Automated Tools and Techniques

1 Introduction

Even very small businesses will usually maintain their accounting records on computer. There are many advantages to this, not least that trial balances will usually balance and control accounts will reconcile to the underlying detailed records. However, the reduced amount of handwritten data and documents can make auditing more difficult. For example, it can be difficult to test whether a computer is carrying out a procedure correctly and it can be more difficult to ‘see’ and examine the information and records than in a manual system.

Automated tools and techniques (ATTs), formerly often called computer-assisted audit techniques (CAATs), assist auditors with computerised records. They include audit software, test data and data analytics tools.

2 Types of automated tools and techniques

2.1 Audit software

Audit software (or audit programs) is software developed and used by auditors. Audit software allows clients’ accounting data files to be read and examined.

The processes carried out by the auditor’s software commonly include:

• Adding up the records. For example, inventory values and receivables balances. The totals are the amounts that should appear in the statement of financial position.
  

• Performing calculations for analytical procedures.
  

• Identifying and printing details of unusual items for further investigation, such as credit balances on a receivables ledger or negative inventory balances.
  

• Picking samples. For example, audit software can be programmed to create a stratified sample or a pure random sample.
  

• Picking all items with particular characteristics, such as all sales orders approved by a certain employee.
  

Once it is set up, audit software can quickly, efficiently and economically examine every item on a data file. This would often be difficult or impossible if attempted manually. It can greatly speed up audit procedures and reduce costs.

Audit software can test whole populations quickly and consistently, select unusual items and repeat routines. Its disadvantages include purchase and set-up costs, training, compatibility with the client’s files and obtaining secure access. Incomplete or unreliable input data produces unreliable results; exceptions still need investigation and judgment.

2.2 Test data

Test data is auditor’s data that is operated on by client’s program. It is used to test the workings and resilience of programs.

The results produced by client programs are compared with predictions of what should happen and any discrepancies are investigated.

Test data is designed to:

• Test that calculations are carried out correctly by client software. For example, enter time sheet data of 50 hours worked and ensure that the correct wages and tax are calculated.
  

• Test that programmed controls and procedures are carried out correctly. For example, if a client’s system should reject orders from customers over their credit limit, test that such orders are indeed rejected by entering an order that should be rejected.
  

• Test how resilient software is against input errors. For example, test what happens if an account number is entered incorrectly, or a negative amount of goods is ordered, or an impossible date is entered.
  

Test data will therefore include normal error-free items, some unusual items and some extreme or unexpected items. Test data will therefore usually be 'dummy' data rather than actual data. Also, because of the risk of corrupting the client's records with such data, it will usually be processed 'dead' (ie using copies of files) rather than live.

Test data directly checks programmed calculations and controls and can deliberately include unusual errors. Designing expected results takes time and expertise; the tests cover only the cases and program version tested. A separate test system might differ from the live system, while live testing risks corrupting records unless dummy transactions are removed. Re-test changed programs and protect client data.

3 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.

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.
  

Advantages of the use of data analytics in audit include:

• Productivity and cost savings – as analysis is automated, the auditor can focus on interpretation;
  

• Ability to perform far more extensive audit procedures, improving audit quality;
  

• Reduced audit risk – entire populations can be subjected to broad ranges of test procedures and 100% testing increases confidence in audit procedures;
  

However, there are also disadvantages, for example:

• 100% testing still only provides reasonable assurance;
  

• It cannot replace the need for professional judgment (e.g. in assessing management’s judgments in making accounting estimates);
  

• Increased risk of overconfidence in technology where results are not infallible.