Automated Tools and Techniques

1 Use in an audit

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Automated tools and techniques (ATTs) is a broad term for tools and techniques used by auditors in performing audit procedures.

The two types of ATT most relevant to the AA syllabus are:

• Audit programs
  

• Test data
  

The techniques can add greatly to audit efficiency and effectiveness. For example, audit programs can very quickly read thousands of records, examining each according to the criteria set by the auditor. Test data can be used to investigate the operation of accounting programs that could not be easily tested in any other way.

1.1 Audit programs

Audit programs (also called 'audit software') are used to examine and interrogate clients’ accounting data. The auditor will have a program which can read the clients’ files. That program can be used for the following:

• To select a sample of transactions to investigate.
  

• The samples could be automatically stratified.
  

• The program might be set to identify odd transactions or balances. For example, credit balances on an account receivable, or inventory which hasn’t moved for some time.
  

• It could also re-perform calculations. For example it is important to check that the sum of the receivables accounts add up to the balance shown in the general ledger and hence in the financial statements.
  

1.2 Test data

Test data is used to investigate the operations of client programs. The auditor designs 'dummy' data that is then processed by the client’s programs. This enables the auditor to check whether or not the client’s programs are operating correctly and as expected, and whether or not the various controls which were supposed to be present are actually operating. For example what happens if a dispatch is entered for a zero quantity or for a non-existing product or for a non-existing customer, or would raise the balance on the customer’s account to above the credit limit? Test data is specially chosen data to check that the controls are present. There would be some normal error-free items, some unusual items and some extreme or unexpected items.

The auditor should predict what the client’s program should do and then compare those predictions with what the client’s program actually produces.

A problem with test data is that the auditor is processing dummy data. Therefore it is usual for test data to be run using copies of files (i.e. 'dead').

2 Data analytics

In general terms, data analytics is “the science of examining raw data to draw conclusions”. So an obvious audit application is in analytical procedures for risk assessment and as a substantive procedure (Chapter 10).

Commonly used data analytics tools or routines include:

• Comparing the last time an item was bought with the last time it was sold, for cost/NRV purposes.
  

• Inventory ageing and how many days inventory is in stock by item.
  

• Receivables and payables ageing and the reduction in overdue debt over time by customer.
  

• Analyses of revenue trends split by product or region.
  

• Analyses of gross margins and sales, highlighting items with negative margins.
  

• Matches of orders to cash and purchases to payments.
  

• ‘Can do did do testing’ of user codes to test segregation of duties and whether any inappropriate combinations of users have been involved in processing transactions.
  

• Detailed recalculations of depreciation on an item-by-item basis using the entire data set and exact dates.
  

• Analyses of capital expenditure vs repairs and maintenance.
  

• Three-way matches between purchase/sales orders, goods received/despatched documentation and invoices.

3 Benefits and Challenges of ATTs

The main benefit of using ATTs is audit efficiency and time saving: less time spent gathering, correlating and summarising information and more time for analysing and evaluating the results or implications of the information and data.

Direct challenges to using ATTs include:

• The need to adhere to strict data privacy regulations and ensure that sensitive information is handled securely.
  

• Audit quality depending on the integrity of the data being analysed.
  

• The risk of over-reliance on the technology.
  

• The risk that algorithms or models used may produce incorrect results or interpretations.
  

• The lack of transparency in “black boxes”, where internal workings are not easily explainable.
  

Questions in the exam in this area will be of a general nature and not require knowledge of specific technologies. However, as an aid to understanding, Chapter 21 in ACCA’s Study Hub includes a brief description of the use of GenAI, Robotic Process Automation and drones in an audit.