Financial Modelling with Excel and AI

Categories: Excel, Financial Modelling
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About Course

The FM 2026 | Financial Modelling masterclass is a high-intensity, “hands-on” programme designed to move you from just using Excel to building professional, decision-grade financial models. This isn’t a lecture where you just sit and look; it is a practical “engine room” experience where you turn business assumptions into fully integrated three-statement models. Using the Henderson Manufacturing case study, you will master the FAST Standard (Flexible, Appropriate, Structured, Transparent) to ensure your work is clean, auditable, and ready for any boardroom or investor

What Will You Learn?

  • Modeling revenue for four key Industries.
  • Leveraging Al in financial modeling.
  • Understand how to model the primary 3 financial statement.
  • Practice financial modeling with real life scenarios up to 5 cases.
  • Learn to model other key FS items like borrowings, working capital, fixed asset, revenue & operating expense.

Course Content

Foundations of Financial Modelling

  • Introduction to FM training
    13:21
  • Q&A for Participants
    04:03
  • Objective for Foundation in FM
    02:42
  • What is a FM?
    19:10
  • Q&A Checkpoint for participants
    15:13
  • Why we build financial models
    12:07
  • What each Industry leans on in financial models
    14:11
  • Q&A Checkpoint for participants
    02:20
  • Skillsets needed to build a financial model
    07:47
  • How is a model built
    07:41
  • Q&A Checkpoint
    02:20
  • Q&A from participants
    12:39
  • Building model inline with FAST
    14:32
  • Q&A Checkpoint for participants
    03:04
  • Dos and Donts in build financial models
    09:08
  • Q&A from participants
    14:08
  • Audit before you trust the model
    10:19
  • Demo of AI Power
    08:18
  • Q&A Checkpoint for participants
    11:16
  • Intro Excel for FM – Shortcuts in Excel
    10:10

Excel for Financial Modelling I

Excel for Financial Modelling II

Modeling Components I

Modeling Components II

Modeling Components III

Modeling Components IV

Case Study I

Case Study II

Case Study III

Case Study IV

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