What Is The Impact Of Analytics And Big Data In Auditing ?

Historically, data was something you owned and was generally structured and human-generated. However, technology trends over the past decade have broadened the definition, which now includes data that is unstructured and machine-generated, as well as data that resides outside of corporate boundaries.

“Big data” is the term used to describe this massive portfolio of data that is growing exponentially. The general view is that big data will have a dramatic impact on enhancing productivity, profits and risk management. But big data in itself yields limited value until it has been processed and analyzed.

Analytics is the process of analyzing data with the objective of drawing meaningful conclusions. Major companies and organizations have recognized the opportunity that big data and analytics provide, and many are making significant investments to better understand the impact of these capabilities on their businesses. One area where we see significant potential is in the transformation of the audit.

Transforming the audit

As we continue to operate in one of the toughest and most uneven economic climates in modern times, the relevance of the role of auditors in the financial markets is more important than ever before. Audit firms must continue their robust audits to serve the public interest by increasing quality on a continuous basis and by delivering more insights and value to the users of the financial statements. Professional skepticism, and a continued focus on the quality of audit evidence, are required throughout an audit. Meanwhile, companies are expecting an enhanced dialogue with their auditors and more relevant insights.

While the profession has long recognized the impact of data analysis on enhancing the quality and relevance of the audit, mainstream use of this technique has been hampered due to a lack of efficient technology solutions, problems with data capture and concerns about privacy. However, recent technology advancements in big data and analytics are providing an opportunity to rethink the way in which an audit is executed.

The transformed audit will expand beyond sample-based testing to include analysis of entire populations of audit-relevant data (transaction activity and master data from key business processes), using intelligent analytics to deliver a higher quality of audit evidence and more relevant business insights. Big data and analytics are enabling auditors to better identify financial reporting, fraud and operational business risks and tailor their approach to deliver a more relevant audit.

While we are making significant progress and are beginning to see the benefits of big data and analytics in the audit, we recognize that this is a journey. A good way to describe where we are as a profession is to draw parallels with the TV and film subscription service Netflix. When the company started in 1997, it adopted a DVD-by-mail model, sending movies to its customers, who returned them after an evening or a week of entertainment. Netflix always knew that the future was in online streaming of movies, but the technology was not ready at that time, nor was high-speed consumer broadband as prevalent as it has since become.

It’s a massive leap to go from traditional audit approaches to one that fully integrates big data and analytics in a seamless manner.

Roshan Ramlukan

Today, we are engaged in the audit equivalent of DVD-by-mail, moving data from our clients to EY for use by auditors. What we really want is to have intelligent audit appliances that reside within companies’ data centers and stream the results of our proprietary analytics to audit teams. But the technology to accomplish this vision is still in its infancy and, in the interim, we are delivering audit analytics by processing large client data sets within our environment, integrating analytics into our audit approach and getting companies comfortable with the future of audit.

The transition to this future won’t happen overnight. It’s a massive leap to go from traditional audit approaches to one that fully integrates big data and analytics in a seamless manner.

Barriers to integration

There are a number of barriers to the successful integration of big data and analytics into the audit, though they are not insurmountable.

The first is data capture: if auditors are unable to efficiently and cost-effectively capture company data, they will not be able to use analytics in the audit. Companies invest significantly in protecting their data, with multilayered approval processes and technology safeguards. As a result, the process of obtaining client approval for provision of data to the auditors can be time-consuming. In some cases, companies have refused or have been reluctant to provide data, citing security concerns.

Moreover, auditors encounter hundreds of different accounting systems and, in many cases, multiple systems within the same company. Data extraction has not historically been a core competency within audit, and companies don’t necessarily have this competency either. This results in multiple attempts and a lot of back and forth between the company and the auditor on data capture.

Today, extraction of data is primarily focused on general ledger data. However, embracing big data to support the audit will mean obtaining sub-ledger information, such as revenue or procurement-cycle data, for key business processes. This increases the complexity of data extraction and the volumes of data to be processed.

While it is reasonably easy to use descriptive analytics to understand the business and identify potential risk areas, using analytics to produce audit evidence in response to those risks is a lot more difficult. One problem with relying on analytics to produce audit evidence relates to the “black box” nature of the way in which analytics works, with algorithms or rules used to transform data and produce visualizations or reports. When the auditor gets to this stage, they need to find the appropriate balance between applying auditor judgment and relying on the results of these analytics.

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