Summary
Learn about the different analysis types available within MindBridge.
- General ledger analysis
- Accounts payable analysis
- Accounts receivable analysis
- Interim analyses
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Review engagement
General ledger analysis
Analyzes transactions posted to a general ledger.
MindBridge uses a suite of machine learning, statistical, and rule-based control points to find anomalies in financial data. This analysis is most effective when analyzing balanced transactions containing 2 or more line entries.
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Accounts payable analysis
Analyzes single-sided sub-ledgers to calculate detailed vendor balances and activity throughout a reporting period.
The accounts payable analysis leverages 15 control points, 5 of which are unique to this analysis. The control points specifically used for this analysis include:
- Vendor with a Debit Balance
- Duplicate Document
- Vendor Activity Flurry
- Unusual Amount by Vendor
- Vendor Not in the Vendor List
Accounts receivable analysis
This analysis can ingest and analyze single-sided sub-ledgers to calculate customer balances and detailed activity, including aging reports.
The accounts receivable analysis leverages 15 control points, 5 of which are unique to this analysis. The control points specifically used for this analysis include:
- Customer Activity Flurry
- New Invoice to Customer in Arrears
- Customer Not in the Customer List
- Unusual Amount by Customer
- Invoice Number Sequence Gaps
- Old Unpaid Invoice
Interim analyses
All of MindBridge’s analyses support interim analyses that can analyze a partial general ledger file. An example interim analysis would contain 9 months of activity. Once you receive the full general ledger export at the end of the reporting period, you can return to your interim analysis.
This analysis also leverages our suite of machine learning, statistical, and rule-based control points to find anomalies in financial data.
Review engagement
The review engagement analysis is used to analyze transactions posted to a general ledger. MindBridge uses our suite of 29 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.
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