This time, we’ll discuss 5 key challenges in Monetary Unit Sampling (MUS), a popular statistical method used in audits. While the benefits are well known, implementing MUS effectively offers several challenges, from selecting the sample size to maintaining statistical validity. The challenges could impact your audit accuracy and reliability and sometimes lead you to start questioning your own credibility as an auditor. In this post, we will elaborate on several key challenges around MUS and offer solutions to the problem.
Monetary Unit Sampling
Monetary Unit Sampling is a statistical sampling method developed for auditing. It treats each dollar (or any other currency) as a sample unit and has an equal chance/probability of being picked as a sample. The mechanism gives larger transactions a higher chance of being selected for testing. This aligns with the auditor’s desire, who, by default, is skeptical of the larger item.
MUS is particularly useful in detecting overstatements. Its other benefit over other statistical sampling methods is that it is relatively easy to use and navigate. However, several challenges may sneak behind you and give you an unnecessary surprise.
Selecting the Appropriate Sample Size
Every sampling method requires a certain way to calculate the sample size. While there are guidelines for getting the sample size, they don’t eliminate the risk of picking the inappropriate one. Here are the consequences of not picking the right sample size.
- Too small sample size. Your audit would cover enough items (or transactions) to detect misstatements or ensure the account balance’s accuracy. This will lead to a false sense of assurance and potentially undetected errors.
- Too large sample size. Conversely, too many samples always mean too much work, increased cost, and inefficient audit engagement.
Determining sample size is a non-trivial task. It requires a careful balance between various factors.
- Tolerable Misstatement. This is the maximum error you could “handle.” The lower Tolerable Misstatement demands a larger sample size.
- Expected Misstatement. The amount of misstatement you expect to be found on the Financial statement. Higher expected misstatement means a larger sample size.
- Risk of Incorrect Acceptance (RIA). This refers to the risk of concluding that the population is not materially misstated when, in fact, it is. You determine this risk after considering various factors, including the audit objective, tolerable misstatement, and, as usual, your professional judgment.
- Population Size. This also influences the sample size. But what makes the factor different from the previous factors is we can’t decide the population size.
You can follow the guides below to mitigate the challenge of selecting the correct sample size.
- Utilize technology. Leverage audit software and tools that can automate sample size calculation based on predefined parameters and risk.
- Consult industry guidelines. Refers to industry standards and best practices for determining MUS sample sizes.
- Consult with an expert. Seek advice from experienced auditors or consultants with MUS expertise to help navigate challenges in sample size determination.
- Comparing several sample size methods. There are multiple methods to calculate sample size. Comparing several methods to boost your confidence about the sample size and to ensure accuracy.
Dealing with High-Value Items
Monetary Unit Sampling’s mechanism ensures the higher value gets a greater chance to be picked, which is the feature you love. But it can backfire because your samples might filled with too many high-value items (or transactions).
This means your sample is less representative of the population due to the need for non-high-value items. Another consequence is the distorted finding, leading to incorrect conclusions about the overall population. Another consequence of the scenario is that you should put more resources into it because high-value items have higher complexity.
The general solution for the challenge is to perform separate testing. You could test the high-value separated from the rest of the population. The approach ensures that the items receive the necessary attention without skewing the overall sample. Here are two ideas for the separate testing method.
- AICPA’s Audit Guide proposes a method to perform a full test for the items greater than the sampling interval.
- On the other hand, Christensen’s study indicates that several firms have a policy to test all items that are greater than tolerable misstatements. This is a good idea to implement. You can exclude all those items from the population. Thus, the population has less variability, and then your samples could address a wider stratum.
Handling Zero or Negative Balances
Zero or negative balances can arise in various conditions, such as customer refunds, bank overdrafts, or payroll deductions. However, due to its nature, MUS couldn’t “see” the zero or negative balances from the population.
Not addressing zero or negative balances may produce a negative effect on your audit, such as:
- The misstatement risk from the said scenarios isn’t covered in your audit procedures.
- Bias in audit results could lead to inaccurate conclusions about the population.
- Incomplete coverage due to the missing items.
To overcome the situation, you may:
- Complement MUS with another test. For example, you may test the completeness of the data using data outside the financial statement. It’s not specific for MUS, but if it is not addressed properly, you may miss the crucial part, which leads to an incomplete population.
- Separate testing of the credit balances from the debit balances.
Addressing Misstatement Found in the Sample
Sometimes, you find misstatements. Addressing the misstatement could be barely challenging. Here are some hurdles in addressing the misstatement.
- Fails to calculate. Extrapolating or projecting the misstatement is one of the challenges because you need to know how to do the error assignment (hint: usually we use the Taint Method) and how to calculate the Upper Misstament Limit. Errors in those procedures lead to inaccurate results.
- Fails when deciding what to do next with the misstatement. While misstatement isn’t ideal, sometimes you find the issues. Luckily, it’s not the end of the world, and there are many steps you can or should take. Such as increasing the sample (if your professional judgment says so), performing additional substantive tests for the same audit assertion, and communicating the misstatement to the management.
But no worries, there are several methods to address the challenge.
- Use audit tools/software/Excel templates to minimize the risk of miscalculation.
- If your firm hasn’t yet a guideline or SOP for handling the misstatement, create one. It can also be used as audit documentation when rationalizing your decision.
- Communicate with the auditee earlier and effectively so they are at least ready with the options to respond to the misstatement.
Maintaining Statistical Validity
MUS is a statistical method. While it can gain your confidence because it reduces bias and a “more scientific” way of doing the audit, it also comes with a need to maintain validity. This means you should employ “the machine” properly to make sure the output is valid.
For example, using MUS offers challenges when maintaining.
- Correct sample size. As we previously discussed, several methods for selecting sample sizes exist, so you should pick the one you are “comfortable” with. Also, don’t forget to ensure that the sampling is selected randomly.
- Accurate calculation. You may fail when crunching the number for each step, from calculating sample size to projecting the misstatement.
- Consistent methodology between engagements or within the same event. This may lead to bias and unreproduceable methods.
Here are some methods you can try to overcome the challenge.
- Use reliable or well-known tools to accompany your audit sampling.
- Proper training for the audit staff.
- Use random selection.
- Quality control.
- Regular review.
Conclusion
Monetary Unit Sampling is a powerful, well-known, and efficient audit sampling method that offers accurate and reliable results. However implementing MUS effectively requires overcoming several key challenges, including determining the right sample size, balancing high-value items, handling zero or negative balances, addressing identified misstatements, and maintaining statistical validity.
By following best practices and utilizing appropriate tools, you can navigate these challenges and leverage MUS to it’s full potential. As a last note, when responding to the challenges, you may adjust your audit procedures. Such actions might require you to increase your resources, so it’s wiser to consult with your supervisor or anyone above you.
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