This course equips finance, strategy, and risk professionals with AI-enhanced scenario planning and sensitivity analysis techniques. Leveraging LLMs for scenario storytelling, Monte Carlo simulations, and advanced feature sensitivity methods, delegates will build robust decision frameworks. By integrating external datasets and generative model insights, participants will construct dynamic scenarios, quantify risk impacts, and present compelling outcomes for executive audiences.

TRAINING TOPICS

• Strategic scenario design with generative AI (GPT‑4, Claude)

• Monte Carlo simulation and probabilistic outcomes

• Sensitivity analysis using SHAP and Sobol indices

• Scenario narratives: AI-generated financial storytelling

• Feature importance & driver decomposition

• Stress testing with macro‑economic and ESG data

• Scenario automation: pipelines & dynamic triggers

• Visualization of scenario impacts in dashboards

• Scenario calibration and validation frameworks

• Governance: scenario auditability and oversight

BY THE END OF THIS TRAINING COURSE, DELEGATES WILL BE ABLE TO

• Design scenario frameworks enhanced by AI storytelling

• Build Monte Carlo engines to quantify scenario uncertainty

• Apply sensitivity metrics to identify key financial drivers

• Generate executive‑ready scenario narratives using LLMs

• Integrate external data (macro, ESG) into scenario models

• Automate scenario generation pipelines with AI triggers

• Visualize scenario outcomes and sensitivities in BI tools

• Establish governance and audit mechanisms for scenario use

TRAINING IS TAILORED TO

• FP&A and strategic planners

• Risk and scenario analysts

• Corporate strategy and treasury teams

• Data scientists in finance strategy roles

• Consulting professionals in financial modeling

• Finance transformation and automation leads

TRAINING METHODOLOGY

• Instructor‑led AI scenario and modeling walkthroughs

• Scoping exercises for scenario frameworks

• LLM workshops for narrative generation

• Monte Carlo and sensitivity coding labs

• Dashboarding and interactive visualization practice

• Peer analysis of scenario assumptions and impacts

• Governance checklist and audit simulation exercises

Day One: SCENARIO FRAMEWORKS & AI STORY DESIGN

• Defining scenario axes and business logic

• LLM‑prompt engineering for scenario narratives

• Lab: generate three strategic scenarios using GPT

• Use‑case: competitive disruption scenario

Day Two: MONTE CARLO ENGINE DEVELOPMENT

• Monte Carlo fundamentals and sampling methods

• Lab: code probabilistic cash flow models in Python

• Scenario: volatility modeling in revenue forecasts

• Evaluation: scenario range and confidence analysis

Day Three: SENSITIVITY ANALYSIS & DRIVER IDENTIFICATION

• Sensitivity techniques: SHAP, Sobol, partial derivatives

• Lab: apply SHAP to Monte Carlo model drivers

• Scenario: isolate cost drivers under uncertainty

• Review: ranking and prioritizing influence factors

Day Four: SCENARIO NARRATIVES & DASHBOARDING

• Structuring storyline outputs with LLMs

• Lab: generate CFO narrative for base and stress cases

• Dashboarding: visualize scenario outputs and sensitivities

• Peer session: critique narrative clarity and decision relevance

Day Five: AUTOMATION, MONITORING & GOVERNANCE

• Scenario pipeline orchestration (Airflow, cron, API)

• Lab: automate scenario generation and alert triggers

• Governance: logging, audit trails, validation routines

• Final group presentations: scenario roadmap & best practices