Has the AI Summer arrived in the audit sector?

EXPLORING OPPORTUNITIES AND THREATS

In recent years, the auditing profession has been undergoing a transformative shift, primarily driven by advancements in artificial intelligence(AI)/machine learning(ML)technologies. Traditionally characterized by labour-intensive manual verification processes,the audit landscape has found itself at an intersection of unprecedented opportunities and challenges, spurred by thedigital era's massive data generation and escalating unethical behaviours.

PROF BOMI NOMLALA, PROF MABUTHO SIBANDA AND HELPER ZHOU (PHD) University of Kwazulu-Natal, School of Accounting Economics and Finance

Leveraging a range of AI tools, including Natural Language Processing for document analysis, Robotic Process Automation for data recon and validation, and predictive analytics for risk assessment, auditors globally are exploring innovative avenues to enhance efficiency and accuracy in their processes.

Furthermore, initiatives like the US government’s AI-driven analytics to identify financial discrepancies and Singapore AG’s office's deployment of AI tools for financial data analysis underscore a burgeoning trend in AI adoption within the public sector.

While private entities lead in harnessing AI capabilities, governments globally are not far behind, taking pivotal steps towards integrating AI in audit procedures, with the United States and Singapore being notable examples, thereby highlighting a global shift towards an AI augmented audit process.

This demands a practical shift and commitment by the South African government auditors to actively start exploring and embracing pertinent auditing tools that improve the quality of audit outcomes. Concerningly though, is the fact that auditors in general, especially across developing countries like South Africa, are not adaptive to the wave of AI/ML-driven innovations.

The slow pace of adopting these technologies may largely be due to two primary reasons. Firstly, a lack of technical skills, which includes data science and machine learning, among others, smothers any potential appetite among auditors to jump and join the “AI mantra” bandwagon. These skills are largely crucial in helping auditors easily conduct advanced data analytics by querying data from multiple sources and also, where necessary, automating menial, repetitive tasks. Secondly, fear of job losses is another fundamental reason why auditors both in the private and public sectors may have a constrained appetite to fully adopt AI in their audit processes. Whilst this possibility may hold true for some mundane tasks, it’s important to note that, in essence, AI, which in this current version is narrow, can only empower users, including auditors, to focus on more creative

tasks that may not be automated. Interestingly, despite these and other challenges, according to a 2020 Accenture report, over 80% of audit and compliance executives anticipate a significant impact of AI on the audit profession, a perspective mirrored by the rapid adoption rate of AI tools in organizations worldwide.

Inevitably, leading players in the audit sector are starting to experiment with various AI-driven tools to improve audit processes. This includes promising developments, like Deloitte's substantial investment in the AI-based auditing platform Deloitte Omnia. In the recent past, EY’s Tokyo office leveraged machine learning to develop an innovative tool named EY Helix GL Anomaly Detector, to detect anomalous entries in large databases. This has been found to be the first of its kind and it's still being refined through increased exposure. Key to note from this, however, is that these developments are largely nascent and yet to be fully replicated across the profession.

Unrelenting breakthroughs from AI technologies like the recent OpenAI’s large language models, including applications like ChatGPT, means that the sector needs to catch up but, importantly, do so responsibly.

These technologies are accompanied by both clear-cut and subtle risks, which auditors should be aware of to avoid compromised audit outcomes. For example, the latest crop of AI tools, particularly generative algorithms, have the capability to create synthetic (which is to say) fictitious data, and this may be misused by companies to hide certain fraudulent transactions, and without better and more advanced tools, auditors may find their role in ensuring the reliability of financial statements seriously compromised.

Inevitably, the profession grapples with pivotal concerns, including potential job losses, the necessity for ethical AI to mitigate biases and data privacy issues, and heightened cybersecurity risks. The latter is amplified by the integration of AI with IoT devices, which increases the potential attack surfaces for cybercriminals, underscoring the urgent need for robust cybersecurity measures.

The audit landscape is further constrained by fragmented data structures, which make it difficult for interested players to develop and train algorithms that can be used across various stages of the audit cycle.

This is further exacerbated by the dominance of manual documentation, especially within government departments, for various transactions like tender submissions. This will require advanced techniques like Optical Character Recognition (OCR) to trough through scanned documents and convert them into readable texts. For a profession that is faced with limited skills, this may hamper the full adoption and utilisation of AI tools to perform effective and efficient audit tasks.

In conclusion, the audit sector stands at a crucial juncture – a transition from a manual to a potentially fully AI-augmented process, teetering between the "AI spring" and "AI summer." While the industry showcases readiness to foster AI-driven transformations, fostering a symbiotic relationship between AI technologies and human expertise appears paramount, thereby nurturing an environment that harmonizes innovation with adaptable regulations.

As we navigate this transformative period, characterized by both hope and hype, the sector is challenged with harmonizing the rapid pace of AI integration with ethical considerations and practical constraints. This period of transition, likened to a burgeoning "AI spring," poses the critical question of whether the traditional audit process can withstand the full brunt of an impending "AI summer," steering toward a future where AI not only augments but potentially revolutionizes the audit landscape. It is within this complex backdrop that this study explored the evolving narrative, evaluating the current state of AI integration in the audit sector while envisioning a future steered by balanced growth, innovation, and ethical adherence, nurturing a terrain ripe for the advent of Artificial General Intelligence (AGI) and possibly quantum computing in auditing.