Why is the audit profession slow in adopting analytics?

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Contrarily, stakeholders continue to have increased expectations for higher quality and more comprehensive audits at reduced costs – understandably so given the audit and accounting profession’s current reputational challenges. Because of this expectation they continuously challenge the practitioner to provide more innovative ways to improve audit effectiveness while showing deep insights. As a result, conventional auditing approaches can’t keep up with exponential change and growing demand for more insightful assurance.

Those charged with governance sometimes have a distorted understanding of their data quality and data management processes, leading to a misaligned expectation of how data can be used in the audit – a challenge for the practitioner.

Other barriers could include:

• understanding what data is available, what it contains and how to obtain it
• understanding how to treat analytics results and use them in the operational effectiveness testing of a control
• data not reconciling to the general ledger
• the organisations’ business process controls remain largely outside of the IT systems and not through their IT systems, creating incomplete data sets making extracting audit evidence difficult
• availability of different tools to utilise to analyse and visualise data, which without guidance from standards creates inconsistency between audits
• analytics tools produce false positives based on how the data analytic routine was developed and practitioners are tempted to revert to a traditional approach instead of understanding the cause of the exceptions

Another misperception is that the first run of an analytical routine provides you with the optimal result set, however in my experience adopting audit analytics in the first year of implementation (for each organisation) highlights a lack of understanding of:

• the business process and data flows
• the test objective

In either event, multiple iterations are required to refine an effective analytical output. This execution effort (in the first year), assists in eliminating false positives and thus the lack of trust in the analytics.

Given these challenges, I still see a benefit in adopting a data driven audit/audit analytics.

The abovementioned two matters emphasise the business imperative of a data driven audit and we know that the auditing profession is under scrutiny. Minimum control testing and sampling of transactions is inadequate in data rich environments, and is becoming acceptable to the regulators. In interactions with regulatory and professional bodies, I see that their views about the use of sophisticated technology and audit analytics are changing.

The South African Institute of Chartered Accountants (Saica) is accelerating support in driving innovation into the auditing and accounting profession. Martin Baumann, former chief auditor and director of professional standards at the Public Company Accounting Oversight Board (PCAOB), commented in an interview for the Journal of Accountancy that: “We wouldn’t want auditing standards to be an inhibitor that might otherwise allow technological audit achievements to move ahead.”

Audit analytics gives us the opportunity to analyse large data sets that were too voluminous to make sense of. Current technologies allow us to provide full population analysis in the risk assessment and execution phases, and to eliminate a one dimensional lens of a process and provide a holistic picture of transactions across business processes, thus aiding in the practitioners’ ability to perform a more informed risk assessment.

How do we bridge this gap and fast track fuller adoption of analytics?

• Change management in the practice – this enhanced audit analytics journey remains a top-to-bottom, bottom-to-top approach. Practitioners need to encourage and empower their audit teams to adopt new ways of executing audits.
• Continuous enhancements of skills for practitioners – Firms planning to move toward advanced audit analytics need to hire or develop staff with advanced analytical capabilities. One approach would be to employ a group of specialists who operate in the analytics field and have limited knowledge of the auditing process. Another belief is that these skills will need to be embedded within each audit professional to be successful. The ultimate solution will lie somewhere between these two extremes.

Universities, professional bodies and regulators such as the Saica, Public Company Accounting Oversight Board (PCAOB) and our standard setters’ board – The International Auditing and Assurance Standards Board (IAASB) - are already in the midst of revising skill set requirements to create the chartered accountant/auditor of the future, not only because of the need, but also to ensure our profession remains relevant and attractive.

Most practitioners are familiar with electronic spreadsheets, but many are not as skilled with robust technology. I am already seeing a transition of skill sets. Practitioners are empowering themselves and their audit teams to perform analytics, instead of reverting to a data or analytics expert. Our new generation yearns to be part of audit and assurance engagements.

• Earlier and combined discussions with your clients’ technology and data specialists (IT department) and finance management – one can foresee a world where audits will be significantly value-added and comparative. Will auditors be able to tell their clients – using anonymised data – how they compare to peers on key metrics and benchmarks? When uncovering business risks, will audits also uncover potential business opportunities? These questions call for an informed dialogue among stakeholders to determine how audit standards and practices will need to evolve to continue enhancing the relevance and value of the audit.
• More effective usage of audit technologies ­– Significant investments in technologies and/or people is not always necessary initially – first understand and use the technologies you have more effectively. You may find several analytics you can perform with your current IT infrastructure
• Invest in self learning – develop your abilities through utilising existing learnings and trainings