Financial Modeling

AI-supported budgeting, forecasting, and scenario modeling in finance teams.

Business impact

• Forecast cycle time — The article says AI and machine learning slash the time it takes to plan, report and forecast.
• Forecast frequency — Bergzeit's finance team now forecasts multiple times a year.
• Forecast accuracy — Bergzeit forecasts faster and more accurately after moving to the AI-enhanced tool.
• Decision speed — TX Group uses instant self-service access in plain English for faster strategic decisions.

Data requirements

• Financial planning data (tabular) — Used for budgets, forecasts, and scenario models.
• ERP/GL data (tabular) — Provides accounting and ledger context for planning.
• CRM data (tabular) — Feeds planning and scenario analysis when connected.
• HCM data (tabular) — Feeds planning and scenario analysis when connected.
• Transactional data (tabular) — Used for drill-downs to identify variance root causes.
• Narrative prompts and questions (text) — Users query the system in plain English.

AI methods and techniques

• Machine learning — Used in FP&A to automate planning work and improve forecasting.
• Predictive forecasting — Builds forecasts from internal and external data.
• Scenario modeling — Supports what-if analysis and stress-testing of assumptions.
• Natural language query — Lets users ask questions in plain English and get answers from connected data.
• Automated variance analysis — Summarises variances in plain language and helps users move from insight to action.

Industries

Real-world evidence

2 documented case studies on record.

Companies using this: Bergzeit, TX Group.