An aging schedule can agree with the ledger while placing overdue invoices in the current column, undermining selections for valuation testing. The auditor therefore evaluates reliability for the specific procedure before using information obtained from internal and external sources.
- Completeness: The information includes the records, fields, periods, and categories necessary for the intended procedure, without omissions or unintended duplication.
- Accuracy: Recorded attributes and calculated outputs correctly reflect their supporting information, including amounts, dates, classifications, and mathematical relationships.
- Authenticity: The information is genuine as represented, with no unresolved concern about its origin or undisclosed alteration.
- Management bias: Management's influence can favor a desired conclusion through selective inputs, exclusions, assumptions, adjustments, or presentation, whether intentionally or unintentionally.
Susceptibility to management bias of data requires attention even when calculations contain no errors and the underlying documents appear authentic. An analysis excluding customers in financial distress can systematically understate collection problems despite accurately processing every included record.
The auditor identifies the information's intended assertion and required detail before choosing procedures to establish its accuracy and completeness.
Common mistakes
- Testing only returned records. Marlow's nine extracted invoices cannot reveal A099 without a completeness procedure reaching beyond that filtered output.
- Reconciliation treated as final. A total of $120,000 can coexist with A104's incorrect bucket if only the omitted invoice is restored.
- Automation treated as corroboration. An AI summary and an automated reconciliation derived from the same incomplete input preserve the same omission.
Bottom line
- Evaluate completeness, accuracy, authenticity, and susceptibility to management bias for the information's intended use.
- Separate population completeness, item accuracy, calculation accuracy, and sufficient detail when validating entity-produced information.
- Match executed extraction settings and processing logic to the intended population, then investigate omissions, duplicates, and contradictory evidence.
- Direct information testing, relevant control testing, or an appropriate combination can support reliability.
Exam shortcut
Start with the proposed use: selecting invoices, identifying terms, or evaluating a valuation input. Then find the field or population feature that could defeat it. If the stem says “agrees in total,” look next for wrong categories, offsetting errors, and shared filters. Choose a procedure capable of finding the stated defect. More selections from an incomplete output cannot reach omitted categories.
The full lesson (about 4,982 words, 33 min read) adds 2 worked examples, all 5 common mistakes, a self-check, free in the app.
Learning objectives
- III.A2
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