Integrating data analytics into your audit plan for a more efficient audit

CaseWareAnalytics, Auditing, Auditing Standards, Automation, CaseWare News, Fraud

Analytics in an audit

(Author: David Stansell, Customer Service Manager & CaseWare Cloud Analytics specialist)

Is Better Planning the Key to an Efficient Audit?

The work of an auditor today has transformed into something significantly different when compared to the auditor of 10 to 15 years ago. The levels of complexity present in the industry with compliance requirements, coupled with the ever-increasing time pressure and the requirement to do more work with fewer people, means that performing the same high-quality audit achievable in past years is becoming harder and harder.

Arguably, the best stage of an audit to find efficiency gains for the entire audit is the planning stage. Better planning leads to more efficient and targeted performance of audit work. Why is it then that the planning of audits is still conducted in roughly the same way that it has been for the past 20 years?

Audit planning as everybody knows it:

The planning of the majority of today’s audits is done on day one of the job, and is often based on the understanding and results from last year’s audit, along with a quick look through the current year financials by a senior team member. This method of planning is potentially problematical, as it opens up the gates to allow areas of risk and fraudulent activities to slip through undetected.

Audit planning as it could (should) be:

With the wealth of data that is generated at the transactional level throughout the year, planning audits based on account balances is no longer sufficient to gain an accurate understanding of the organisation. The lowest level of transactions as a whole should be analysed so that you can develop an understanding of that population of data. From there, aim to pinpoint specific areas of concern or risk or areas where efficiency improvements can save the organisation money. This raises an audit from a purely statutory exercise, adding direct value to the client’s business.

A reinvented approach to audit planning 

Data analytics is not a new concept. It has been around in various shapes and sizes over the years and has numerous applications, most of which have been around the areas of fraud and error detection in samples from a population or dataset. This is not all that data analytics can offer.

With the ever-evolving technology that is available, data analytics tools are becoming more and more powerful in both their processing power (using resources available via the cloud) and the insights that they can provide with visual representations of that data. Implementing data analytics in the planning stage of your audits will revolutionise your approach going forward.

When you are able to run an analysis across the entire population of a dataset, instead of just a sample, you may find that it is a lot simpler to prepare your audit plan because:

  • The metrics, tests and subsequent visualisations that are available within data analytics software will highlight areas of the organisation that need a closer look. 
  • It will give you an accurate understanding of the size of the organisation which in turn, will simplify the planning around resource allocation and ultimately assist you in focussing your audit on the right areas.

Audit planning combined with the power of data analytics

Analytics run early on during the planning phase of an audit, helps to ensure that you are moving down the right path from the start and could ultimately mean less wasted effort overall. Analytics can provide insights into risky areas – areas where controls are breaking down or areas where controls are causing inefficiencies.

Analytics run early in the audit process leads to:

  • better informed auditors who are acutely aware of the scale and scope of the organisation and are better equipped to ask meaningful questions to elicit the right answers
  • a shift in the relationship between the auditor and the organisation; the auditor is no longer seen as a person coming in once a year to pass judgement on the organisation, but rather someone who takes on a more business consultative role
  • auditors are more able to display a deep understanding of the organisation and its operations, and be genuinely interested in the ongoing success of the client’s business.

With effective data analysis applied at a transactional level, the auditor is now in a position to ask specific, closed-ended questions like “Your cash payments usually average at around $3,000 per month, but in August they spiked to $7,500. What happened in August?” They are also aware of the non-financial side of the transactions and can ask questions along the line of “Your payroll usually has two users actively processing transactions, in May this went to 3 and the extra user processed a payment to herself. What happened in May?”

It is this detailed understanding of the organisation that will revolutionise your audits if the information available via data analytics is sought at the start of the engagement, for effective planning and interrogation strategies. Ideally, you want to minimise surprises during the audit itself, as this might dictate extra or re-work for the engagement – however, during the audit, if a deeper, more specific look at identified areas are justified, data analytics can assist here too.

Designing a data analytics integrated audit plan

Now that the benefits of data analytics have been explained, it is prudent to point out that data analytics will bring about a significant methodology or mindset change, and can generate an overwhelming amount of information and cause untold frustration if not applied in a structured manner.

Our recommended approach for auditors starting out with data analytics is to take it slowly and start with a few key areas at a time. This approach will help you to come to terms with the concept of analytics and to get a feel for the results you can expect. Results that are raising some red flags need to get assessed as a priority and this allows you to gauge the effectiveness of the analytics product based on the results of the testing. Once you get the hang of data analytics testing you will feel more confident in applying it to other areas of the audit.

Five key areas of the audit that will serve as a good starting point are:

  • Accounts payable
  • Accounts receivable
  • General ledger
  • Payroll
  • Inventory

See Part 2 of this article for a more detailed understanding of the sorts of tests that can be applied to these data populations and transactions – all images produced in this second part of the blog are produced using our CaseWareCloud Analytics product.

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