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Duration 14 hours
Course Outline
Introduction to WrenAI in Financial Analytics
- Core features and advantages for finance teams
- Comparison against traditional BI tools
- Finance-specific application scenarios
Financial Data Modeling with WrenAI
- Defining KPIs for financial performance
- Standardized metrics for FP&A and risk analysis
- Managing financial data sources
Regulatory and Audit Considerations
- Compliance frameworks in financial reporting
- Designing audit-friendly dashboards
- Maintaining data integrity and traceability
Creating Regulatory-Aware Dashboards
- Building dashboards for risk management
- Integrating compliance checks and annotations
- Configuring user roles and access controls for finance dashboards
Integration with Finance Systems
- Linking WrenAI to ERP and accounting platforms
- Enabling real-time data updates for financial reporting
- Managing data refresh cycles
Advanced KPI Modeling
- Scenario-based financial modeling
- Forecasting using AI-driven metrics
- Stress testing for risk assessment
Monitoring and Governance
- Usage tracking and audit logging
- Regulatory reporting workflows
- Best practices for governance in financial analytics
The Future of WrenAI in Financial Analytics
- AI-assisted regulatory reporting
- Emerging compliance requirements
- Expanding financial use cases for WrenAI
Summary and Next Steps
Requirements
- Familiarity with financial reporting and KPIs
- Practical experience with data analysis or BI tools
- Knowledge of regulatory compliance within the finance sector
Target Audience
- Finance analytics teams
- FP&A professionals
- Risk analysts
