Unusual Items - Comparison with Population

The Unusual Items - Comparison with Population analytic test identifies transactions that are considered unusual when compared to a selected group of transactions.

For example, this test can be used to identify transactions posted by users that post rarely when compared to other transactions. The users would be summarized against the population to obtain a percentage of transactions and any users below a set threshold would be shown in the result.

You can also use this test to analyze date fields. The transaction population is summarized by date and dates with a low number of transactions are identified in the result.

This analytic test can be used to identify:

  • Fictitious or unsupported transactions

  • Systemic issues in how a department processes transactions

  • Improperly configured system settings which may need improvement and further evaluation

  • Possible attempts to manipulate the financial statements

  • Financial reporting areas with lower accuracy and precision

  • Weak internal controls which may have further implications for consideration

  • Weak monitoring and detecting activities

  • Transactions that occurred outside normal business days

  • A risk of material misstatement due to fraud

Fields used for analysis

The following fields are required for this analysis:

  • Reference field(s) - Unique field(s) that are used to create a unique transaction ID such as the Entry ID field for the general ledger dataset. These columns are not part of the result but are used to identify the transactions that are part of the result. This field is already defined in the test and cannot be modified.

  • Unusual field(s) - The fields whose values are examined to determine if they are unusual. These fields can be numeric, date, time or character fields that contain some type of recurring pattern. For example, a numeric field that contains user IDs could be used to identify transactions posted by users that rarely post. Fields that contain amounts or quantities are not the best fit for this test because they usually do not have a recurring pattern.

Parameters

The following parameters must be set for this test:

  • Percentage value - Enter the percentage value that the test will use to determine which values should be considered unusual. For example, if you enter a percentage value of 3%, then any item in the column that makes up 3% or less of the population is considered unusual and included in the test results.

  • Percentage type - the type is if the percentage should be less than, less than or equal to, equal to, greater than or greater than or equal to the percentage value.

Test configurations

Note: The configurations available to you vary depending on the product you’re using.

The following configurations are available for this test:

  • Unusual Items compared with population

  • Unusual date items compared with population

Technical specifications

When you run the Unusual Items - Comparison with Population analytic test, the following steps are performed to run the test:

  1. If needed place any filters on the data in order that a subset is used for the analysis. If no filter is placed, the analysis will be run on the entire data file. This step can also be performed as the last step instead of the first.

    Note: Filters are not currently available and will be included in a later release.

  2. Validate that the necessary reference fields have been selected. If fields have not been selected, then create a unique reference field. This step is only performed if specific fields have been selected. If all the fields are available, this step is not necessary.

  3. Obtain the columns to create the subset that this analytic will be performed on. This subset could be based on one or more columns, the columns can be of any type and any order.

  4. Obtain the percentage for comparison.

    • If the user selected one of the following only one percentage is needed:

      • Less than (<)

      • Less than or equal to (<=)

      • Equal to (==)

      • Greater than or equal to (>=)

      • Greater than (>)

    • If the user selects a range then there needs to be a low value percentage and a high value percentage. The range can be based on the following:

      • Greater than (>)

      • Greater than or equal to (>=)

      • Less than or equal to (<=)

      • Less than (<)

  5. Summarize the subset columns in order to obtain the number of records per unique subset.

  6. Calculate the percentage of each subset based on the total number of records in the population.

  7. Extract all subsets percentages that fall within the percentage comparison as selected in step 4.

  8. Extract the transactions (lines) that relate to the subsets extracted in step 7.

  9. Extract the result fields selected by the user. All fields are extracted by default.

    Note: The ability to select result fields is not currently available and will be included in a later release.