Financial Planning

AI-driven financial planning automates forecasting and scenario analysis for better decisions

Business impact

• Forecast accuracy — AI improves prediction precision by analyzing historical and real-time data
• Planning cycle time — Automation reduces time spent on manual data consolidation and modeling
• Decision-making speed — Real-time insights enable faster, better-informed financial decisions
• Operational efficiency — Streamlined workflows reduce errors and free finance teams for strategic work
• Client satisfaction — Personalized financial advice and faster responses enhance client experience

Data requirements

• ERP and financial systems (Structured) — Provide structured transactional and accounting data for analysis
• Historical sales and market data (Numeric) — Used for trend analysis and demand forecasting
• Adviser-client interaction notes (Text) — Text data analyzed to extract client needs and preferences
• Real-time inventory and pricing data (Numeric) — Supports dynamic pricing and supply chain financial planning

AI methods and techniques

• Predictive AI — Forecasts financial outcomes and market trends using historical and real-time data
• Generative AI — Generates scenario analyses, summaries, and conversational insights for planners
• Agentic AI — Automates planning workflows and interacts with users to provide recommendations

AI models and model families

GPT-4o, Claude, ChatGPT, SensibleAI, Microsoft Copilot

Industries

Real-world evidence

15 documented case studies on record.

Companies using this: Amazon, BMO, Bill Holdings, Chilton Capital Management, Delta, Eaton, Edward Jones, First Internet Bank, Kiabi, Netflix, Northwestern Mutual, RBC Wealth Management U S, Tesla, Vena, Walmart.