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Duration 14 hours
Course Outline
Introduction to WrenAI for Financial Analytics
- Key features and benefits relevant to finance teams
- Comparison with conventional BI tools
- Finance-specific use cases
Financial Data Modelling in WrenAI
- Defining KPIs for financial performance
- Standardised metrics for FP and A and risk analysis
- Managing financial data sources
Regulatory and Audit Considerations
- Compliance frameworks in financial reporting
- Designing audit-friendly dashboards
- Safeguarding data integrity and traceability
Creating Regulatory-Aware Dashboards
- Building dashboards focused on risk management
- Incorporating compliance checks and annotations
- Managing user roles and access control in finance dashboards
Integration with Finance Systems
- Connecting WrenAI to ERP and accounting platforms
- Enabling real-time data updates for financial reporting
- Managing data refresh cycles
Advanced KPI Modelling
- Scenario-based financial modelling
- Forecasting using AI-driven metrics
- Stress testing for risk assessment
Monitoring and Governance
- Usage tracking and audit logging
- Workflows for regulatory reporting
- Best practices for governance in financial analytics
The Future of WrenAI in Financial Analytics
- AI-assisted regulatory reporting
- Emerging compliance requirements
- Expanding financial use cases for WrenAI
Summary and Next Steps
Requirements
- Foundational knowledge of financial reporting and KPIs
- Practical experience with data analysis or BI tools
- Understanding of regulatory compliance within the finance sector
Target Audience
- Finance analytics teams
- FP and A professionals
- Risk analysts
