Data Analytics in Auditing: Opportunities and Challenges

Data analysis looks for transactions that do not follow the expected patterns. These transactions might be more likely to result in a substantial misrepresentation or a sign of fraud. Additionally, some auditors are concerned that computers will replace them due to the strength of data analytics tools.   

However, data analytics tools do not eliminate the need for auditors; instead, they provide them more time to review analysis findings and choose when and how to take additional measures. As a result, when auditors have access to data analytics technologies, they have more time to devote to giving their clients insight. Based on the findings of audit data analytics, auditors can also provide value-added services to their clients.   

Now that that has been said let us look at some of the benefits of data analytics in auditing.    

Why is Data Analytics Important in Audit? 

Here are some advantages that auditors can anticipate after implementing data analytics.   

Improved Risk Management 

Enhancing risk management across a company is among the top advantages of employing advanced data analytics for internal audits. If you want to evaluate accounting procedures to find financial hazards, IT records to find cybersecurity threats, or anything else; you cannot usually check all the data by hand.   

Risk management gaps may result when the auditor substitutes constrained data sampling techniques for what would otherwise be information overload. A whole data set may contain unrecognized but significant outliers. By swiftly reviewing vast amounts of data, you can identify and comprehend these risks using data analytics for internal audits.   

Testing Full Data Sets 

Historically, data analysis has consisted of taking a sample of a data set from conventional spreadsheets and drawing conclusions using the auditor’s familiarity with the entity and the sample data.   

As the complete data set needs to be evaluated, there is a chance for accuracy. The use of data analytics software enables more complete audits by testing the entire data set rather than simply samples.  

There is a chance for inaccuracy when judgments are made based on the auditor’s familiarity with the entity. When a corporation exclusively conducts business from Monday through Friday, an external auditor can overlook that multiple transactions were entered over the weekend. Data analytics could identify these transactions as “Unusual Days” in this situation. 

Using Any Data Source 

Accounting companies will face increased pressure to add value for their audit clients in the 2020s. However, it can take time to develop solid insights when data is dispersed over multiple files, systems, and solutions. 

Data analytics software makes combining data from many sources simple, enabling auditors to execute analyses fast and effectively and offer their clients higher-quality insights. 

Data extraction from any source should be made simple by data analytics software.   

Improved Reporting 

Additionally, data analytics can be leveraged to provide more understandable, powerful reports. Your audit function can develop data visuals, such as charts and graphs, that concisely explain audit findings by employing audit analytics software.  

Internal auditors may use long tables and lengthy explanations that overwhelm the audience without data analytics. However, using data analytics, you may provide understandable reports for senior management, the audit committee, or other stakeholders, allowing them to make the most of presentations. 

Custom Analytics 

Deep study frequently takes more time and money than most clients are ready to invest. Auditors can examine data in greater detail with automated data analytics technologies without considerably increasing personnel workload. 

Due to the abundance of data accessible, fraud detection can frequently be challenging with standard auditing approaches. Numerous tests can be customized using data analytics based on the traits of each entity.  

Improved Audit Quality 

Overall, data analytics can assist in raising audit quality at each stage of the procedure, which raises audit quality overall. Internal auditors can employ data analytics to effectively understand their work and work with other stakeholders on anything from audit planning to testing to reporting.  

More specifically, data analytics can be utilized to undertake audit procedures like Benford’s testing, stratification, Monetary Unit Sampling, and gap and duplicate identification more methodically and effectively. 

Better Customer Service 

When working with customers, auditors must always adhere to the standards of independence and professionalism first. The use of data analytics can provide benefits above and beyond the conventional audit of historical financial statements, provided that these requirements are taken into account effectively.  

Audit data analytics can offer excellent chances to add more knowledge to evaluating risks and controls. Communication with customers is improved as issues are brought up early in the audit process. Clients can see how their regular data is reviewed, allowing for a fresh viewpoint and the chance to comprehend their data from a different angle.  

Challenges of Data Analytics in Auditing 

There will be challenges in switching to an integrated audit using data analytics and artificial intelligence. Several barriers need to be addressed first. 

Data Extraction 

Auditors must be able to swiftly and affordably retrieve their clients’ data before performing analytics in an audit.   

However, given the recent spate of data breach incidents, it has become increasingly important for organizations to invest in data protection. Organizations must preserve their data through multiple-layer permission procedures and technological measures.  

As a result, getting the client’s consent to provide the auditors’ access to their data can take time and effort. In the 3V era, auditors (and their customers) will also need to switch from extracting and removing data to being granted extensive access to data warehouses locally. 

Skills Gap 

The integration of data analytics and artificial intelligence will be practical only when it affects the audit’s nature, breadth, and extent. As a result, auditors will need to learn new skills centred on knowing what questions to ask about the data and how to apply analytics to generate audit evidence, reach audit findings, and derive valuable business insights. 

Integrating data analytics and artificial intelligence into the audit will require a ground-up initiative to better comprehend and influence the educational programs in universities and colleges, boosting learning and development programs within the audit firm and instituting effective implementation programs to support audit teams.   

Adherence To Auditing Guidelines and Criteria 

The norms and laws regulating the auditing industry were created long before Big Data was ever a thing. However, before the auditing standards and regulations can truly be aligned with data analytics, standard setters, like all of us, are playing catch-up in understanding and considering the relevant factors and feedback from various stakeholders. This is true even though ongoing efforts are made to maintain the significance of the standards and regulations through amendments and the introduction of new standards.   

Multiple Accounting Systems 

When it comes to data extraction, the wide variety of accounting systems that auditors frequently deal with within their client base and, often, the multiple accounting systems used by the same business strategy can be intimidating.  

Obtaining data from unknown accounting systems may take time and effort. Data collection consequently necessitates a lot of tries and back-and-forth interactions between the company and the auditor.  

Currently, only accounting ledger data can often be extracted. However, utilizing big data will require acquiring both structured and unstructured data, financial and non-financial, to support the audit. This makes data extraction and data mapping more complex. 

In Conclusion- 

The primary function of auditors will remain static; however, how audits are conducted in the future will differ significantly from how they are done now, and the subjects of those audits will change over time. Larger data sets and analytics will allow auditors to understand the business better, pinpoint significant risk areas, and provide higher quality and coverage while adding more value to the company.  

However, the profession must collaborate closely with critical stakeholders, from the companies they audit to the regulators and standard setters, to achieve this change. The data analytics solutions provided by SG Analytics are extensive, powerful, and simple to use. Discover how it can assist you in consistently producing high-quality audits. 

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