Analytics for Audit and Assurance
What you must know
Understand how analytics supports risk assessment, planning, substantive testing and fraud detection. Key concepts: full-population testing, journal entry testing (unusual timing, round amounts, unusual users, post-closing entries), three-way match (purchase order, goods receipt, invoice), duplicate payment detection, Benford's Law for anomaly screening, and recalculations. Know the audit standards concepts of sufficient appropriate evidence, relevance and reliability, and how automated tools and techniques (CAATs) fit within the audit approach. Understand completeness and accuracy testing of client-generated data - the auditor must verify the population is complete before relying on it. Know the distinction between tests of controls and substantive procedures, and how analytics can serve either.
Traps in questions
A common trap is treating an analytics exception as proof of fraud - exceptions are indicators requiring follow-up, not conclusions. Another is relying on client data without testing its completeness and accuracy. Questions may confuse Benford's Law (expected digit distribution, useful for screening large transactional populations) with a definitive fraud test. Also watch for the misconception that full-population testing eliminates the need for professional judgement.
Rule of thumb
Before any audit analytics: verify completeness (reconcile record counts and control totals to the general ledger or source system) and accuracy (agree a sample back to source documents).
Worked example
Journal entry testing flags 120 entries posted outside business hours by dormant users. These are exceptions, not findings of fraud. The auditor investigates each, documents explanations, and escalates only where evidence supports misstatement or irregularity.