situation includes:
Model ecosystem:
- Types of models: [credit, market, operational, financial crime, etc.]
- Model complexity spectrum: [rule-based to advanced AI/ML]
- Model uses: [decision-making, reporting, forecasting, etc.]
- Development sources: [internal, vendor, hybrid approaches]
- Model interdependencies: [model chains, shared data sources]
Risk considerations:
- Financial impact of model errors
- Regulatory compliance requirements
- Reputational risk factors
- Operational dependencies
- Model complexity and opacity
- Data quality and availability
- Implementation and use controls
Current challenges:
- [e.g., Inconsistent risk assessment across model types]
- [e.g., Difficulty quantifying certain risk dimensions]
- [e.g., Balancing depth of assessment with resource constraints]
- [e.g., Addressing emerging risks from new modeling techniques]
Please help me develop a comprehensive model risk assessment methodology by:
1. Outlining a structured risk assessment framework:
- Key risk dimensions to evaluate
- Quantitative and qualitative assessment approaches
- Scoring methodology and scales
- Risk tiering and classification approach
- Assessment frequency and triggers
- Roles and responsibilities in the assessment process
2. For each risk dimension, suggest:
- Specific assessment criteria
- Measurement approaches and metrics
- Information sources and evidence requirements
- Common pitfalls and how to avoid them
- Benchmarking considerations
3. Recommending tailored assessment approaches for:
- Different model types (statistical, ML/AI, qualitative)
- Different model uses (decisioning, reporting, forecasting)
- Different development sources (internal, vendor, hybrid)
- Models with varying levels of complexity and materiality
- New models vs. existing models
4. Providing implementation guidance:
- Assessment tools and templates
- Documentation requirements
- Governance and approval processes
- Integration with model inventory
- Reporting and escalation procedures
- Continuous improvement mechanisms
Format your response as a structured model risk assessment methodology that balances comprehensiveness with practical implementation considerations.5. Evaluation Criteria:
- Does the prompt clearly describe the model ecosystem and risk considerations?
- Does it articulate current challenges and constraints?
- Does it request a comprehensive framework with specific assessment criteria?
- Does it ask for tailored approaches for different model types and uses?
6. Practice Activity:
Create your own advanced prompt for model risk assessment related to:
- AI/ML models with specific focus on ethical considerations
- Models used for consumer-facing decisions
- Models with significant data limitations
Exercise 3: Model Monitoring and Performance Tracking Prompt Engineering
Objective:
Learn to craft prompts that help design effective model monitoring and performance tracking frameworks.
Background:
Model Risk Managers must establish robust monitoring mechanisms to ensure models continue to perform as expected over time. A key challenge is designing monitoring approaches that detect performance deterioration and emerging issues promptly.
Exercise:
1. Scenario:
You need to enhance your institution's model monitoring and performance tracking framework to improve ongoing oversight of model performance.
2. Basic Prompt Example:
How should we monitor our models?3. Prompt Improvement Activity:
- Identify the limitations of the basic prompt
- Add specificity about your model types and performance concerns
- Include context about monitoring objectives and constraints
- Request a comprehensive monitoring framework with specific metrics and processes
4. Advanced Prompt Template:
I am a Model Risk Manager at a [size/type] financial institution enhancing our model monitoring and performance tracking framework. Our current situation includes:
Model portfolio:
- Credit risk models (scoring, PD/LGD/EAD, CECL, stress testing)
- Market risk models (VaR, pricing, hedging)
- Operational risk models
- AML/fraud detection models
- Behavioral models (prepayment, deposit pricing)
- [Other relevant model types]
Monitoring challenges:
- [e.g., Varying data availability across model types]
- [e.g., Determining appropriate monitoring frequency]
- [e.g., Setting meaningful thresholds for investigation]
- [e.g., Addressing seasonal patterns and regime shifts]
- [e.g., Resource constraints for monitoring activities]
Monitoring objectives:
- Detect model performance deterioration promptly
- Identify data drift and population shifts
- Ensure continued alignment with business objectives
- Support regulatory compliance requirements
- Optimize model update and recalibration cycles
- Provide early warning of emerging issues
Please help me design a comprehensive model monitoring and performance tracking framework by:
1. Outlining key components of an effective monitoring program:
- Monitoring governance and oversight
- Performance metric selection methodology
- Threshold setting approaches
- Monitoring frequency determination
- Escalation and response protocols
- Documentation and reporting standards
2. For each major model type, recommend:
- Specific performance metrics and indicators
- Data requirements and sources
- Appropriate monitoring frequency
- Threshold setting considerations
- Benchmark and challenger approaches
- Visualization and reporting techniques
3. Suggesting specialized monitoring approaches for:
- Machine learning and AI models
- Models with limited performance data
- Models subject to significant regime changes
- Vendor models with black-box components
- Models with complex interdependencies
- Models with high regulatory scrutiny
4. Providing implementation guidance on:
- Automation opportunities and tools
- Resource allocation and prioritization
- Integration with model inventory systems
- Roles and responsibilities
- Regulatory documentation requirements
- Continuous improvement processes
Format your response as a comprehensive model monitoring framework that balances rigor with practical implementation considerations and resource constraints.5. Evaluation Criteria:
- Does the prompt clearly describe the model portfolio and monitoring challenges?
- Does it articulate monitoring objectives and constraints?
- Does it request specific monitoring approaches for different model types?
- Does it ask for practical implementation guidance and resource considerations?
6. Practice Activity:
Create your own advanced prompt for model monitoring related to:
- Credit models during economic uncertainty
- AI/ML models with potential for algorithmic bias
- Models with complex interdependencies