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Duration 14 hours
Course Outline
Introduction to WrenAI in Financial Analytics
- Core features and key benefits for finance teams
- Comparison with conventional BI tools
- Specific use cases within the finance sector
Financial Data Modeling with WrenAI
- Defining KPIs for financial performance tracking
- Standardizing metrics for FP&A and risk analysis
- Management of financial data sources
Regulatory and Audit Considerations
- Overview of compliance frameworks in financial reporting
- Designing dashboards that are audit-friendly
- Safeguarding data integrity and ensuring traceability
Creating Regulatory-Aware Dashboards
- Building dashboards tailored for risk management
- Integrating compliance checks and descriptive annotations
- Configuring user roles and access controls for finance dashboards
Integration with Finance Systems
- Connecting WrenAI to ERP and accounting platforms
- Enabling real-time data updates for financial reports
- Managing data refresh cycles effectively
Advanced KPI Modeling Techniques
- Scenario-based financial modeling
- Forecasting using AI-driven metrics
- Stress testing for comprehensive risk assessment
Monitoring and Governance
- Tracking usage and maintaining audit logs
- Workflow for regulatory reporting
- Best practices for governance in financial analytics
The Future of WrenAI in Financial Analytics
- AI-assisted regulatory reporting capabilities
- Emerging compliance requirements
- Expanding financial use cases for WrenAI
Summary and Recommended Next Steps
Requirements
- Fundamental understanding of financial reporting principles and KPIs
- Practical experience utilizing data analysis or BI tools
- Knowledge of regulatory compliance standards within the financial sector
Target Audience
- Finance analytics teams
- Financial Planning and Analysis (FP&A) professionals
- Risk analysts
