Data analytics allows auditors to examine every transaction instead of a small sample, find patterns people would miss, and focus fieldwork on the exceptions that matter. Yet many audit functions struggle to move beyond occasional spreadsheet analysis.
Using analytics well starts with the audit question. Auditors decide what risk they want to test, which data fields are needed, how to confirm the data is complete and reliable, and how exceptions will be investigated. Common uses include duplicate payments, split purchases, unusual journal entries, payroll anomalies and expense patterns.
Typical challenges include incomplete data, tests that produce too many false positives, exceptions reported without context, and analytics that are built once and never reused. Sampling decisions also matter: analytics and sampling should complement each other, not compete.
The resources below help you build analytics into everyday audit work. Tools help you select tests and choose between analytics and sampling; the guide explains how to run analytics from question to conclusion; and templates support data requests and sampling plans. When a test proves valuable, the continuous monitoring resources show how to repeat it.
11 free resources · No registration
Decide which audit tests should become monthly, weekly or near-real-time monitoring routines — based on risk, repeatable logic, reliable data and clear ownership.
PDF • 6 Pages • Free
Choose the strongest practical test method — inquiry, inspection, observation, reperformance or data analytics — for each control and assertion.
PDF • 6 Pages • Free
Plan a defensible sampling approach — population, objective, risk strata and selection method — before you pull a sample. A planning aid, not a sample-size calculator.
PDF • 6 Pages • Free
Match each risk to a practical data test, the fields it needs and whether it suits one-off audit work or continuous monitoring.
PDF • 6 Pages • Free
Start with the audit question, not the software: define and validate the data, design risk-based tests, investigate exceptions in context and save logic for reuse or monitoring.
PDF • 6 Pages • Free
Design repeatable tests that flag exceptions reliably — choosing suitable risks, defining exact logic and thresholds, assigning investigation owners, piloting with real data and tracking trends.
PDF • 6 Pages • Free
Verify invoice processing and payment controls — controlled receipt, PO/receipt matching, justified non-PO invoices, independently reviewed payment runs, and analytics for duplicates, threshold payments and recent bank changes.
PDF • 6 Pages • Free
Review sales and revenue controls — approved price and discount authority, controlled overrides and promotions, order-to-invoice matching, period-end cut-off, and analytics on discounts, credit notes and revenue trends.
PDF • 6 Pages • Free
Issue clear PBC and information requests that state the item, period, population, required fields and format, track owner and status, and check completeness against the source system.
PDF • 6 Pages • Free
Document a defensible sample: population source and completeness, risk strata, sample size and selection method, high-risk items added, exceptions found and the conclusion on the population.
PDF • 6 Pages • Free
Design and document a repeatable monitoring test: risk statement, data fields and rule logic, alert threshold and tolerance, false-positive handling, alert reviewers, evidence retained and escalation.
PDF • 6 Pages • Free