Training Courses
Financial Model Review in the AI Era
Error classes, testing technique and where AI-assisted review earns its place. As models grow more complex and AI plays a larger role in building them, the ability to systematically interrogate, validate and challenge outputs matters more than ever. Assess both the technical integrity and commercial logic of a model, and prioritise review effort under time pressure.
See dates & pricesHave a question? Talk to us- 2 daysPractitioner-led
- Hands-onYou review a real model,
not watch one being reviewed - In person or digitalDelivered in person
and worldwide online - Take awayA structured review
approach you can reuse
Who this is for
For anyone who relies on a model being right
Financial model review is a critical discipline in supporting investment decisions, transaction assurance and risk management. This course is designed for professionals who build, review, analyse or rely on financial models in transaction, investment, lending or advisory contexts.
Typical attendees are analysts, managers, senior managers and other professionals involved in model assurance, deal execution or decision making. No prior financial modelling course is required; the techniques apply to any model you are handed, not only ones built using our own methodology.
You will get the most from this course if
- You build, review, analyse or rely on financial models in transaction, investment, lending or advisory contexts
- You want a structured, efficient method for catching errors and assessing commercial reasonableness under time pressure
- You want to understand where AI genuinely helps review, and where it introduces new risks
- You need to review AI-assisted or AI-generated models, not only traditionally built ones
What you will learn
A structured method, applied to a real model
Seven modules take you from the principles of model review through mechanical, commercial and AI-assisted technique, to a full case-study review of a third-party transaction model.
Understanding model review and knowing where to start
Learning objective: understand the purpose of model review and how AI is changing model risk.
Activities and exercises:
- Discuss different forms of model review, and where to focus effort under time constraints
- Compare traditional and AI-assisted model development approaches, and the impact this has on model risk
Approaches to model review: when to do what
Learning objective: define alternative approaches to model review and understand when to use them.
Activities and exercises:
- Compare top-down, cell-by-cell and risk-based approaches, and review model structure, checks and calculation flow
- Hands-on exercises with audit tools such as Inquire and sparklines, and AI tools to aid your analytical review
Core mechanical review techniques
Learning objective: learn how to identify and investigate mechanical model errors.
Activities and exercises:
- Identify inconsistent formulas, hardcodes and spreadsheet errors
- Review formula consistency, anchoring and calculation design
- Learn how to interpret complex formulae and VBA code, using traditional and AI-assisted techniques
Commercial and analytical review
Learning objective: assess the commercial reasonableness of model outputs, assumptions and behaviours.
Activities and exercises:
- Review outputs, assumptions and key metrics for commercial reasonableness
- Learn how to apply benchmarks, ratios and industry knowledge to challenge model results
- Analyse key inputs, outputs, trends and value drivers, and learn how AI can extract and validate assumptions from supporting documentation and help manage model version control
- Use sensitivities, stress tests, scenarios, delta sheets and bridges to evaluate behaviour and movements
Troubleshooting unstable models
Learning objective: learn how to diagnose and resolve common model stability issues.
Activities and exercises:
- Diagnose balance sheet, cash flow, circularity, calculation and VBA issues
- Apply traditional and AI-assisted techniques to troubleshoot model instability
- Improve model stability, performance, file size and calculation speed
- Discuss common causes of model instability and practical remediation techniques
Reviewing AI-assisted models
Learning objective: understand the risks and limitations associated with AI-generated and AI-assisted models.
Activities and exercises:
- Identify common weaknesses in AI-generated and AI-assisted models
- Distinguish presentation quality from underlying technical quality
- Review examples of AI-generated models and identify potential weaknesses
- Discuss where AI-generated outputs require additional validation and challenge, and compare AI-generated solutions with traditional modelling approaches
Case study application
Learning objective: bring together the techniques covered during the course in a realistic model review scenario.
Activities and exercises:
- Review a third-party transaction model using mechanical, analytical, commercial and AI-assisted techniques
- Identify, classify and prioritise findings according to significance and risk
- Discuss which review approaches identify different types of issues, and translate findings into practical recommendations to improve model quality
- Present findings to the class and discuss conclusions, recommendations and lessons learned
Ready to see it on the calendar? Course dates, cities, formats and prices are all on the live schedule.
How the course runs
You learn modelling by modelling
Interactive, instructor-led and built on a single modelling standard that has been taught to more than 20,000 people. You build the model yourself with individual coaching through every exercise, and you keep the completed files.
This course also runs privately: see in-house training for delivery built around your team and sector, or browse the full course portfolio.
Book this course
Dates, prices and online booking are on the course schedule. Not sure it is the right course? Ask us.
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