Description

This training is designed for managers in finance, audit, and management control roles who want to understand and leverage generative artificial intelligence in their work. It provides a clear overview of AI’s capabilities to automate analyses, optimize financial processes, and improve decision quality. Through concrete examples and practical workshops, participants will learn how to integrate generative AI while respecting ethical and regulatory constraints specific to the financial sector.

Who is this training for ?

For whom ?

• Managers and leaders in finance, audit, and management control functions.
• Financial analysts and auditors seeking to familiarize themselves with generative AI.
• Anyone involved in the digital transformation of financial professions.

Prerequisites

• Knowledge of finance, audit, or management control roles and processes.
• No advanced technical AI skills required.

Training objectives

Understand the fundamentals of generative AI applied to finance, audit, and management control roles

Identify key use cases for automating analytical tasks, report generation, and anomaly detection

Support the deployment of AI solutions ensuring compliance and risk management

Training program

• Fundamentals of Generative AI, Security & Ethics
  • Introduction to generative AI applied to finance, audit, and control.
  • How language models work (LLMs, fine-tuning, specialized agents).
  • Differences between analytical AI, generative AI, and financial automation.
  • Risks: hallucinations, data manipulation, unverifiability.
  • Compliance challenges: GDPR, auditability, proof retention.
  • Ethical principles specific to finance, audit, and control functions.
  • Examples of use cases in reporting, auditing, closing, forecasting.
  • Deliverables: Document on finance/audit AI use cases; Risk control plan for AI deployment; Simplified AI charter co-created with teams; GDPR & data security factsheet tailored to financial data.
• Prompt Engineering (ChatGPT, Gemini, Claude…)
  • Basics of prompt engineering for finance and audit tasks.
  • Structure of an effective prompt (context, instructions, expected output).
  • Practical examples: Monthly budget reporting; Quarterly forecasting; Variance and cost analysis; Reviewing control procedures.
  • Testing prompt variations to improve accuracy
  • Mini workflows: from prompt to automated output
  • Validating responses for coherence, accuracy, and traceability
  • Deliverables: Set of 5 finance/audit/control prompts; Best practices guide for prompt writing; Mini automated workflow (file + diagram); Comparison table of generated responses (prompt A vs B).
• AI Toolbox & Knowledge Management
  • Selection of AI tools relevant for financial teams (Notion AI, Excel Copilot, ChatGPT, Power BI + GPT, MindMeister, etc.).
  • Using AI to synthesize, document, and structure knowledge.
  • Setting up a collaborative space for prompts, tools, and best practices
  • Visual models: mind maps, validation diagrams, audit checklists
  • Deliverables: Annotated directory of AI/no-code tools relevant to finance; Mind map templates (audit, internal control, financial analysis); Explanatory diagrams (validation or information flow chains); Internal rollout plan for tools/prompts (via Notion, Teams, etc.).
• Creating Agents & Automating Financial Workflows
  • Identifying repetitive tasks for automation: Invoice data extraction; Budget threshold monitoring;Preparing periodic reports.
  • Building simple AI agents (e.g., closing assistant, data extractor).
  • Orchestrating actions with no-code tools (Make, Zapier, Power Automate…).
  • Testing scenarios: invoices, alerts, automatic controls.
  • Visualizing automated workflows.
  • Key considerations: human validation, critical thresholds, logging.
  • Deliverables: AI agent prototype (data extraction or reporting assistant); Automated process diagram (from data input to report); Documented test scenarios with validation criteria; Checklist for phased deployment of automated workflows.