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Course Outline
Introduction to WrenAI in Financial Analytics
- Core features and advantages for finance teams
- Comparison against traditional BI tools
- Specific use cases within finance
Financial Data Modeling with WrenAI
- Defining KPIs to measure financial performance
- Standardized metrics for FP and A and risk analysis
- Managing various financial data sources
Regulatory and Audit Considerations
- Compliance frameworks in financial reporting
- Designing dashboards that are audit-friendly
- Maintaining data integrity and traceability
Creating Regulatory-Aware Dashboards
- Developing dashboards focused on risk management
- Embedding compliance checks and annotations
- Managing user roles and access controls in finance dashboards
Integration with Finance Systems
- Connecting WrenAI to ERP and accounting platforms
- Enabling real-time data updates for reporting
- Optimizing data refresh cycles
Advanced KPI Modeling
- Scenario-based financial modeling
- AI-driven metrics for forecasting
- Conducting stress tests for risk assessment
Monitoring and Governance
- Tracking usage and maintaining audit logs
- Streamlining regulatory reporting workflows
- Adopting governance best practices in financial analytics
The Future of WrenAI in Financial Analytics
- AI-assisted regulatory reporting
- Navigating emerging compliance requirements
- Expanding the scope of financial use cases for WrenAI
Conclusion and Next Steps
Requirements
- Fundamental understanding of financial reporting and KPIs
- Practical experience with data analysis or BI tools
- Knowledge of regulatory compliance in the finance sector
Target Audience
- Finance analytics teams
- FP and A professionals
- Risk analysts
14 Hours
