What sort of data analytics tests might be relevant for audits?

Part 2 of a blog by David Stansell, Customer Service Manager for Caseware Australia & New Zealand. Read Part 1 of this blog here.
Please note that the images provided in this blog are all produced by our Caseware Cloud Analytics product.

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.

To find out more about our Caseware Cloud Analytics product click this link.
If you require a deeper analysis of financial data, or analysis of other datasets and transactions, our Caseware IDEA product could suit.
Contact our sales team on +61 3 9660 4680 or via sales@caseware.com.au

More blogs, associated with Analytics:
Top 3 considerations of a data analytics solution for auditIntegrating data analytics in your audit plan for a more efficient audit