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Overview: Analyses available in MindBridge

Article author
Jonathon Plowman-Samson
  • Updated

General Ledger Analysis

Our general ledger analysis is used to analyze transactions posted to a general ledger. MindBridge uses our 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 two or more line entries.

General Ledger Analysis - Required & Optional Data

  • General ledger (required)
  • Opening balance (optional)
  • Closing balance (optional)

Accounts Payable Analysis

This analysis can ingest and analyze single-sided subledgers 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:

Accounts Payable Analysis - Required & Optional Data

  • Accounts payable detail (required)
  • Vendor list (optional)
  • Customer opening balances (optional)
  • Open payable list (optional) 

Accounts Receivable Analysis 

This analysis can ingest and analyze single-sided subledgers 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:

Accounts Receivable Analysis - Required & Optional Data

  • Accounts receivable detail (required)
  • Customer list (optional)
  • Customer opening balances (optional)
  • Open receivable list (optional) 

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.

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