5 Benefits of Adopting Data Analytics in Internal Audit
Data analytics is used to analyze data and find transactions that don’t fit normal patterns. These transactions may have a higher chance of causing a material misstatement or even indicate fraud. And data analytics solutions are so powerful that some auditors worry they’ll be replaced by machines.
But data analytics tools don’t take auditors out of the equation — in fact, they free them up to look at analysis results and determine when further actions should be taken, and what those actions should be. As a result, when auditors have data analytics tools at their disposal, more of their time is available for providing insight to their clients. Auditors can also offer value-added services to their clients based on audit data analytics results.
With that, let’s look at some of the top benefits auditors can expect to see after adopting data analytics.
1. Testing entire data sets
Historically, data has been analyzed by sampling a data set from traditional spreadsheets and forming conclusions based on those samples and the auditor’s knowledge of the entity.
This creates the potential for error as the entire data set is not examined. Data analytics software tests the entire data set, not just samples, allowing more thorough audits to be performed.
When conclusions are based on the auditor’s knowledge of the entity, there is the potential for error. For example, an external auditor may miss the fact that several transactions have been entered on a weekend when the entity’s business hours are only from Monday to Friday. In this case, data analytics could capture these transactions as “Unusual Days.”
2. Using data from any source
In the 2020s, accounting firms will continue to be under pressure to provide more value to their audit customers. However, it can be difficult to develop strong insights when data is spread across multiple files, systems, and solutions.
Data analytics software makes it easy to integrate data from multiple sources so auditors can run analyses quickly and efficiently, providing higher quality insights and more value to their clients.
Ideally, data analytics software also lets you easily extract data from any source.
3. Bringing data analytics into the audit workflow
The use of analytics software is not typically part of an audit workflow. Auditors often have to perform data analysis separately or rely on additional data specialists. This results in longer audits, more costs and no visibility of the tests that are performed.
Data analytics helps to simplify engagements by bringing automated testing into established audit workflows and providing useful reports for future audit evidence.
4. Artificial intelligence and machine learning applications
Analytics software uses artificial intelligence data analytics to work like human auditors. Its machine learning (ML) capabilities adapts its algorithms to provide the most accurate results based on the available data set.
By using AI and ML, analytics software can quickly and accurately examine all of the transaction and trial balance entries in an engagement’s data set, and provide meaningful results for further review. This can include tailoring the analyses to give more granular results, and to look at areas of concern that may have been identified in the initial analyses.
5. Tailored analytics
Conducting deep analysis often requires more time, and more money than most clients are willing to commit. Automated data analytics tools allow auditors to dig deeper into data without using significantly more staff time.
Fraud detection can often be difficult with traditional auditing practices due to the large amounts of available data. Data analytics allows numerous tests to be tailored based on the characteristics of each entity.
John Olley writes about data analytics solutions and their application in auditing with Caseware IDEA.
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