Audit & Evaluate Excel Models

Overview

This intermediate course is designed for financial analysts, business planners, and finance professionals who need to ensure the integrity and reliability of their spreadsheet models. In a world where a single formula error can lead to multimillion-dollar losses, mastering model auditing is no longer optional. This course equips you with a systematic, repeatable framework to audit complex Excel models, diagnose critical errors, and confidently evaluate the impact of any changes. You will move beyond basic error checking to master Excel’s powerful formula auditing tools, implement robust version control, and document your models for transparency and collaboration. Through hands-on activities, you will learn to trace data dependencies, analyze scenarios, and build a structured review process inspired by professional best practices. The course uses real-world case studies, like the JPMorgan “London Whale” trading loss, to bring the consequences of model failure to life. By the end, you won't just build models; you'll build trusted models that stand up to scrutiny and support sound decision-making.

Syllabus

Auditing Excel Models for Accuracy

In this module, learners will develop an "auditor's mindset" by diving into the essential tools and techniques for diagnosing errors in complex financial models. Moving beyond simple error messages, they will learn to systematically trace data flows, identify broken links, and untangle convoluted formulas using Excel's built-in auditing features. Real-world case studies, such as the TransAlta incident, will reinforce the critical importance of a methodical review process in preventing costly mistakes.

Evaluating Model Changes and Ensuring Integrity

Once a model is error-free, how do you keep it that way? This module transitions from reactive error-fixing to proactive model governance. Learners will discover how to implement a version control system using a simple revision log and evaluate the bottom-line impact of changes to model assumptions. By comparing different model versions, they will learn to quantify how modifications affect key outputs, ensuring transparency and building a defensible audit trail for all future updates.