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Analysis types available in MindBridge

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Summary

MindBridge supports several analysis types, depending on your organization's needs and methodologies. Review the analytical capabilities of each analysis below, as well as the datasets required to access them.

Transaction Risk Analytics

MindBridges's Transaction Risk Analytics ("TRA") offer tailored configurations of machine learning algorithms that you can run against large, structured financial and operational datasets. These configurations help you uncover financial risk and operational inefficiency, deliver meaningful results for specific use cases, and can help drive outcomes in your organization.

TRA configuration files are created in collaboration with a MindBridge Customer Success Manager ("CSM"), and can be geared towards your organization’s unique needs and methodology.

During creation, a TRA analysis may be configured for any of the following time frames:

  • Full: This time frame is intended to be used when analyzing a complete dataset. It may represent a full fiscal year of data, but the analysis period (i.e., the period of time being analyzed) can be set up for any amount of time. Learn more
  • Periodic: This time frame is intended to be used when analyzing sequential periods of data, imported on an ongoing basis for the duration of the analysis period. Learn more

Reach out to your CSM or Account Manager today to see if TRA is right for your organization.

General ledger analysis

The General ledger ("GL") analysis uses various combinations of machine learning, statistical, and rule-based control points to find anomalies in financial data. It runs analytics on 100% of the transactions and entries, and is most effective when analyzing balanced transactions containing 2 or more line entries.

A GL analysis can be created in engagements that use any of the following MindBridge libraries (or, a custom library that uses one of the following as a base library):

During creation, a GL analysis may be configured for any of the following time frames:

  • Full: This time frame is intended to be used when analyzing a complete general ledger. It may represent a full fiscal year of data, but the analysis period (i.e., the period of time being analyzed) can be set up for any amount of time. Learn more
  • Interim: This time frame is intended to be used when analyzing a portion of the general ledger, allowing you to conduct audit work prior to year-end. An interim analysis can be converted into a full analysis once the complete general ledger is available. Learn more
  • Periodic: This time frame is intended to be used when analyzing sequential periods of data in a general ledger, imported on an ongoing basis (depending on your analysis configuration). Learn more

Relevant files

  • General ledger: Usually an export from an ERP or accounting system. This export typically needs to be converted into an excel format such as .xlsx or .csv.
  • Opening balances: Contains account balances at the start of the fiscal year.
  • Closing balances: Contains account balances at the end of the fiscal year.
  • *Chart of accounts: Helps MindBridge map your account numbers to the appropriate MAC code.
  • *Account mapping: Helps MindBridge map your account numbers to the appropriate MAC code.

*When you import a general ledger, chart of accounts, or account mapping file, MindBridge detects and maps accounts automatically, but you will need to verify your account mappings before running an analysis. Account mappings are shared between all general ledger analyses within an engagement.

Note: Review the import requirements for general ledger analyses below.

 

Analysis capabilities

  • Risk scoring and analytics: Access by importing a general ledger.
  • Trends and ratios: Access by importing a general ledger and opening balance. Importing an opening balance ensures that account balances reflect the position of the accounts at the start of the fiscal year.
  • Prior period comparison: Access by importing general ledgers and opening balances for the current period and immediately prior period.
  • Period-over-period trends and ratios: Access by importing a general ledger for the current period and at least one previous period. Importing an opening balance for each period ensures that all account balances are up-to-date with adjusting entries made prior to that period.
  • Expected range: Access by importing the general ledgers for the current period and up to 4 prior periods.
  • Income statement report: Access by importing a general ledger.
  • Balance sheet report: Access by importing a general ledger and an opening trial balance.
  • Pre-analysis completeness check: Access by importing a general ledger, an opening balance, and a closing balance.
Note: If enabled by your App Admin, you may also have access to the Risk monitoring dashboard, allowing you to compare summary analytics between two date ranges.

Accounts payable analysis

The AP analysis can analyze single-sided sub-ledgers to calculate detailed vendor balances and activity throughout a reporting period.

This analysis leverages machine learning, statistical, and rule-based control points, the following 5 of which are unique to this analysis:

Relevant files

  • AP detail: Contains all increases and reductions in AP accounts, including invoice and cash activity.
  • Vendor list: Your client’s approved vendors.
  • Vendor opening balances: Your client’s vendor accounts at the start of the fiscal year.
  • Open payables list: Contains all open payables as at the start of the year, allowing for detailed aging calculations.
Note: Review the Accounts payable data checklist to learn more about these datasets, including required files.

Analysis capabilities

  • Risk scoring and analytics: Access by importing an AP detail.
  • Trends: Access by importing an AP detail. Optionally, importing an open payables list or vendor opening balances file ensures that vendor balances reflect the position of the accounts at the start of the fiscal year.
  • Period-over-period trends: Access by importing the AP detail for the current period and up to 4 prior periods. Importing an open payables list or vendor opening balances file for each period in the analysis ensures that all vendor balances are up-to-date with adjusting entries made prior to that period.
  • Vendor aging: Access by importing an AP detail file and an open payables list.

Accounts receivable analysis

The AR analysis can analyze single-sided sub-ledgers to calculate customer balances and activity throughout a reporting period, including aging reports.

This analysis leverages machine learning, statistical, and rule-based control points, the following 5 of which are unique to this analysis:

Relevant files

  • AR detail: Contains all increases and reductions in AR accounts, including invoice and cash activity.
  • Customer list: Provides the names of each customer.
  • Customer opening balances: Contains the balances on your client’s customer accounts at the start of the fiscal year.
  • Open receivables list: Contains all open receivable as at the start of the year, allowing for detailed aging calculations.
Note: Review the Accounts receivable data checklist to learn more about these datasets, including required files.

Analysis capabilities

  • Risk scoring and analytics: Access by importing an AR detail.
  • Trends: Access by importing an AR detail. Importing an open receivables list or customer opening balances file ensures that customer balances reflect the position of the accounts at the start of the fiscal year.
  • Period-over-period trends: Access by importing the AP detail for the current period and up to 4 prior periods. Importing an open receivables list or customer opening balances file for each period in the analysis ensures that all customer balances are up-to-date with adjusting entries made prior to that period.
  • Customer aging: Access by importing an AR detail and an open receivables list.

Review engagement

The review engagement analysis can analyze transactions posted to a general ledger in order to help you develop an opinion related to reasonable assurance. MindBridge uses our suite of machine learning, statistical, and rule-based control points to provides streamlined and repeatable analytics to improve efficiency.

Visit Review engagements: Resource guide to learn about file import requirements.

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

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