Auditors love statistical sampling and so does the MindBridge Ai team. Why wouldn’t we—statistical sampling uses the laws of probability to measure sampling risk. We truly believe that statistical sampling largely outperforms judgment sampling.
Don’t get us wrong—the experience of a Partner is undisputed and their ability to focus the audit and identify areas of risk in financial statements is the key to a successful audit and, of course, the peace of mind once an audit opinion is issued. However, once the risk areas are identified and a population of thousands (if not millions) of transactions remains to be reviewed, experience alone might not be sufficient to select a test set.
Statistical sampling and the laws of probability make it feasible to pull a test set from a huge population so an audit team can use it to give reasonable assurance that the overall population is free from material misstatements.
Can audit teams give the same reasonable assurance and test fewer transactions?
There are many commonly used methods to enhance statistical sampling in the audit profession. For example, Monetary Unit Sampling is one (very, very popular) way that a test set can enrich the items most likely to be of interest to auditors. In Monetary Unit Sampling, every dollar is regarded as a distinct unit and given equal chance of being in a transaction picked for testing. Therefore, the higher the amount of a transaction is, the more likely it will be part of the test set. In an audit context, this is great as larger transactions, the ones that are more likely to cause a material misstatement, are more likely to be picked for testing.
How did MindBridge come up with an improved sampling methodology?
First, let’s clarify what improved means. For us improved means to enrich data in a meaningful way so it makes the whole testing process more efficient. Similar to Monetary Unit Sampling, we enrich a data set with additional information to make it more likely that transactions get sampled that are of audit interest.
The intelligent sampler
We run a number of tests to calculate a risk score for every single transaction in a data set. This risk score is the basis of our intelligent sampler which pulls a stratified sample set across the whole population. As with Monetary Unit Sampling, every transaction has a chance for being selected for field testing; however, the ones more likely to be of audit interest having a higher chance.
What does “of audit interest” mean?
“Of audit interest” means the likelihood that a transaction is misstated due to fraud or error. In a number of field experiments, the Ai Auditor™ has proven its ability to identify and push fraud- and error-prone transactions to the top of our risk ranking.
This ability of our Ai Auditor™ to identify transactions which are more likely to be fraudulent gives audit teams the ability to reach the desired confidence level with a smaller test set of transactions. MindBridge’s intelligent sampler is a focusing tool that helps the auditor identify audit areas that they should spend the most time on, but at the same time makes sure they also spend enough time in other areas beyond the high-risk transactions.
Comparing statistical random sampling with intelligent sampling
Our data science team created a general journal for educational purposes. This journal consists of 2966 transactions of which 24 transactions are non-compliant. The non-compliance rate in this file is 0.8%.
Assuming an acceptable error rate of 1%, it would be required to test a sample set of 230 transactions to achieve a 90% confidence utilizing statistical random sampling methodology.
Our intelligent sampler takes an equal number of samples from the Low and Medium/High-risk categories. This ensures that every transaction has a chance of being selected in the sample, but those with higher risk have a higher chance. Using this method the auditor has a 90% chance of discovering non-compliance in the file with just testing a sample set of 14 transactions.
|Approach||Confidence level / error rate||Required Sample Size|
|Statistical Random Sampling||90%/1%||230|