Summary

MindBridge analyses using the periodic time frame can provide insights on financial and operational data that is imported on an ongoing basis, such as monthly or quarterly, so you can track risk over time using the Risk monitoring dashboard.

By proactively monitoring your automated detective controls, you can help reduce the response time once a risk has been identified, mitigate inaccurate financial reporting, and identify unusual or unexpected changes within a business that may require further investigation or new control activities. Additionally, finance teams can use the periodic time frame to demonstrate compliance with their legal obligations by enhancing their response to SOX requirements.

Learn more about the periodic time frame below, including details about:

• Overview of the periodic time frame
• Handling post-close entries and adjusting files
• Periodic analysis run results
• How historic data is used in the analysis

Additionally, you can also review how to import data into a periodic analysis.

Overview of the periodic time frame

As opposed to the full or interim analysis time frames, which require a complete ledger (or a complete ledger as-at a given interim date), the periodic time frame allows you to import and analyze data on a regular cadence, such as each month, or each quarter, over a given analysis period.

For example, suppose you wanted to analyze GL data on a monthly basis over the course of one calendar year (let's say "2022" for this example). When creating the analysis, the analysis period start date should be set to January 1, 2022, and the end date to December 31, 2022.

Once the analysis is created, you could import the data for January into the current period then run the analysis*. Next, you could append the February data by importing it the following month, then run the analysis again — you will see results that comprise January and February data.

You would continue this pattern for March, April, May, etc., until data has been imported for the entire analysis period, at which point you can choose to roll the whole analysis forward into the next year, and use it as historic data for the next period.

Post-closing entries and adjusting files

It's fairly common for entries to be adjusted in order to correct mistakes, or to modify entries found in an earlier time period. Issues such as incorrect transaction or entry details (e.g., incorrect amounts, incorrect vendors, etc.), would need to be adjusted for accuracy. In MindBridge, post-closing entries are considered to be entries that apply to previously imported data. 

For example, suppose your January data was imported on February 2nd, after month-end close. Then on February 5th, an issue was found in an entry from January 10th. In this case, the January data would need to be adjusted, but there are a few ways post-closing entries may be handled:

• Adding a new entry to the dataset with an effective date that reflects the appropriate timing.
  • This is the preferred approach for handling adjustments, as they can usually be tracked separately from the previously imported data.
• Modifying an older entry directly in the ERP or financial management system.
  • This approach should be avoided when analyzing data with MindBridge — if a previously imported entry is modified directly, it will not be updated in the analysis, and MindBridge will fall out of sync with your ERP or financial management system.

Special handling required in MindBridge

If adjustments are required after a dataset has been imported into MindBridge, some hands-on work may be required in order to get the most accurate analysis results.

Follow this process to ensure no duplicate entries appear in the analysis:

1. Determine an appropriate cut-off point for finalizing adjusting entries.
2. Use your ERP or financial management system to run a report that compiles effective period data, and note the date that the report is run.
3. Run a completeness check against the analyzed data. This can be performed within MindBridge, or by determining a total row count for the period.
4. When you are ready to import the next periodic dataset, use your ERP or financial management system to run a report that compiles adjustments and modifications for the period already imported into MindBridge. The report may be based on the entered date, the changed date, the last modified date, the record date, etc., but should not be based on the effective date or the period itself.
   
   Note: If you are unable to complete this step, reach out to your Customer Success Manager (CSM) to see if there are any applicable options for workarounds.
5. Compare the completeness check against the report data to make sure the previous period aligns with your expectations. If not, determine the difference between the calculated balances for the analyzed period and the adjusted entries for the same period, and use that to ensure that the balance at the beginning of the new period is correct.
6. Import the next dataset into the current period and run the analysis.
7. Run a completeness check against the newly analyzed data.
   
   

Periodic analysis run results

Because you can import data on an ongoing basis, each time the data is analyzed the reason for running the analysis is logged within the Run history section for the given analysis (located on the Data page), which you can refer to any time. For example, if you imported a new file, that reason would be recorded within the Run history.

You will always be able to access the analysis results of the most recent analysis run when using the periodic time frame.

Learn more about the Run history section

Materiality and account scoping

If you have added a materiality threshold and scoped the accounts for significance, this work will persist between subsequent analysis runs.

Learn how to add a materiality threshold to your analysis

Learn how to automatically scope the accounts for significance

Annotations

Annotations are linked to the results of a specific analysis run, and are archived automatically as a result of subsequent runs.

Learn more about working with annotations

Tasks

Tasks help you keep track of entries that may need further investigation, and are stored within the Audit Plan in the MindBridge sidebar (located on the left side of any MindBridge screen).

If you add tasks to your periodic analysis, they will be copied forward for each subsequent analysis run and the Task Details will display information about changes to risk scores in the History section.

Learn more about tasks

Reports

Reports generated within MindBridge can be exported to help you document and present your findings. 

Reports are linked to the results of a specific analysis run, and are archived automatically as a result of subsequent runs.

If you generate reports from the results of one analysis run, ensure they are exported and saved before the next run is performed, or they will be lost.

How historic data is used

Historic data helps MindBridge build an understanding of and expectations around patterns in the current period data, which can then be leveraged for forecasting and flagging outliers and anomalies. We recommend importing at least 6 months of historic data into the prior period(s) before running a periodic analysis for the first time. 

MindBridge allows you to add up to 4 prior periods* of data to your periodic analysis, but this historic data will not be scored, and will be used in different ways depending on the results dashboard. 

Historic data may be imported directly into a prior period on the Data page, or it may be accrued over time as you continue to import data, or alternatively, it may be sourced from rolling a periodic analysis forward. While historic data informs MindBridge's analytics, it is not represented in analysis results.

Risk monitoring dashboard

The Risk monitoring dashboard allows you to compare risk scores and summary statistics across categories, between two periods of time. This dashboard leverages the current period and up to 12 months of historic data to build context around the current period activity. 

If your analysis contains more than 12 months of data, this dashboard will ignore data beyond the 12 months that occurred immediately prior to the most recent period.

For example, suppose you were working on a periodic analysis that was set up with the analysis period January 1 to December 31, 2022. If you imported 6 months of historic data (spanning July 1 to December 31, 2021), then began the process of importing and analyzing data each month in 2022, once the data for July 2022 is imported, all 12 months of data leading up to it would be leveraged for additional context during the analysis. However, once you import data for August 2022, the data from July 2021 would be dropped, and so on, until the end of the analysis period. 

Learn more about the Risk monitoring dashboard

If historic data is not available

If historic data is not available, the analysis' risk scores and control point results may be less accurate — but only for a short time. As you continue to import and analyze subsequent data from the general ledger, MindBridge uses each file to build context around the current period data, which means your analysis results will become more accurate over time. 

Anything else on your mind? Chat with us or submit a request for further assistance.