How to Get Started with Audit Data Analytics

Forward-thinking firms are adopting data analytics tools into their audit processes. Audit data analytics help firms deliver a high-quality audit, control audit costs, and create better conversations with clients. But many firms could use a little help getting started with audit analytics. Where and how should you use analytics? What’s the secret to finding the right analytics tool? What about training?

To answer these questions and more, Wolters Kluwer’s Colleen Knuff and Stefan Davis sat down with Michael Holmstrom, a Senior Manager at Marks Paneth. They discussed what it takes to get started with audit data analytics and how professionals at Marks Paneth leverage the TeamMate® Analytics tool in their audit process.

Watch the On-Demand Webinar to hear the complete conversation. This article will summarize some key points from the webinar.

Why do Firms Need Audit Data Analytics?

Firms can use analytics to address many common challenges. Today, clients view audits as a commodity and they are exerting downward pressure on audit fees. Audit analytics can help auditors perform more comprehensive testing in less time, helping to hold down the cost of an audit even while increasing the quality of the audit.

At the same time, clients are asking firms to help them wrap their arms around a growing volume of corporate data. Clients want to consult with firms that can help them derive actionable intelligence from their data—insights that will help them grow their businesses. So, good audit data analytics can help firms expand their consulting opportunities and better satisfy clients.

What Areas of an Audit Will Benefit From Analytics?

When professionals think about data analytics, most consider things like extracting samples or creating visualizations. But that thinking is too narrow; data analytics can be applied throughout the entire audit. Use data analytics to inform planning, test against full data sets, spot hidden risks, and add value to reports. Here are a few good areas for application:

Risk assessment and planning: Analytics can help ensure you’ve identified the areas of greatest risk and focused your testing plan on those areas. Consider using trend analysis and predictive analytics to assess how the business is performing compared to the expectation.

Getting away from sampling: Use analytics to replace sampling with 100% testing. Sample sizes keep getting bigger as the volume of client transactions grows. It’s becoming less efficient to do sampling and analytics can test 100% of data more quickly than sampling. When sampling is unavoidable, audit analytics can help auditors efficiently select a robust sample while minimizing the sample size needed. One example of that is using monetary unit sampling because that can often give a smaller sample size, yet greater coverage.

Substantive tests of details: Audit analytics make it easier to corroborate or cross-check pieces of client data against one another quickly. An example would be making sure an order matches the invoice and matches the goods receipt. Auditors can check this across a huge amount of transactions running through the business. Auditors can also look at year-over-year trends, for example, sales by customer, and use substantive analytics to find anomalies with significant deviations.

Internal controls testing: Auditors can use analytics to validate the application of controls for every instance. Instead of sampling 30 instances of a control that has happened 2000 times over a year, auditors can test quickly at every application to ensure controls operated effectively.

Spotting red flags of fraud: Analytics can help the auditor look for instances of fraud, whether it's payroll fraud, accounting fraud, or any other fraud as driven by your audit risk assessment. Use risk indicators to pull out the riskiest transactions from journal entries and do a deeper analysis.