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Analyze

Complete an analysis

This article walks you through the full audit analysis workflow, from setting up your engagement to exporting your final workpaper.
Before any analysis can happen, you need to pull in the client's ledger data. On the
Getting Started
 screen, select your audit area then
Continue
.
  1. Request the client's ledger data:
    1. Go to the
      Communications
       tab.
    2. Select
      Create Request
      .
    3. Select
      Request Ledger Data
       from the options.
    4. Enter a title for the request.
    5. Select the client contact.
    6. Set a due date for the request.
    7. Optionally, add a description to give the client more context.
    8. Send the request.
    Once the request is sent, the following happens automatically:
    • The client receives an email with a link to a microsite.
    • The client selects their accounting package from a supported list.
    • The client signs in using their accounting package credentials.
    • Data ingestion begins automatically after sign-in.
    • Both you and the client receive confirmation emails once ingestion is complete.
    important
    The person responding to the ledger data request needs to have administrator permissions to the accounting package. Without administrator access, the data ingestion can't be completed.
  2. Select a date range to define your testing population.
    This step does more than filter by date; it actively defines the population you'll be testing. When you select a date range, the system filters all unpaid and partially paid receivables within that range, traces every associated transaction, and uses the results to build your testing population.
  3. Configure the 3 key analysis settings.
    Each setting directly impacts how your population is segmented and how samples are calculated.
    Significant Amount:
     This is the dollar threshold used to determine whether items are considered individually significant. Any items that meet or exceed this amount are placed in the
    Significant Items
     segment and tested at 100% — they're not sampled. This value must be less than or equal to your
    Tolerable Misstatement
    .
    Tolerable Misstatement:
     This is the auditor's estimate of acceptable misstatement. It's used in sample size calculations. If your engagement is integrated with Guided Assurance, values from CX-2 Financial Statement Materiality will automatically populate here, but you can override them if needed.
    Level to test:
    You can choose
    Balance
    so risk scores roll up from individual transactions to their associated balance, or
    Transaction
    to score each transaction individually. We recommend
    Balance
     for AR/AP.
  4. Select the Risk Identification Techniques (RITs) to apply to your population. The following RITs are available:
    • Back Dated: Flags transactions dated more than 30 days after their effective date.
    • Closing Entry: Identifies entries posted in the last 14 calendar days of the year.
    • Duplicate Transaction: Detects transactions that appear to be duplicates.
    • Forward Posted: Catches transactions posted to a future date.
    • Non-Working Day: Flags transactions recorded on weekends or holidays
    • Round Sum Value: Identifies transactions with full integer amounts (no cents).
    • Isolation Forest: Uses AI/ML to detect outliers that don't fit normal patterns.
  5. Export your results to an Excel workpaper.
    The export is formatted as a pivot table for easy navigation and includes everything you need to document your work:
    • Engagement context, including the engagement name and year-end date
    • Tolerable misstatement and significant amount values
    • A summary of each RIT — whether it was toggled on or off, the unusual item criteria used, and any relevant thresholds
    • All segments with full transaction-level detail
    • Sample documentation, if samples were generated during the analysis

This article applies to:

  • Product: audit-intelligence
  • Sub-Product: ai-analyze