Data analytics in the audit world

Artificial Intelligence (AI) and other new technologies are starting to become mainstream in business and everyday life today, and the audit world is no different. From identifying new business opportunities, to assessing potential cost savings, and even working out when your finance team is doing their work, data analytics (DA) offers endless possibilities.

What does “data analytics” actually mean?

Data analytics is the process of examining data and drawing conclusions as to what that data shows. Whilst the underlying concept isn’t new – data has been reviewed and assessed ever since data first started to be collected – technological developments now mean that this process can be done more efficiently than ever. Specialist software can analyse data from diverse sources (internal and external) and different formats (quantitative and qualitative).

What are the benefits of data analytics?

Well, it depends! The benefit from DA depends on the business using it.

Some uses can be quite straightforward – for example, a business can use DA to produce monthly KPIs which sit alongside their management accounts pack. Other uses can be more complex, such as using DA to analyse future & historic market trends which could influence decisions over product development and manufacturing.

DA can also be used to analyse the granular detail of transactions, when applied to full sets of business data. This could include, for example, identifying who in the team is posting certain transactions, when bank account details are changed for suppliers or flagging up when transactions are edited, which can help to identify or deter potential fraud. Ultimately, DA can be used to support a business to make more informed and timely decisions.

How does this affect an audit?

Auditors can use DA software to extract and manipulate business data, and to analyse it more effectively. This process can help auditors better understand their client’s business and the risks to the audit, which leads to a higher quality process.

A couple of specific examples of DA in audit include: –

  • Stock – using DA to analyse the transaction history of specific SKUs, such as the last time certain items were bought and sold, and at what price.

This can make the auditor’s stock work more efficient, by enabling the auditor to process data about large volumes of stock in a short space of time. It can also provide management with insights into this part of the business which could, in turn, support future business decisions around stock; and

  • Segregation of duties – a common ‘internal control’ is the segregation of duties within the finance team, which reduces the risk of fraud or error occurring.

DA can enable the auditor to review who in the finance team is processing and reviewing transactions on the system, which can help to identify instances where controls are not acting effectively. This supports future control recommendations which can help the business to strengthen its control environment, which supports better decision making at senior levels.

Author, Jonathan Delaney Senior Manager

So will auditors just be replaced by data analytics software then?

Not quite! Whilst DA offers so much when it comes to extracting and analysing data, the key to successfully using the tools is to draw appropriate conclusions and make the right recommendations. This is where the auditor comes in.

As well as doing the ‘ticking and bashing’, the auditor is also able to interpret what the numbers say and to communicate this, and any appropriate recommendations, to management. Ultimately, it’s not just about the numbers, it’s also about making the numbers talk. Whilst this skill remains out of reach of the likes of ChatGPT*, the auditor won’t be replaced just yet!

*other AI tools are available!

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