Auditing with data analytics and IDEA software

19 February 2021

Our senior auditor Luke explores the increasingly popular use of auditing with data analytics

Data analytics in auditing is becoming more and more commonplace. To understand this, first we look back at the history of audit.

Traditional internal auditing

Auditing – as a concept – can be traced back to early civilisations.

In ancient Egypt, cargo being brought off ships was independently checked and reported to the authorities.

Over time, the internal audit function has adapted and evolved as a result of changes in the size and functions of organisations, legislation and new technology.

From the 1970s it was common practice for internal auditors to take a risk based approach to testing key controls within an organisation.

This usually involved looking at a sample of transactions or data points.

Data analytics and audit

In recent years, internal auditors have moved away from taking small samples of data. Instead they now leverage the power of data analytics.

This has enabled auditors to analyse 100% of data sets quickly and efficiently. It increases the audit coverage and reliability of the audit conclusions.

Data analytics also enables a greater focus on strategic risks by moving the audit focus away from spending time on routine tests.

Veritau’s IDEA group

Veritau has been using the specialist data analytics software IDEA for a number of years. IDEA allows a trained user to analyse large and complex sets of data.

Our IDEA group are experts in the software. We engage in proactive data matching exercises to detect possible fraud and error, identify data quality issues, support fraud investigations and provide more effective audit assurance.

Our auditors have used IDEA to support many areas of testing, including:

  • Identifying duplicate benefit claims and invoices.
  • Matching staff establishment payroll records to active users on IT systems.
  • Identifying benefit claimants who have previously written-off debts.
  • Checking staff have paid the correct pension contributions.
  • Identifying fuel cards being used with vehicles they are not registered to.

Use of data analytics can be hindered by poor data quality and the inability to extract data from the necessary systems.

But as more organisations become familiar with these techniques, the challenges become easier to overcome.

Many data analytical tools such as IDEA now have inbuilt functions to enable scripts to be saved and for data to be continually audited.

Real-time auditing offers the ability to spot errors and inconsistencies much more quickly hence limiting their potential impact.

IDEA case study on Covid grants fraud

Covid grants fraud: IDEA case study

Read our case study on using data analytics to find fraud and error in Covid grants system.

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