Analytical procedures: investigate a trend before concluding
An analytical procedure compares an observed result with a defensible expectation, then investigates meaningful differences. Normalize for activity, timing, and process changes before calling a movement unusual. A trend is a lead for further work, not proof of an error or fraud.
Hypothetical teaching data, not industry statistics
April
$20/ shipment
$56,000 / 2,800 shipments
May
$20/ shipment
$60,000 / 3,000 shipments
June
$30/ shipment
$90,000 / 3,000 shipments
May to June: ($30 - $20) / $20 = 50% increase in unit cost. Volume is unchanged. Investigate the cause before drawing a conclusion.
Build a comparison that can answer the question
| Decision | How to apply it | Mistake to avoid |
|---|---|---|
| Expectation | Choose a business relationship, such as delivery cost per shipment. | Comparing total costs when shipment volume changed substantially. |
| Comparable data | Check definitions, period cutoffs, completeness, and one-off items. | Mixing dispatched and delivered shipments in the same denominator. |
| Follow-up | Corroborate plausible explanations with contracts, invoices, or operational records. | Accepting an explanation because the trend looks smoother after an adjustment. |
Expectation
Choose a business relationship, such as delivery cost per shipment.
Avoid: Comparing total costs when shipment volume changed substantially.
Comparable data
Check definitions, period cutoffs, completeness, and one-off items.
Avoid: Mixing dispatched and delivered shipments in the same denominator.
Follow-up
Corroborate plausible explanations with contracts, invoices, or operational records.
Avoid: Accepting an explanation because the trend looks smoother after an adjustment.
A decision path you can use
- 1Set the expected relationship
- 2Calculate and identify the anomaly
- 3Corroborate the explanation
Understand the method
Work through the reasoning
Form an expectation before looking for a story
An analytical procedure compares an observed relationship with a reasoned expectation. For example, delivery expense might vary with shipment volume, distance, fuel rates, and service mix. A simple increase from last year is a starting observation, not a finding. Define the relationship and the factors that could reasonably change it before interpreting the result.
The expectation must be precise enough for the purpose. A broad trend can help identify areas for investigation, while a conclusion about improper charges requires more specific evidence. Record the assumptions behind a benchmark so later explanations can be tested instead of accepted simply because they fit the chart.
Make periods and groups comparable
Check definitions, completeness, and calculation rules before comparing values. Revenue per employee can change because revenue recognition changed, contractors were excluded, or a business was acquired. Ratios need an appropriate denominator; raw counts can exaggerate deterioration when transaction volume rises.
Segment data where important differences would otherwise be hidden. An overall return rate might remain stable while one product’s defect rate rises sharply. At the same time, avoid splitting the data into so many tiny groups that random variation becomes a dramatic story. Explain the basis and limitations of the comparison.
Use anomalies to direct additional work
An unexpected relationship identifies a question to investigate. Duplicate amounts may arise from legitimate recurring invoices or from duplicate payments. A sudden improvement in cycle time may reflect better operations or a changed start date in the reporting system. Trace the anomaly to underlying transactions and corroborate the proposed explanation.
Management’s explanation can be relevant evidence, but it should be evaluated against records and other information. If a volume change supposedly explains expense growth, calculate whether its size and timing fit. Retain contradictory observations and consider whether the original analytical model needs revision.
Communicate the calculation and its limits
A useful chart identifies its population, period, units, and comparison basis. Show whether a value is a count, percentage, average, or indexed change. Hypothetical training data should be identified as such. Avoid implying that an illustrative pattern is a measured trend in the exam or in an actual organization.
The workpaper should preserve the source, transformations, formula, expectation, anomalies, and follow-up. A reader should be able to understand how the analysis informed the finding. Do not convert correlation into proof of cause: the analysis can guide investigation without independently establishing misconduct or a control failure.
Original teaching example
Apply the method to a decision
The scenario
In this hypothetical dataset, May delivery cost is $60,000 for 3,000 shipments. June cost is $90,000 for 3,000 shipments. A manager attributes the increase to growth.
The decision
Cost per shipment increased from $20 to $30, or 50%, while the recorded volume stayed constant.
The proposed growth explanation does not fit the denominator. Examine fuel charges, shipment mix, contract rates, duplicate invoices, and cutoff differences. The calculation identifies a question to investigate; it does not prove which explanation is correct.
Independent hypothetical case. It is not an official IIA question, an actual investigation, or a scoring prediction.
A second application
The rate improves because the denominator changed
The situation
A dashboard shows complaints per order falling from 4% to 2%. This year’s denominator includes canceled orders, while last year’s included only completed orders.
Reason through it
The two rates use different populations. Recalculate on a comparable basis and investigate the actual complaints before attributing the apparent improvement to better service.
Take the decision
Validate definitions before interpreting the trend. An attractive chart cannot compensate for an inconsistent denominator.
Original hypothetical teaching case.
Check your understanding
Choose the best answer, then check the reasoning for each alternative. These short learning checks are not a full exam simulation. Answers are not saved or sent.
Questions about this topic
Can analytics replace all detailed testing?
Only when the chosen procedures and reliable data provide adequate evidence for the objective. An unexplained anomaly or a weak expectation often calls for additional examination rather than a broad assurance conclusion.
Are the charts on this lesson actual exam or industry trends?
No. They use labeled illustrative data to teach analytical judgment. They are not evidence of exam frequency, pass rates, or measured performance at a real organization.
Sources and syllabus scope
Prepared by MiloPrep editorial team. Source links checked 2026-10-10. These notes and cases are independent teaching material; the IIA sources do not verify or endorse them.
- IIA expanded syllabus
Establishes the examined topics. Primary domain: Information Gathering, Analysis, and Evaluation (40% of Part 2). This is a domain allocation, not a separate weight for this lesson.
- Global Internal Audit Standards reference
- Locator: Standards 14.1-14.3. Supports the professional requirements cited here; our scenarios and solution steps are original applications. Current IIA Standards resources.
Part 2: Internal Audit Engagement. Selected learning notes, not complete curriculum coverage. Browse all CIA topics.