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AI and big data in the Accounting Profession

AI and big data in the Accounting Profession

By Dr Amir Michael

The dramatic events taking place in the 21st century – since the Enron scandal in 2001, through the financial crisis in 2008 and the recent accounting and audit scandals have put an increasing level of pressure on businesses to rebuild the public’s trust in their practices.

As a result, managers and leaders are not only required to be highly skilled and talented as an essential prerequisite, but also to actively contribute to improving the public perception of their organisations and industries, through their daily practices, throughout their professional lives. One of the means of improving the skills, perceptions and understandings of managers is further education. Therefore, it also becomes the responsibility of business schools, the training houses and incubators of current and future business leaders, to acknowledge the need for their students to gain a contemporary set of skills to face the many varied and evolving challenges they are required to face day-to-day.

One of the biggest challenges facing modern business is managing the explosion of data that new technologies have made available to the world. The main reasons why businesses struggle, and often fail, is their inability to be flexible enough or have the right skills to deal with big data – featuring large volume, high velocity, huge variety and questionable veracity. Integrity was the main element for businesses collapsing. Consequently, producing credible information with appropriate levels of integrity requires acquiring analytical skills to handle and make sense of big data, such as: visualisation, data transformation, data mining, data modelling, database analysis, tool selection and quantitative analytical skills.

Big data is characterized by its colossal volume captured from different sources, variety of nature whether structured or unstructured, velocity in terms of data dynamic and change over time, veracity regarding the quality of data and usefulness for different purposes (Hashem et al, 2015).

This what we refer to as the Big Data Five Vs. Given the massive levels of corporate investment in big data, $34 billion in 2013 increasing to $232 billion in 2016, the Big Four accounting firms have certainly recognised the importance big data plays in improving the quality of assurance services they provide to their clients (Alles and Gray, 2016). In the audit context, incorporating big data in assurance services means moving beyond customary financial accounting information, represented by the traditional structured financial statements regulated by the accounting standards and audited in compliance with the statuary audit standards and regulations, towards big unstructured non-financial data and a variety of narratives. Consequently, traditional data analytic techniques need to be rapidly upgraded from using simple Excel spreadsheets to analyse samples of accounting data to more advanced analytical tools which enable professionals to visualize big unstructured data sets and analyse the predictions built on them (Alles and Gray, 2016).

The Financial Reporting Council (FRC) in 2017 and the International Auditing and Assurance Standards Board (IAASB) in 2016 called for a review to the use of Audit Data Analytics (ADA) by auditors to share good practices and encourage continuous improvement of audit quality. IAASB recommended that auditors need to capture better evidence to acquire a broader and deeper understanding of the entity’s environment, risk and business operations.

This was followed by an IAASB report in 2018 which showed how the different types of data analytics; descriptive, diagnostic, predictive and prescriptive analytics can help to gain a clearer understanding of the different business insights. It is believed that auditors need to learn these tools, using unregulated disclosures, to have better understanding of a business’ sustainability and future prospects. These tools are now based on artificial intelligence platforms, providing critical thinking, diagnostic analysis and advanced visualizations, and showing a dynamic analysis of the business’ operations beyond structured data disclosures.

The reported growth supports the notion that “data is the new oil” (Al-Htaybat and Alberti-Alhtaybat, 2017), especially environmental, social, and sustainable reporting, which is driven by a realization that growing levels of disclosure are being undermined by a credibility gap, arising from the lack of confidence in both the data and the reporting organisations (Doane, 2000 & Swift and Dando, 2002). The release of financial disclosures via social media, such as Twitter, can help to reduce the information asymmetry (Al-Htaybat and Alberti-Alhtaybat, 2017).

Dealing with artificial intelligence tools requires critical thinking, which is about asking good questions related to unstructured unfamiliar problems and searching for the right answers to resolve the problem. The ability to answer good questions requires full engagement with big data, whether structured or unstructured and captured from a variety of sources in different formats. In order to gain this knowledge, the critical thinking process needs to include other advanced skills such as: business analytics, measurement, representations, information systems and quantitative research tools.

However, dealing with big data is not an easy task. There are some common obstacles professionals will face including: data integrity, data identification, data aggregation and data confidentiality.


Al-, K., Htaybat, & Larissa. (2017). Accounting, Auditing and Accountability Journal Big Data and Corporate Reporting: Impacts and Paradoxes. Accounting, Auditing & Accountability Journal Accountability Journal Accountability Journal, 30(30).

Alles, M. and Gray, G.L. (2016) ‘Incorporating big data in audits: Identifying inhibitors and a research agenda to address those inhibitors’, International Journal of Accounting Information Systems, 22: pp. 44-59.

Doane, D. (2000), Corporate Spin: The Troubled Teenage Years of Social Reporting. New Economics Foundation London.

Hashem, I.A.T, Yaqoob, I, Anuar, N.B, Mokhtar, S, Gani, A. and Khan, S.U. (2015) ‘The rise of ‘’big data’’ on cloud computing: Review and open research issues’, Information Systems, 47: pp. 98-115.

Swift, T. and Dando, N. (2002) ‘From methods to ideologies: Closing the assurance expectations gap in social and ethical accounting auditing and reporting’, Journal of Corporate Citizenship, 8: pp. 81-90.

This article first appeared in IMPACT magazine in January 2019.

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