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Duration 14 hours
Course Outline
Introduction to WrenAI for Financial Analytics
- Key features and advantages for finance teams
- Comparative analysis with traditional BI tools
- Specific use cases in the finance sector
Financial Data Modeling in WrenAI
- Defining KPIs for financial performance tracking
- Standardized metrics for FP&A and risk analysis
- Managing diverse financial data sources
Regulatory and Audit Considerations
- Compliance frameworks applicable to financial reporting
- Designing dashboards with an audit-friendly approach
- Safeguarding data integrity and ensuring traceability
Designing Regulatory-Aware Dashboards
- Constructing dashboards focused on risk management
- Incorporating compliance checks and explanatory annotations
- Implementing user roles and access controls in finance dashboards
Integration with Finance Systems
- Linking WrenAI with 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 using AI-driven metrics
- Conducting stress tests for risk assessment
Monitoring and Governance
- Tracking usage and managing audit logs
- Streamlining regulatory reporting workflows
- Best practices for governance in financial analytics
The Future of WrenAI in Financial Analytics
- AI-assisted regulatory reporting capabilities
- Addressing emerging compliance requirements
- Expanding financial use cases within WrenAI
Summary and Next Steps
Requirements
- A solid grasp of financial reporting standards and KPI definitions.
- Practical experience with data analysis or BI tools.
- Knowledge of regulatory compliance practices within the finance sector.
Target Audience
- Finance analytics teams.
- FP&A professionals.
- Risk analysts.
