Materiality is more than a technical accounting concept—it’s the lens finance teams use to determine what information is significant enough to shape a financial statement user’s understanding of an organization’s financial position. In practice, it helps finance teams decide where limited time and resources should be focused.
As financial reporting demands become more complex, rethinking materiality becomes a business imperative to make better data-driven decisions. To me, rethinking materiality means redirecting professional judgment and effort toward what matters most to investors, auditors, regulators, and other stakeholders.
Here’s a five-step roadmap for how finance teams—especially those within government entities—can rethink materiality to make better financial reporting decisions.
The first question finance teams should ask is whether they’re putting more effort into measuring reported amounts than what’s beneficial to the end user. For instance, perhaps there are reportable activities where a high volume of small transactions don’t make a difference in the user’s understanding of the financial condition. This may include small leases, minor software agreements, minor accruals, etc.
The second question to ask is whether important qualitative “barriers” might be standing in the way of realizing the opportunity or whether qualitative considerations could lead to the conclusion that quantitatively insignificant amounts might nonetheless be material. These may include legislative or regulatory requirements and political or public sensitivities, particularly when a topic is likely to draw public attention.
This next step helps quantify those opportunities (i.e., using the data to confirm whether the opportunities are real, and if so, using that data to determine how materiality should be applied). To accomplish this, it’s helpful to distinguish between two related but distinct decisions:
To make threshold decisions in a principled, defensible way, I suggest borrowing a concept from statistics known as the Pareto distribution. Many financial data sets follow a Pareto distribution pattern: a small number of large items account for most of the total value, while a large number of small items account for relatively little. This pattern is commonly associated with the 80/20 rule.
Data sets that often follow a Pareto distribution pattern include capital assets, leases, and certain types of accruals. Because much of the total value sits in a small number of large items, the reporting threshold can be raised significantly—removing large numbers of low-value items from detailed accounting—while changing the total reported value by only a small amount.
This next step mitigates qualitative risks so the opportunity can be realized. One promising strategy is to “quarantine” the risk. For instance, rather than applying a costly control broadly for consistency, finance teams can focus it on the smaller group of items that actually presents the risk.
Next, finance teams should consider whether efforts to rethink materiality could die of 1,000 immaterial cuts—meaning, they should ask: “Do many small exclusions or estimates accumulate into something that matters for decision making?”
Remember, the risk of cumulative impact is different for activities deemed a “no-go” decision (i.e., they won’t be reported at all) versus activities that are reported but have been applied to new reporting thresholds.
Therefore, let’s start with activities where new thresholds have been applied. Most times, the risk of a material cumulative impact is negligible. Cumulative materiality doesn’t mean that each year’s differences are added to the prior year’s differences. In most cases, those differences will self-correct over time. For example, if too much asset value is expensed each year rather than capitalized, the “understatement” of depreciation expense in subsequent years would offset the difference. As a result, it would exist only for the useful life of the uncapitalized assets. For example, a government entity’s expensing of newly acquired uncapitalized assets each year would likely result in a relatively consistent difference year to year, unless the entity’s small asset acquisition pattern is notably irregular.
Additionally, each threshold is set relative to the distribution of the data that makes up the activity (e.g., capital assets and leases). That means any individual threshold is sound in the context of its activity—and individually sound thresholds are unlikely to add up to collectively unsound results.
Now, let’s examine the risk of the cumulative impact arising from any activities that have been excluded entirely from reporting (i.e., the no-gos). The concern with this risk is that if a finance team were to deem multiple activities entirely immaterial, those activities might have an impact when considered together. Consider this: If leases and compensated absences are both deemed immaterial individually, if they were added together, would the impact on total liabilities matter?
To test this proposition, finance teams should estimate the impact of those two activities. The Government Finance Officers Association’s (GFOA’s) report, “Close Counts: Expanding the Use of Estimates for Better, Faster Financial Reporting,” provides insights into using estimates for financial reporting that can help.
Let’s suppose the test suggests that there could be material cumulative impact. The most expedient solution could be to use the estimates in the GFOA report. For activities that were too small to include individually, the error in the estimate would have to be huge to have a material impact. As a simpler example, let’s imagine you want to be 99% accurate in your reported liabilities. It turns out that compensated absences comprise 1% of all liabilities and leases comprise 1%. So, if you excluded both you’d only be 98% accurate. Your estimate of the combined liability from both leases and compensated absences could be as large as 50%, and you’d still be 99% accurate.
If it’s not possible to report the estimate, another option would be to do the accounting for the smallest number of excluded activities to get to your preferred accuracy level. In our example above, reporting either leases or compensated absences would be sufficient for 99% accuracy. It wouldn’t be necessary to do both.
Finally, government finance teams should also consider fund-level versus government-wide materiality. If prudent estimation methods make each component of a government-wide or fund-level total materially accurate, aggregating those components won’t make the resulting total materially inaccurate. If each component of a total is materially accurate, the total will be as well. Also, think of materiality in proportionate terms, not absolute terms. For example, $100,000 might be trivial relative to a total reported value of $100 million but nontrivial relative to $1 million.
The final step in rethinking materiality is to document, monitor, and communicate decisions so they can withstand scrutiny over time. To do so, finance teams should maintain a clear internal record that describes:
This will help auditors, colleagues, and successors follow the rationale behind materiality judgments.
Another important thing to consider is that materiality decisions aren’t “set-and-forget” actions—these decisions must adapt over time.
That’s where tripwires come in: predefined events or changes that automatically trigger a review of materiality decisions. For instance, a major policy or service change could alter transaction patterns or a natural catastrophe could cause a sharp increase in P-card purchases for emergency materials. In these cases, historical patterns shouldn’t be used for estimates. Tripwires help ensure that once-reasonable materiality decisions don’t linger past their useful lives.
Notably, when stakeholders understand the logic and safeguards behind materiality decisions, they’re more likely to support the result. Therefore, I suggest sharing the following with these financial statement users:
As you can see, rethinking materiality isn’t a one-time exercise—it’s an ongoing discipline. With the right data, safeguards, and communication, finance teams can make data-driven decisions that are both efficient and defensible, ensuring stakeholders receive the financial information that really matters.
