A crucial aspect of the work of a certified public accountant (CPA) who works as an auditor is to grasp and analyze risk and control data to understand and opine on the risk profile. Data assessment is historically viewed as an individual activity – inconsistent, conducted ad-hoc, or by specialists with cutting-edge toolsets.
Audit data analytics is used to examine data and, among other reasons, identify transactions that do not follow the normal patterns. These transactions might have a greater chance of resulting in a material misstatement or even point to fraud.
“We are stepping into an era of large-scale transformation in auditing. The use of audit analytics for evidential and information purposes is just the beginning.”
As the growth of business and transactional data prevails, traditional auditing methods cannot keep pace with the data inflow. Operational and financial transactions are increasingly going digital, stretching the range of variables to examine, outliers to recognize, and trends and patterns to keep tabs on.
Leading-edge audit analytics helps mine massive datasets to provide smaller subsets of substantial data for the CPAs to analyze, enhancing audit quality as well as the value of business insights CPAs can offer.
Benefits of Audit Analytics
When CPAs have data analytics tools at their disposal, much of their time is available for delivering insight to their customers, rather than crunching numbers. Also, CPAs can provide value-added services to their customers as per the audit analytics outcomes.
In that spirit, let’s delve into some of the key benefits CPAs are likely to see after including data analytics in their arsenal.
Streamline Traditional Audits
Until now, the application of analytics has not generally been a part of the conventional audit workflow. CPAs usually had to do data analysis independently or depend on additional data experts and prepare their datasets for examination. This leads to longer audit runtimes, with greater costs and zero transparency of the analysis performed.
An increasing number of businesses are leveraging audit analytics to help streamline engagements by bringing automated analysis into traditional audit workflows and delivering crucial reports for future audit evidence.
Analyze Entire Datasets
Traditionally, data has been analyzed by sampling a dataset from conventional spreadsheets and drawing conclusions according to those samples and the auditor’s understanding of the entity.
This leads to the possibility for misstatement as the whole dataset is not assessed, increasing the odds of overlooking the potentially deceitful outlier entries. Audit analytics tests the whole dataset, not only samples, enabling more comprehensive audits to be carried out.
Similarly, when conclusions are drawn from the auditor’s understanding of the entity, there is the possibility of misstatement. Case in point, an external auditor might skip the fact that numerous transactions have been inputted on a weekend when the entity’s working hours are only from Monday through Saturday. In such a case, audit analytics can record these transactions as “Unusual Days.”
Tailor-made Analytics
Adopting audit analytics generally requires an initial investment of money and time than most customers are willing to commit.
However, once completely trained and cognizant of the software, audit analytics enables CPAs to take a deeper dive into data without using more man-hours, as this time is saved by the pace of the software’s outputs.
Often, fraud detection can be tough with conventional audit workflows due to the huge chunks of data available. By analyzing 100% of a specific dataset, audit analytics enables several examinations to be tailored as per the characteristics of each entity.
Leverage Data from Any Source
Throughout the coming decade, accounting companies will be under constant pressure to deliver more value to their audit clientele. Despite that, developing critical insights can be hard when data is spread across several systems, files, and solutions.
Audit analytics software makes it simple to pull out data from several sources and incorporate it in a single dataset, so CPAs can analyze fast and effectively, offering better quality insights and more value to their customers.
AI and ML Add another Feather to the Cap
Audit analytics software leverages artificial intelligence (AI) data analytics to mimic human CPAs. Its machine learning (ML) capabilities adjust to its algorithms each time to deliver precise results as per the existing and previous dataset.
With AI and ML, audit analytics can rapidly and accurately analyze all of the transactions and trial balance entries in an engagement’s dataset, delivering substantial results for further reviews.
This can consist of tweaking the assessments to offer more granular outcomes and inspecting areas of concern that might have been recognized in the initial assessments.
No More Humans at Work?
Audit Analytics can be so strong that some CPAs anxious they will be replaced by machines. It, however, does not take CPAs out of the equation – indeed, it offers CPAs the quintessential benefit of time.
Now, CPAs have the extra time to monitor their analyses’ results and figure out when further steps must be taken, and what those steps should be.
Perhaps, a bigger challenge is preparing more-adept professionals to apply audit analytics when they have been doing sampling for the timeframe of their careers. To deliver the high-grade service possible, CPAs will have to learn to apply analytics tools.
Companies can achieve this by re-skilling their existing audit workforce. For independent auditors, becoming an expert in data analytics might require enrolling in classes and online courses. Moreover, companies that plan to shift towards advanced audit analytics must recruit individuals with advanced analytical capabilities.
Conclusion
With the ever-growing data, audit analytics has become a must for CPA organizations. They can do much more than simply filing taxes and running numbers. They can help enterprises grow, project future industry opportunities, alert them about deceptive transactions, and so on.
Further, even as audits get more automated, human auditors will continue to play an instrumental, if considerably different, role. Some will analyze high-scale results of automated and semi-automated audits. Others will engage closely with innovative audit analytics and automation systems.
And they will, no doubt, continue to provide value by examining vital assumptions, properly contradicting conclusions with professional skepticism, and adding value to the entire financial reporting process.
