Chapter 11: Audit sampling and automated tools and techniques

Audit Evidence

• Selecting all items (100% testing)

• Selecting specific items

• Audit sampling

Selecting all items (100% testing)

More common for substantive procedures than tests of controls

Appropriate for:

• Population with small number of high value items

• Significant risk of material misstatement and other means do not provide sufficient, appropriate audit evidence

• Repetitive calculations performed using automated tools and techniques

Selecting specific items

Not sampling as cannot be projected to the entire population

Appropriate for:

• High value or key items (eg suspicious, risky or prone to error)

• All items over a certain amount

• Items to obtain information (eg about the nature of the entity's

transactions)

• Stratification

Audit sampling

Non-statistical

• Haphazard selection

• Block selection

Statistical (free from bias)

• Random selection

• Systematic selection

• Value weighted selection (or monetary unit sampling (MUS)

monetary unit sampling (MUS)

Advantages

• The auditor can design and evaluate the sample quickly and in a cost-effective way using automated tools and techniques.

• All material items are automatically selected, ensuring all material items are tested.

Disadvantages

• Selecting the sample can be time-consuming if automated tools and techniques cannot be used to select the sample.

• MUS does not cope where there are negatively valued items in the population.

• MUS will not be effective if the population is not randomly ordered.

Sampling risk

is the risk that the auditor's conclusion, based on a sample, may be different from the conclusion if the entire population were subjected to the same audit procedure.

The auditor must determine a sample size that will reduce sampling risk to an acceptably low level.

Other factors which affect sample size include:

• Risk of material misstatement

  • If the auditor assesses the level of inherent risk and control risk to be high, then detection risk needs to be low in order to reduce audit risk to an acceptably low level.

    Detection risk includes both sampling and non-sampling risk, and in order for sampling risk to be low a larger sample size is needed.

• Required confidence level

  • This describes how confident the auditor needs to be that the sample results are representative of the population as a whole.

    The greater the degree of confidence the auditor requires, the larger the sample size needs to be.

• Expected error

  • This relates to the level of errors the auditor expects to find in the population.

    If the level of expected error is high then the sample size will need to be larger in order to make a reasonable estimate of the actual amount of the error in the population.

• Tolerable error/ misstatement (thiếu sót có thể chấp nhận được)

  • This relates to the level of error or misstatement that the auditor can accept in the population before he is concerned that there is a material misstatement.

    The lower the level of tolerable errors that can be accepted, the larger the sample size needs to be.

Evaluation of sample results

Where there are errors in the sample, the auditor should consider:

• The nature and cause of the error

• Whether the error is a 'one-off' (anomalous) error or a recurrent issue

• Whether the error effects the purpose of the audit procedure

• Whether the error affects other areas of the audit

Anomaly: "a misstatement or deviation that is demonstrably not representative of misstatements or deviations in a population" : sai sót bất thường không đại diện cho tổng thể

Tolerable misstatement: "a monetary amount set by the auditor in respect of which the auditor seeks to obtain an appropriate level of assurance that the monetary amount set by the auditor is not exceeded by the actual misstatement in the population".

Tolerable rate of deviation: "a rate of deviation from prescribed internal control procedures set by the auditor in respect of which the auditor seeks to obtain an appropriate level of assurance that the rate of deviation set by the auditor is not exceeded by the actual rate of deviation in the population"

Evaluation of sample results

Tests of details - monetary errors

Tests of controls - deviation (or error) rate

Automated tools and techniques

Automated tools and techniques: are "applications of auditing procedures using the computer as an audit tool".

Audit software (used for substantive procedures)

Audit software: consists of computer programs used by the auditor, as part of their auditing procedures, to process data of audit significance from the entity's accounting system. Audit software is used to conduct substantive procedures. tI may consist of generalised audit software or custom audit software.

Generalised audit software: allows auditors to perform tests on computer files and databases, such as reading and extracting data from a client's systems for further testing, selecting data that meets certain criteria, performing arithmetic calculations on data, facilitating audit sampling and producing documents and reports. Examples of generalised audit software are ACT and IDEA.

Custom audit software: is written by auditors for specific tasks when generalised audit software cannot be used.

• Audit software can be used to:

  • Select information

  • Read and extract data from a client's system and produce a report in a specified format

  • Perform calculations

  • Print reports in specified formats

Test data (used for tests of controls)

(a) Test data used to test specific controls ni computer programs

(b) Test transactions

Audit data analytics (ADA)

Big data: si a broad term for the larger, more complex datasets that can be held by modern computers. The term refers to a qualitative shift in the amount of data that is available in comparison with the past.

Data analytics: is the examination of data to try to identify patterns, trends or correlations. As the quantity of data has increased, it has become more and more necessary to evolve ways of processing and making sense of it.

The use of data analytics software will initially involve significant costs on the part of the auditor and extensive training, however it could offer auditors the ability to examine all of an entity's data and test entire populations. This in turn should improve both audit efficiency and audit quality. Large quantities of data can be interrogated relatively quickly, allowing auditors to focus immediately on the riskiest areas, and thus obtain evidence to reduce audit risk.

Examples of how auditors might use data analytics include:

• Analyse patterns relating to revenue or costs per product or per customer

• Trace the matching of orders to goods despatched/goods received documentation and ot the invoice, in order ot determine whether revenue and costs should be recognised

• Interrogate journals to determine whether there are any patterns (regarding who has processed certain journals) where fraud is suspected

• To calculate inventory ageing and how many days inventory is in stock by item

• Detailed recalculations of depreciation on non-current assets by item