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Duration 14 hours
Course Outline
Introduction to WrenAI for Financial Analytics
- Core features and benefits specific to finance teams
- Comparison of WrenAI with traditional BI tools
- Key finance-specific use cases
Financial Data Modeling in WrenAI
- Defining KPIs to measure financial performance
- Establishing standardized metrics for FP&A and risk analysis
- Managing and organizing financial data sources
Regulatory and Audit Considerations
- Overview of compliance frameworks in financial reporting
- Designing dashboards with audit-readiness in mind
- Ensuring data integrity and full traceability
Designing Regulatory-Aware Dashboards
- Creating dashboards tailored for risk management
- Incorporating compliance checks and contextual annotations
- Implementing user roles and access controls in financial dashboards
Integration with Finance Systems
- Connecting 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 using AI-driven metrics
- Conducting stress testing 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 approaches to regulatory reporting
- Addressing emerging compliance requirements
- Exploring new financial use cases for WrenAI
Summary and Next Steps
Requirements
- A solid understanding of financial reporting and KPI definitions.
- Practical experience with data analysis or BI tools.
- Familiarity with regulatory compliance standards in the finance sector.
Target Audience
- Finance analytics teams
- FP&A professionals
- Risk analysts
