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Duration 14 hours
Course Outline
Introduction to WrenAI for Financial Analytics
- Key functionalities and advantages for finance teams
- Comparison with conventional BI tools
- Finance-specific application scenarios
Financial Data Modeling in WrenAI
- Defining KPIs for financial performance
- Standardizing metrics for FP and A and risk analysis
- Managing and organizing financial data sources
Regulatory and Audit Considerations
- Compliance frameworks in financial reporting
- Designing audit-friendly dashboards
- Safeguarding data integrity and traceability
Designing Regulatory-Aware Dashboards
- Constructing dashboards for risk management purposes
- Incorporating compliance checks and annotations
- Managing user roles and access controls in finance dashboards
Integration with Finance Systems
- Linking WrenAI to ERP and accounting platforms
- Enabling real-time data updates for financial reporting
- Optimizing data refresh cycles
Advanced KPI Modeling
- Scenario-based financial modeling techniques
- Forecasting utilizing AI-driven metrics
- Conducting stress tests for risk assessment
Monitoring and Governance
- Tracking usage and maintaining audit logs
- Streamlining regulatory reporting workflows
- Best practices for governance in financial analytics
Future of WrenAI in Financial Analytics
- AI-assisted regulatory reporting capabilities
- Emerging compliance requirements
- Expanding the scope of financial use cases for WrenAI
Summary and Next Steps
Requirements
- A solid grasp of financial reporting concepts and KPI frameworks.
- Prior experience utilizing data analysis or Business Intelligence (BI) tools.
- Knowledge of regulatory compliance standards within the financial sector.
Target Audience
- Finance analytics teams.
- FP and A professionals.
- Risk analysts.
