Exercise 1: Control Effectiveness Analysis Prompt Engineering
Objective:
Learn to craft effective prompts for analyzing control effectiveness using data science techniques.
Background:
Risk & Control Data Scientists are responsible for analyzing the effectiveness of risk controls using data-driven approaches. A key challenge is developing methodologies to quantify control effectiveness and identify improvement opportunities.
Exercise:
1. Scenario:
You need to develop a data-driven approach to assess the effectiveness of payment fraud controls in a digital banking environment.
2. Basic Prompt Example:
How can I measure fraud control effectiveness?3. Prompt Improvement Activity:
- Identify the limitations of the basic prompt
- Add specific details about the payment fraud controls
- Include context about available data
- Request structured analysis methodology
- Ask for visualization and reporting recommendations
4. Advanced Prompt Template:
I am a Risk & Control Data Scientist at a [size] financial institution developing a data-driven approach to assess the effectiveness of our payment fraud controls in our digital banking environment.
Control environment:
- Control types: [rule-based alerts, ML models, authentication steps, etc.]
- Control points: [transaction initiation, authentication, post-transaction monitoring]
- Current metrics: [false positive rates, detection rates, manual review volumes]
- Recent changes: [new controls implemented, rule adjustments]
- Known challenges: [specific fraud types, customer friction points]
Available data:
- Transaction data: [volume, fields, time period]
- Alert/case data: [volume, disposition information]
- Fraud loss data: [confirmed fraud cases, recovery information]
- Customer data: [relevant customer attributes]
- Control activation logs: [when controls triggered/actions taken]
- Investigation notes: [unstructured data from fraud analysts]
Analysis objectives:
- Quantify effectiveness of individual controls and the overall control framework
- Identify control gaps and overlaps
- Measure false positive/negative rates
- Assess customer impact and friction points
- Identify opportunities for control optimization
- Develop ongoing monitoring metrics
Please help me develop a comprehensive control effectiveness analysis framework by:
1. Recommending key analytical approaches for:
- Control coverage assessment (gaps and overlaps)
- Control effectiveness measurement
- False positive/negative analysis
- Customer impact quantification
- Root cause analysis of control failures
- Control optimization opportunity identification
2. For each analytical approach, provide:
- Detailed methodology and calculation steps
- Required data elements and preparation
- Statistical techniques to employ
- Interpretation guidelines
- Common pitfalls and limitations
- Visualization recommendations
3. Suggesting specific metrics and KPIs for:
- Individual control effectiveness
- Overall control framework performance
- Customer experience impact
- Operational efficiency
- Cost-benefit analysis
- Comparative benchmarking
4. Recommending approaches for:
- Data integration across multiple sources
- Handling data quality issues
- Addressing class imbalance in fraud detection
- Incorporating feedback loops from investigations
- Developing predictive indicators of control degradation
- Creating executive and operational dashboards
Format your response as a comprehensive control effectiveness analysis framework that balances statistical rigor with practical business application.5. Evaluation Criteria:
- Does the prompt clearly describe the control environment and available data?
- Does it provide context about analysis objectives?
- Does it request specific analytical approaches with detailed methodologies?
- Does it ask for metrics and KPIs for different aspects of control effectiveness?
- Does it consider practical implementation challenges?
6. Practice Activity:
Create your own advanced prompt for control effectiveness analysis related to:
- AML transaction monitoring controls
- Cybersecurity defense controls
- Credit underwriting quality controls
Exercise 2: Anomaly Detection Prompt Engineering
Objective:
Develop skills to craft prompts that help design effective anomaly detection systems for risk and compliance.
Background:
Risk & Control Data Scientists must develop systems to detect unusual patterns that may indicate risk events or compliance issues. A key challenge is designing anomaly detection approaches that balance sensitivity with false positive rates.
Exercise:
1. Scenario:
You need to develop an anomaly detection system to identify unusual employee activities that may indicate internal fraud or policy violations.
2. Basic Prompt Example:
How can I detect employee fraud?3. Prompt Improvement Activity:
- Identify the limitations of the basic prompt
- Add specific details about employee activities and systems
- Include context about risk scenarios
- Request structured anomaly detection methodology
- Ask for implementation and alert management recommendations
4. Advanced Prompt Template:
I am a Risk & Control Data Scientist at a [size] financial institution developing an anomaly detection system to identify unusual employee activities that may indicate internal fraud or policy violations.
Activity environment:
- Employee types: [tellers, loan officers, back office, IT, etc.]
- Systems accessed: [core banking, CRM, loan origination, etc.]
