A review engagement is conducted to provide limited assurance about whether the financial statements as a whole are free from material misstatement.
MindBridge empowers accountants to enhance their analytical capabilities by analyzing the underlying data contained in the financial statements. The use of AI and advanced analytics, such as multivariate regression, statistical analyses, and data visualization, provides insights that improve the sufficiency and appropriateness of review evidence in a more timely fashion.
The core value of the MindBridge Review library for review engagements is the streamlined and repeatable analytics that can be used to improve efficiency.
MindBridge Review: Account grouping
This library uses the MindBridge Account Classification (MAC) code system.
MindBridge Review: Ratios
This library contains MindBridge’s 25 predefined ratios.
MindBridge Review: Filters
This library includes 16 predefined filters made to help you narrow your search results when viewing an engagement’s transactions in the Data table dashboard. MindBridge’s predefined for-profit filters fall into the following categories.
Additional Assurance filters
- High Impact Order
- MindBridge AI Journal Entry Testing
- Unusual Transactions
Purchasing Cycle filters
- Cost of Sale
- Payroll Transaction
- Repairs and Maintenance Review
- Reversed Revenue or Reversal of Revenue
- Other Income
- Transactions near Analysis Period End
- Transactions near Period End
- Manual Transactions near Analysis Period End
- Manual Transactions near Period End
- Material Transactions near Analysis Period End
- Material Transactions near Period End
MindBridge Review: Control points
This library contains MindBridge’s 29 rules-based, statistical, and machine learning control points.
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