Though they may seem alike, sensitivity analysis and scenario analysis are only somewhat similar. Both are financial modelling techniques designed to assess the impact of risk on a project (often an investment). By changing variables strategically, users can analyse how changes (e.g. market conditions) will affect a given outcome.
It is easy to confuse sensitivity analysis and scenario analysis with one another or a myriad of other financial analyses relevant to financial statements. Let’s explore sensitivity and scenario analysis by defining what they are not:
And, most importantly, sensitivity and scenario analyses are not identical. In this article, we’ll examine the intricate relationship between these analyses and the role financial statements play in both.
You may have heard sensitivity analysis referred to as ‘simulation’ or ‘what-if’ analysis. Financial Planning and Analysis (FP&A) teams can more accurately forecast financial performance by changing specific variables.
For example, finance teams might measure the effect of a company’s Net Working Capital (NWC) on its profit margin. Businesses can allocate resources by carefully measuring the impact of the NWC – a key business driver. For instance, changing how they manage their working capital (i.e. accelerating receivables, optimising inventory levels or extending payables) and relying less on external finance, etc.
The purpose of a sensitivity analysis is, therefore, to measure robustness when key variables change.
Scenario analysis involves assessing how the value of a portfolio might change in response to shifts in key influencing factors, such as:
A well-conducted scenario analysis is prophetic, accurately predicting the outcomes of potential long-term decisions.
Case in point: A manufacturer might conduct a scenario analysis to determine the impact of increasing raw material prices and sales volume on the firm’s operating profit. The firm can then adjust for both best-case and worst-case scenarios and allocate financial resources accordingly.
Much of a company’s financial data is embedded within its financial statements, requiring careful analysis to extract meaningful insights. (Unfortunately, there’s surprisingly little online literature discussing these statements’ crucial role in sensitivity and scenario analyses).
While financial statements provide a solid foundation, they don’t offer the full picture. For instance, effective scenario analysis and sensitivity analysis may also require:
Let’s say that you want to examine the potential profitability of a business using its income statement. Firstly, you would need to identify the key data points on the income statement, such as:
Then, you would adjust each of these variables systematically (e.g. increase revenue by 5% or reduce COGS by 10%) to assess how sensitive net profit, gross margin or operating income is to changes in each line item.
More specifically, the user can leverage specific data points in the P&L statement in strategies like:
Applying an appropriate sensitivity analysis model to a company's income statement can uncover various actionable insights.
How financial statements (specifically) form the foundation of scenario analysis isn’t readily apparent. Scenario modelling is arguably more complicated. Rather than changing one or two variables, the user must create a scenario by filling in the assumptions and details of each scenario (and comparing carefully). For example, the 'Base Case' represents the most likely outcome in a scenario analysis. This scenario is a benchmark for comparing other scenarios, like best-case and worst-case scenarios.
However, you can leverage balance sheet metrics to test resilience and funding requirements. More specifically, balance sheet metrics like accounts receivable and inventory can be scenario-tested to measure their effects on working capital, debt-to-equity ratio, return on assets (ROA) and other metrics.
When completed repeatedly and accurately, financial leaders can leverage the insights from scenario analyses to:
Note that we used the word ‘repeatedly’. FP&A teams might find themselves repeatedly setting up, calculating and validating analyses. Completing low-skilled Excel work represents a waste of time and talent.
That’s where AI comes into play.
We've previously explored AI in financial modelling, concluding that its strength lies in reducing friction during the process – not in building models entirely from scratch. However, with the rapid pace of AI advancements in finance, that view may soon be outdated.
Yet, in the meantime, FP&A teams could experiment with lightweight AI tools to reduce busy work and get to the strategic decision-making and measurement involved in sensitivity and scenario analyses. Examples of such tools may include:
Most tools like these are not difficult to use. In fact, if used correctly, they can generate hours in weekly time savings. Though FP&A experts are not becoming obsolete, activities like manual data extraction, copying and pasting and repetitive report generation most definitely are.
Sensitivity and scenario analyses are key financial modelling techniques. Practically, both analyses pivot around accessing financial statement data quickly and accurately.
Both sensitivity and scenario analyses are founded on understanding a company’s financial health and risk profile, which should be (accurately) presented on the company’s financials. FP&A teams leveraging AI for financial statements will likely spend countless hours completing low-skilled data management tasks – keeping them from more important tasks they’ve trained for.
Financial Statements AI is designed to remove the manual friction from analysing financial statements. Instead of wasting time copy-and-pasting and editing data, Financial Statements AI releases structured, editable data in Excel from financial statement PDFs.
Try it out now for free – book a demo with our financial data project team or email hello@evolution.ai.
