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? 

 

Authored by David Stansell, Customer Service Manager & CaseWare Cloud Analytics specialist 

   

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.  

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?” 

These questions allow the auditor to understand the business more and ask better questions. 

 

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 

   

Deciding what transactions to examine, and 

what to look for can be a challenge Avoid generating an overwhelming amount of information and increased frustration levels, especially if you are new to conducting analytics on transactions. 

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 

 

1. Data Analytics for Accounts Payable Data 

Although suppliers will normally raise any problems regarding Accounts Payable, it is often important to confirm whether or not liabilities are being understated or suppressed. 

● Summarize invoices by supplier to prove individual balances 

● Create activity summaries by supplier ● Total posted invoices for the year for accurate vendor 

rebates ● Calculate days in Accounts Payable and average days for 

invoices to be paid ● Test for duplicate payments/invoices, bank account 

details, POs, invoice payments, or freight and tax charges. 

  

2. Data Analytics for Accounts Receivable Data 

Tests of Accounts Receivable or the Sales Ledger are usually tests of validity. Items of particular concern are old invoices, unmatched cash and large balances, particularly where customers are in financial stress. These can all be identified with exception tests. 

● Profile debtors using Stratification to see how many large debts there are and what proportion of value is in the larger items 

● Analyze average sales amount by customer, sales representative, product, region, etc. 

● Produce an aged debt analysis (consider how to deal with unallocated cash and credit notes) 

● Report credit balances ● Identify duplicate invoices (both invoice number and 

customer/ value), credits or receipts, in any order. 

 

3. Data Analytics for General Ledger Data 

General or Nominal Ledgers contain balances for each account together with transaction history and various references and descriptions. 

● Provide totals of entries generated by different sources (e.g., purchase or sales ledger, journal vouchers, etc.) to show the volume and value 

● Analyze year-to-date activity for large operating accounts ● Total transactions by account to prove the trial balance ● Test for transactions with dates outside the posting month 

or year (cutoff); duplicate postings ● Compare balances with previous periods, budgets or 

management accounts to show variances and fluctuations. 

4. Data Analytics for Payroll Data 

Payroll is one of the traditional audit areas applicable to most organizations and an excellent area to use data analysis software. The main objective is validity. 

● Summarize/stratify salaries by department/grade ● Analyze costs for special pay, overtime, premiums, etc ● Sort employees by name and store to identify conflicts-of 

interest where managers have relatives working for them ● Gross pay, hourly rates, salary amounts, exemptions ● Extract all payroll checks where the gross amount exceeds 

the set amount. 

5. Data Analytics for Inventory Data 

Inventory and stock can vary in volume and cost within organizations, so it’s a worthwhile area to perform some testing. 

● Reconcile physical counts to computed amounts ● Analyze usage and ordering to improve turnover; analyze 

high-value transactions ● Statistically analyze usage and ordering to improve 

turnover ● Identify surplus obsolete/damaged inventory by sorted 

turnover analysis; differences between standard and actual costs; stock acquired from group companie 

● Monetary Unit or Random samples for physical verification or checking additions. 

NOTE: All of the graphs provided over the last few pages have been generated using CaseWare Cloud Analytics (™). 

 

 

Talk to us about our analytics for audit solutions: 

Desktop: CaseWare IDEA 

Cloud: CaseWare Cloud Analytics 

Or join us for an online product demonstration 

CaseWare Australia & New Zealand Unit 2A, 12 Hoddle Street ABBOTSFORD VIC 3067 

 www.caseware.com.au 

+61 3 9660 4680