ACCA Applied Skills · Audit and Assurance

Automated tools and techniques: formula sheet

Full chapter guide

Key formulas

Audit software: Audit software = programs run on the client's live data files; Mainly substantive procedures: recalculation, sorting, matching, duplicate and gap testing, sample selection.
Test data: Test data = dummy transactions (valid + invalid) processed by the client's system, results compared with expectations; Mainly tests of controls. Use dummy data on a copy of the system where possible, or reverse the entries, so live records are not corrupted.
Reliability check: Result is reliable only if the program tested is the program used all year; Test data tests the system at one point in time. The auditor must be satisfied that no program changes were made afterwards.
Test data: Test data = dummy valid and invalid transactions → expected result vs actual result; Used as a test of controls. Always predict the expected result before processing.
Integrated test facility: ITF = dummy entity in live system + test transactions processed with real data; Dummy entries must be reversed or removed afterwards.
Audit software: Audit software = auditor's programs run on client's live data files; Mainly substantive testing: extract, recalculate, sort, compare, sample, check duplicates and gaps.
Key distinction: Test data tests the program's controls; audit software tests the data; Use this one line to separate the two in any question.
Link rule for answers: Risk → analytic test → what exception shows → follow-up procedure; Use this chain so every test you suggest is tied to a risk and a next step.
Data reliability check: Reliable output requires complete, accurate and relevant input data; Always state that you must test the data before relying on analytics results.
Population coverage: Analytics can examine up to 100% of items; sampling examines only a part; Analytics does not guarantee all misstatements are found, because exceptions still need judgement.
Audit evidence test: Evidence must be sufficient (quantity) and appropriate (relevance + reliability); Any tool is judged by whether it improves one or both of these.
Tool answer structure: What it does → benefit → limitation → effect on audit risk; Use this four-part pattern for each technology in a written answer.
Assertion link: Drones: existence. Blockchain: occurrence, rights. AI/RPA: whole-population testing, accuracy; Link each tool to the assertion it mainly supports. These are typical links, not fixed rules.

Quick revision

CAATs are audit techniques that use computers to perform or support audit procedures.
Test data tests the client's program controls by running dummy transactions through the system.
Audit software examines the client's actual data files, for example to recalculate, sort or select items.
Test data is mainly a test of controls; audit software is mainly a substantive tool, though it can support both.
Typical audit software uses: ageing receivables, finding duplicates, finding gaps in sequences and recalculating depreciation.
Data analytics can test entire populations and highlight unusual items or patterns.
Output from any tool is only reliable if the input data is complete and accurate.
Disadvantages include cost, training needs, and the risk of changing or corrupting live client data.
Using a tool does not remove the need for auditor judgement on the results.
Emerging technologies such as AI and blockchain may change how evidence is obtained but do not change audit objectives.
In answers, always tie the tool to a specific risk or assertion in the scenario.
Objective test answers are all or nothing, so read each option's wording carefully.

Common mistakes

Confusing test data with audit software. Fix: Remember: test data puts dummy items in to test controls. Audit software takes the real data out to analyse it.
Saying CAATs replace the auditor's judgement. Fix: Say that the auditor still plans the work, designs the tests, interprets exceptions and concludes.
Saying test data and audit software are the same thing. Fix: Remember: test data tests controls in the program; audit software tests the client's data.
Listing only valid test transactions. Fix: Include invalid items such as an over-limit order or a missing field, and state the expected rejection.
Saying analytics proves there are no misstatements because it tests everything. Fix: Say analytics gives stronger evidence but results still need investigation and professional judgement.
Listing generic benefits with no link to the scenario. Fix: Name the client's data and a specific test, then state the benefit for that case.
Saying technology replaces the auditor's judgement. Fix: State that the tool supports the audit, and the auditor must evaluate its output with professional scepticism.
Claiming drones prove ownership or valuation of inventory. Fix: Say drones mainly support existence and quantity. Ownership and valuation need other procedures.

Exam tips

Match the technique to the task: data analysis points to audit software, and testing program controls points to test data.
In written answers, always tie the CAAT to a specific account, control or assertion in the scenario.
Give both advantages and disadvantages when the verb is 'evaluate' or 'discuss'.
Objective questions are all or nothing, so check the wording for 'dummy transactions' or 'analyse files' before choosing.
Show a brief answer layout in Section C: technique, procedure, benefit, limit.
Always state the purpose: test data and ITF test controls; audit software performs substantive tests on data.
In written answers, give scenario-specific examples, such as aged receivables recalculation or duplicate supplier payments.
For advantages, use points like whole population tested, speed and reuse in later years. For disadvantages, use set-up cost, skills needed, risk of corrupting live data and reliance on the right file being used.