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Duration 14 hours
Course Outline
Introduction to WrenAI for Financial Analytics
- Core features and advantages for finance teams
- Comparison against conventional BI tools
- Specific use cases in the finance sector
Financial Data Modeling in WrenAI
- Establishing KPIs for financial performance
- Standardized metrics for FP and A and risk analysis
- Administration of financial data sources
Regulatory and Audit Considerations
- Compliance frameworks within financial reporting
- Dashboard design optimized for audits
- Maintaining data integrity and traceability
Creating Regulatory-Aware Dashboards
- Constructing dashboards for risk management purposes
- Embedding compliance checks and annotations
- User roles and access controls in financial dashboards
Integration with Finance Systems
- Connecting WrenAI to ERP and accounting platforms
- Real-time data synchronization for financial reporting
- Managing data refresh cycles
Advanced KPI Modeling
- Scenario-based financial modeling
- Forecasting using AI-driven metrics
- Stress testing for risk evaluation
Monitoring and Governance
- Usage tracking and audit log management
- Workflows for regulatory reporting
- Best practices for governance in financial analytics
The Future of WrenAI in Financial Analytics
- AI-assisted regulatory reporting
- Evolving compliance requirements
- Expanding the scope of financial use cases for WrenAI
Summary and Next Steps
Requirements
- A solid grasp of financial reporting structures and KPIs
- Hands-on experience with data analysis or BI platforms
- Knowledge of regulatory compliance standards in the finance sector
Target Audience
- Finance analytics teams
- FP and A specialists
- Risk analysts
