Audits in Accounting: Improving Audit Quality with Data Analytics
Auditing is crucial to ensuring the accuracy and fairness of financial information; however, one of the biggest threats to the audit field‚Äôs reputation is data quality. This is due to the large volumes of data that businesses produce these days. In order to manage the vast quantity of data, auditors are turning to data analytics.
Data Analytics and Audits
Technology plays a major role in business growth as it aids in reducing operational costs and improving customer service. As such, many businesses have adopted enterprise resource planning (ERP) systems. These systems produce huge volumes of data, making it nearly impossible to analyze using the traditional audit process.
Auditors are left with no choice but to also use IT-based solutions, which has led to the development of audit software to support the auditors in data extraction and analysis. To further enhance the workings of audit software, the programs are being integrated with data analytics. Since data analytics works with structured data, the systems incorporate machine learning (deep learning) to extract useful data from a host of unstructured data.
Although these developments in the audit profession have not changed the primary role of auditors, they have changed how an audit is done by helping produce high quality audit evidence.
How Data Analytics Improves Audit Quality
Traditional audits involve combing through piles of data, which is time consuming. As a result, auditors prefer workarounds like data sampling, which does not give a true outcome. With data analytics, an auditor does not have to restrict data to financial reporting systems only. Instead, they can use data from multiple complete data sets, such as sales statistical data and employee and customer master data. This enables an auditor to go beyond traditional audits that target limited data and include different audit-relevant data.
Using data analytics tools, auditors look for predefined patterns that help reveal ambiguous relationships between variables that a manual system might not identify. This helps facilitate a more comprehensive decision that includes all data sets.
By integrating data analytics, auditors have access to a powerful tool that helps them better understand a business. As a result, they can easily identify key audit risks, provide deeper insights into a business‚Äô systems and controls, detect fraud, and provide value in a less costly manner.
Apart from simplifying and speeding up the audit process, data analytics also enables auditors to focus on key risks. The capabilities of data analytics continue to evolve to the point of automating the auditing process through advanced data analytics (ADA). This enables the automation of routine audit processes, allowing the auditors more time for matters that require professional judgment.
Challenges of Audit Data Analytics
Audit data analytics is not without its challenges, though. One obstacle is data exchange between a business and an auditor, whether internal or external. This is in relation to different systems used in data collection. To handle this challenge, the AICPA introduced data standards to be used for data requests and to ensure the production of standard reports from the ERP systems.
Another challenge is the integrity of data fed into the analytics systems, as this determines the quality of the end results. Systems used should be designed around collecting meaningful data. Auditors must also ensure that the conclusions fed into the systems are accurate and correct.¬†
Although data analytics reduces the sampling risk, it introduces the challenge of dealing with exceptions, mostly referred to as outliers, that produce results outside audit expectations. This calls for auditors to investigate the exceptions/outliers to determine if they are errors or misstatements.
Integrating data analytics into the audit process greatly improves audit quality and credibility. With rapid advancements in technology, the capabilities of data analytics will continue to evolve, making auditing work even more efficient while maintaining high quality.
Although the adoption of data analytics is dependent on the size of a business, the availability of skilled staff is also crucial. To remain relevant in a fast-changing environment, auditors need to advance their skills to effectively use the data analytics tools.
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