source: cio.com

(Found the paper I wrote for AAS’s pre-departure training assignment when tidying up my computer files and decided to post it here since why not. Hehe. Pritania Astari and Nadhya Fitri peer-reviewed and Barbara Wiechecki a.k.a the best tutor (*wink) final-reviewed it.)

In today’s world, there is a popular refrain “The world’s most valuable resource is no longer oil, but data.” (The Economist, 2017). Back in 2013, Deloitte published a report titled “data is the new gold” to support the idea about the importance of data for organisations. It is not surprising that now many organisations regard data as a strategic asset. With the growing awareness of the significance of data for organizations, arises the demand for auditors to sustain their relevance by utilising data analytics techniques. Data analytics involves the analysis of the entire sets of data to identify anomalies and trends to provide audit evidence as well as insight for further investigation. This process usually incorporates an analysis of overall populations of data, rather than the widely-used approach of only inspecting a small sample of the data (Bragg, 2019). With that qualification, no wonder data analytics is considered one of the most important technological advancements that should be implemented in the auditing process.

Data analytics aids both internal and external auditors with the ability to look into a large number of data, conclude, and acquire insights immediately. To support an organization’s data-driven decision-making, internal auditors use various data analytics techniques in their audit engagements. To examine the quality of financial reports, data analytics can be a powerful tool for external auditors. However, for many audit organizations, the plan to adopt data analytics encounters both support and resistance. This paper will address both sides of the arguments around the utilisation of data analytics for auditors. First, the opportunities of the implementation will be discussed. Subsequently, this essay will also analyse the challenges of the utilisation. Finally, it will propose whether audit activities should stick with the traditional audit or a change is needed.

One of the main reasons data analytics is needed in audit activities is that it increases audit quality. A survey conducted by the Institute of Chartered Accountants in England and Wales revealed that around 70% of senior audit practitioners believed data analytics improve audit quality (ICAEW in renaix.com, 2016). Faster data analysis not only assists auditors to obtain timely results but also significantly helps them widen the audit scope. Timely assurance helps the organisation to recognise opportunities and problems immediately. In addition, data analytics keeps track of all the data by analysing the population so the overall assurance could be provided. As a result, examining the organisation’s whole data universe, instead of the sample, in a timely manner generates a better audit quality.

Furthermore, the proper use of analytics will increase the efficiency and effectiveness of the audit process. The auditor does not need to check each of the data since the analytics process will do it instead. The auditor can therefore pay more attention to the more risky and/or critical areas. 

An additional reason is, data analytics is a powerful tool to detect fraud and mitigate risk. Data analytics can offer unique and valuable insights regarding the client’s risk and control environment by scrutiny the details which might otherwise be overlooked in manual sampling techniques in traditional audits (Geat and Xie, 2017). With the continuous audit technique powered by data analytics, fraud and risk could be recognised immediately. In a world where fraud and risk could destroy an organisation overnight, an audit process that can keep a keen eye on those things is critical for the organisation.

Lastly, data analytics can offer predictive analytics-based recommendations. Predictive analytics encompasses various analytical and statistical techniques for establishing innovative methods for future forecasting (Selvaraj and Maruppada, 2018). With data analytics, auditors can predict the future of an organisation based on past data and deliver the results to improve the organisation’s next moves. Predictive analysis-based recommendations are needed as in a world full of rivalries like today, the ability to predict the future is a competitive advantage every organisation aims to have.

On the other hand, data analytics implementation for audit activities may find many challenges like a lack of support from the key stakeholders. The executives may not be enthusiastic about the change for many reasons including their incomplete understanding of the techniques. In addition, data owners may be resistant to giving the auditor access to their database because of the assumption that the access will disturb their ongoing operation. Considering those things, it is argued that it is difficult to properly perform data analytics for the audit process. In line with those arguments, Gorgi et al. (2016) stated that auditees want to maintain their data integrity. They are concerned that the data analytics process performed by auditors may corrupt or alter the data. More than that, many organisations worry that when auditors are exporting company data for audit purposes, data security breaches (i.e. access by unauthorised party to the data) may happen.

Moreover, the technical issues around the analytics process are difficult to be ignored. As mentioned previously, access to the database means it is vulnerable to privacy breaches and data security issues. The data owner needs to be convinced of the auditor’s ability to keep the data safe. The other technical issues that will be faced by the auditors include data quality issues, analysis of unstructured data, and understanding data relations. In the US, for example, 63% of entities use data analytics to support their auditing process, but only 28% expressed that the data was of high quality. (consultancy.uk, 2018). These issues need to be addressed by the auditors before they decided to utilise data analytics for their operation.

Lastly, data analytics implementation demands huge investment. While a lot of organisations want to spend money to keep up with the technology advancement, it is argued that they want to spend more on the utilisation of data analytics. Data analytics-related resources such as supporting infrastructure, application, and training are known to be expensive. With a lack of understanding of the benefits gained by the utilisation of data analytics, the executives will not approve the spending related to it.

The debate over whether data analytics should be implemented to assist auditors faces both opportunities and challenges. Some believe that it is the right time to start capitalising on the potential of implementing data analytics while others reject the idea or at least, point out that it could be implemented later under more satisfactory circumstances. However, while many challenges need to be addressed, such as by providing clear and persuasive information to the executives and data owners, it is believed that the opportunities outweigh them. That is why auditors are advised to implement data analytics while at the same time solving problems around the utilisation. 

REFERENCES

Bragg, Steven. (2019). Audit Data Analytics Definition. Available at https://www.accountingtools.com/articles/2019/5/9/audit-data-analytics (Accessed in March 21st, 2021)

Consultancy.uk. (2018). Data Analytics to Become A Game Changer for Internal Audit. Available at https://www.consultancy.uk/news/16863/data-analytics-to-become-a-game-changer-for-internal-audit (Accessed in March 21st, 2021)

Deloitte. (2013). The Analytics Advantage Report. United Kingdom: Deloitte Network.

Geat, Kang Wai and Zoey Xie. (2017). Data Analytics  A Boon For Auditors. Illinois: ISACA

Gorgi, Juli-ann et al. (2016). Audit Data Analytics Alert. Toronto: CPA Canada

Renaix.com. (2016). What Is The Secret To Data Analytics And Audit Quality?. Available at https://www.renaix.com/the-secret-to-data-analytics-and-audit-quality/ (Accessed in March 24st, 2021)

Selvaraj, Poornima and Pushpalatha Marudappa. (2018). A Survey of Predictive Analytics Using Big Data With Data Mining. International Journal of Bioinformatics Research and Applications Vol. 14 No. 3

The Economist. (2017). The world’s most valuable resource is no longer oil, but data. Available at https://www.economist.com/leaders/2017/05/06/the-worlds-most-valuable-resource-is-no-longer-oil-but-data (Accessed in March 21st, 2021)