Financial Modeling Through Expert-Led Training
Our instructors bring decades of practical experience from investment banking, corporate finance, and advisory roles. They're not just teaching theory—they're sharing frameworks they've built under pressure, models they've defended in boardrooms, and techniques that actually hold up when the stakes matter.
Learn from Practitioners Who've Done This Work
We handpicked instructors based on one criterion: they've built financial models that influenced real decisions involving real money. That matters more than any credential.
Piers Alderton
Former M&A Director
Spent twelve years building valuation models for transactions ranging from M to 0M. His approach cuts through complexity—he'll show you how to structure models that executives can understand in fifteen minutes, because that's usually all you get.
Rhiannon Thistlewood
Corporate Finance Specialist
Developed financial planning frameworks for ASX-listed companies going through growth phases. She's particularly good at teaching scenario modeling—the kind that helps you anticipate what breaks when assumptions change, not just what works when everything goes according to plan.
Their Teaching Methodology
Both instructors follow a hands-on format. You'll spend roughly 60% of each session working through models yourself, with them looking over your shoulder. They'll point out shortcuts that save hours, flag common errors before you make them, and explain the reasoning behind structural choices. Expect lots of "here's what I'd do differently if I built this again" moments.
Curriculum Built Around Real Scenarios
We organized the program around six core modules. Each tackles a different type of financial challenge you're likely to encounter.
Foundation Architecture
Setting up models that scale without breaking. You'll learn structuring principles that keep your work maintainable when it grows from 5 tabs to 25.
Financial Statement Integration
Building three-statement models that actually balance. Plus troubleshooting techniques for when they don't—which happens more often than textbooks admit.
Valuation Frameworks
DCF, comparable company analysis, and precedent transactions. Focus is on understanding when each method makes sense and when it doesn't.
Scenario Analysis
Designing models that handle multiple scenarios without becoming unwieldy. You'll work through base, optimistic, and stressed cases on an actual company dataset.
LBO Mechanics
Leveraged buyout modeling from structure to returns. This gets into debt schedules, return waterfalls, and sensitivity tables that private equity teams actually use.
Presentation Preparation
Translating complex models into executive summaries. Learn to pull out the three numbers that matter most and present them without jargon or ambiguity.
Collaborative Learning Environment
Small cohort size means you'll get to know everyone. That's intentional—some of the best learning happens when you're comparing approaches with peers.
Peer Review Sessions
Weekly sessions where you'll walk through each other's models. It's a chance to spot issues you'd miss in your own work and see alternative approaches to the same problem.
Group Projects
Two major projects done in teams of three. You'll tackle a valuation and an LBO model using real company data. These projects simulate the collaborative nature of most finance work.
Network Building
Participants typically come from banking, corporate finance, consulting, and advisory backgrounds. That mix creates useful conversations and often leads to professional connections that outlast the program.
Market Context for 2025-2026
Financial modeling doesn't happen in a vacuum. Here's what we're seeing shape the field right now and what we're building into the curriculum.
ESG Integration
More firms are incorporating environmental and social metrics into valuation. We'll cover how to quantify these factors without turning models into guessing exercises.
Automation Tools
Software is handling more routine modeling tasks. That shifts the focus toward judgment calls—knowing what to model, not just how. We spend time on both.
Cross-Border Complexity
Transactions increasingly involve multiple jurisdictions. That adds tax considerations, currency risks, and regulatory layers that affect model assumptions in ways you need to anticipate.
Risk Modeling
Stakeholders want more sophisticated stress testing. You'll learn techniques for modeling downside scenarios that go beyond simple sensitivity tables.
Data Quality Issues
More data doesn't always mean better models. We'll cover how to work with imperfect information and document your assumptions clearly when data gaps exist.
Speed Expectations
Decision timelines keep compressing. That makes efficiency crucial—you need models you can build quickly without sacrificing rigor. We'll show you shortcuts that don't compromise accuracy.