- Normal activity patterns: [transaction volumes, timing, access patterns]
- Access controls: [authentication, authorization, segregation of duties]
- Known risk scenarios: [previous incidents or near-misses]
Available data:
- System access logs: [login/logout, failed attempts]
- Transaction data: [customer transactions by employee]
- HR data: [roles, reporting lines, tenure]
- Customer data: [relationships, account information]
- Physical access data: [badge swipes, location information]
- Policy exception data: [overrides, approvals]
Detection objectives:
- Identify unusual access patterns or privilege escalation
- Detect abnormal transaction patterns
- Identify potential segregation of duties violations
- Detect unusual timing or location of activities
- Identify suspicious patterns across multiple systems
- Balance detection rate with false positive management
Please help me develop a comprehensive anomaly detection framework by:
1. Recommending anomaly detection approaches for:
- User behavior analytics
- Transaction pattern analysis
- Peer group comparison
- Temporal pattern detection
- Network analysis of employee-customer relationships
- Multi-system activity correlation
2. For each detection approach, provide:
- Detailed methodology and algorithm selection
- Required data elements and preparation
- Parameter tuning considerations
- Baseline establishment approach
- Threshold setting methodology
- Performance evaluation metrics
3. Suggesting implementation strategies for:
- Alert generation and prioritization
- False positive reduction techniques
- Alert investigation workflow
- Feedback loop incorporation
- Model updating and maintenance
- Emerging pattern detection
4. Recommending approaches for:
- Privacy and ethics considerations
- Explainability of detection results
- Investigation support materials
- Governance and oversight
- Performance reporting
- Continuous improvement mechanisms
Format your response as a comprehensive anomaly detection framework that balances detection effectiveness with operational efficiency and ethical considerations.5. Evaluation Criteria:
- Does the prompt clearly describe the activity environment and available data?
- Does it provide context about detection objectives?
- Does it request specific anomaly detection approaches with detailed methodologies?
- Does it ask for implementation strategies and alert management?
- Does it consider privacy, ethics, and governance considerations?
6. Practice Activity:
Create your own advanced prompt for anomaly detection related to:
- Customer account takeover detection
- Trading activity surveillance
- Third-party payment processor monitoring
Exercise 3: Risk Monitoring Dashboard Prompt Engineering
Objective:
Learn to craft prompts that help design effective risk monitoring dashboards and reporting systems.
Background:
Risk & Control Data Scientists must develop dashboards and reporting systems that provide actionable risk insights. A key challenge is designing effective visualizations and metrics that communicate complex risk information to different stakeholders.
Exercise:
1. Scenario:
You need to design a comprehensive risk monitoring dashboard for operational risk that provides insights to multiple stakeholder groups.
2. Basic Prompt Example:
What should I include in a risk dashboard?3. Prompt Improvement Activity:
- Identify the limitations of the basic prompt
- Add specific details about operational risk monitoring needs
- Include context about different stakeholder requirements
- Request structured dashboard design methodology
- Ask for visualization and interaction recommendations
4. Advanced Prompt Template:
I am a Risk & Control Data Scientist at a [size] financial institution designing a comprehensive operational risk monitoring dashboard with these characteristics:
Risk monitoring scope:
- Risk categories: [process, people, systems, external events]
- Key risk indicators: [current KRIs being tracked]
- Control effectiveness metrics: [current monitoring approaches]
- Loss event data: [collection methodology, categorization]
- Issue management: [tracking, remediation, aging]
- Risk appetite: [thresholds, limits, escalation criteria]
Stakeholder profiles:
1. Board Risk Committee:
- Information needs: [strategic oversight, governance]
- Technical sophistication: [limited, high-level]
- Meeting frequency: [quarterly, monthly]
- Key concerns: [material risks, emerging issues]
2. Senior Management:
- Information needs: [tactical decisions, resource allocation]
- Technical sophistication: [moderate, business-focused]
- Review frequency: [weekly, monthly]
- Key concerns: [trends, hotspots, action items]
3. Risk Management Team:
- Information needs: [detailed analysis, root causes]
- Technical sophistication: [high, risk-focused]
- Review frequency: [daily, continuous]
- Key concerns: [specific metrics, control effectiveness]
4. Business Line Leaders:
- Information needs: [operational impacts, accountability]
- Technical sophistication: [varies by role]
- Review frequency: [weekly, monthly]
- Key concerns: [performance impact, resource needs]
Available data:
- Risk assessment results: [frequency, methodology]
- Control testing outcomes: [pass/fail rates, issues]
- Key risk indicators: [metrics, thresholds, trends]
- Loss event data: [internal losses, near misses]
- Issue tracking: [open issues, remediation status]
- External data: [industry events, benchmarks]
Please help me design a comprehensive risk monitoring dashboard framework by:
1. Recommending a multi-level dashboard approach with:
- Information architecture and hierarchy
- Navigation and drill-down capabilities
- Customization options for different users
- Mobile vs. desktop considerations
- Notification and alert mechanisms
- Export and reporting capabilities
2. For each stakeholder group, suggest:
- Key metrics and visualizations
- Appropriate level of detail
- Recommended data refresh frequency
- Action-oriented features
- Benchmark and context information
- Narrative elements and insights
3. For key risk areas, recommend:
- Effective visualization techniques
- Trend and pattern displays
- Threshold and limit indicators
- Correlation and relationship views
- Forecasting and predictive elements
- Root cause analysis capabilities
4. Suggesting implementation approaches for:
- Data integration and quality management
- Calculation engine and processing
- User access and security
- Maintenance and updating
- Performance optimization
- Training and adoption
Format your response as a comprehensive risk monitoring dashboard design framework that effectively communicates complex risk information to different stakeholders.5. Evaluation Criteria:
- Does the prompt clearly describe the risk monitoring scope and stakeholder profiles?
- Does it provide context about available data?
- Does it request a multi-level dashboard approach with stakeholder-specific recommendations?
- Does it ask for visualization techniques for different risk areas?
- Does it consider implementation and maintenance approaches?
6. Practice Activity:
Create your own advanced prompt for risk monitoring dashboard design related to:
- Credit risk portfolio monitoring
- Compliance risk and regulatory change management
- Cybersecurity risk monitoring