Data analytics is used to analyse data and, among other reasons, 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 can be 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 provide auditors with the very important benefit of time; auditors now have the additional time to look their 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, as opposed to crunching numbers. 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 datasets
Historically, data has been analysed 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, which increases the likelihood of potentially fraudulent outlier entries being missed. Data analytics software tests the entire dataset, not just samples, allowing more thorough audits to be performed.
Likewise, 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
Throughout the 2020s, accounting firms will be under continued 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, such as IDEA Data Analysis, makes it easy to extract data from multiple sources and integrate it into a single dataset, so auditors can run analyses quickly and efficiently, providing higher quality insights and more value to their clients.
3. Bringing data analytics into the audit workflow
Until recently, the use of analytics software has not typically been part of the standard audit workflow. Auditors often had to perform data analysis separately or rely on additional data specialists and prepare their datasets for analysis. This results in longer audits runtimes, with higher costs and zero visibility of the tests that are performed.
More and more companies are using data analytics to help 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 each time to provide accurate results based on the available and past 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 dataset, providing 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
Adopting data analytics often requires an initial outlay of time and money than most clients are unwilling to commit. But it is important to remember that, once fully trained and familiar with the software, automated data analytics tools allow auditors to dig deeper into data without using more staff time, as this time is saved by the speed of the software’s outputs.
Fraud detection can often be difficult with traditional auditing practices due to the large amounts of available data. By analysing 100% of a given dataset, data analytics allows numerous tests to be tailored based on the characteristics of each entity.
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