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Duration 14 hours
Course Outline
Introduction to WrenAI in Financial Analytics
- Core features and value propositions for finance teams
- Benchmarking against traditional BI tools
- Specific application scenarios in finance
Financial Data Modeling with WrenAI
- Defining KPIs to measure financial performance
- Standardizing metrics for FP and A and risk analysis
- Overseeing financial data sources
Regulatory and Audit Implications
- Compliance frameworks in financial reporting
- Designing dashboards for audit readiness
- Maintaining data integrity and traceability
Crafting Compliance-Centric Dashboards
- Developing dashboards for effective risk management
- Embedding compliance checks and contextual annotations
- Configuring user roles and access controls in financial dashboards
Integration with Financial Systems
- Linking WrenAI to ERP and accounting platforms
- Enabling real-time data updates for reporting
- Optimizing data refresh cycles
Advanced KPI Modeling Techniques
- Scenario-based financial modeling
- Forecasting using AI-enhanced metrics
- Conducting stress tests for risk evaluation
Monitoring and Governance
- Tracking usage and managing audit logs
- Streamlining regulatory reporting workflows
- Applying governance best practices in financial analytics
The Future of WrenAI in Financial Analytics
- Leveraging AI for regulatory reporting
- Navigating emerging compliance requirements
- Expanding the scope of WrenAI use cases in finance
Summary and Action Items
Requirements
- A solid grasp of financial reporting and KPI frameworks
- Prior experience with data analysis or BI tools
- Knowledge of regulatory compliance within the finance sector
Target Audience
- Finance analytics teams
- FP and A specialists
- Risk analysts
