Auditing in the AI era: Striking a balance between obsolescence and reinvention

ME PoV Spring 2024 issue

As artificial intelligence (AI) continues to evolve, its impact on various industries becomes increasingly profound. In the realm of external audit, AI technologies are reshaping traditional approaches and introducing unprecedented efficiencies. These advancements promise to streamline audit processes, enhance accuracy, and provide auditors with powerful tools for data analysis. However, alongside the opportunities, concerns about job displacement, ethical considerations, and the need for regulatory adaptations are substantial. 

Current landscape of AI in the external audit

In the ever-evolving landscape of AI, the external audit profession finds itself at a critical crossroads of transformative change. As AI technologies advance, auditors are increasingly leveraging these innovations to revolutionize their traditional approaches. This fundamental shift goes beyond mere automation; it encompasses a fundamental redefinition of audit methodologies. The promise of heightened accuracy and sophisticated data analysis tools draws auditors into a domain where efficiency gains are not just a byproduct but a central objective. On that front, Deloitte has released its own chatbot for its workforce across the UK, Europe, and the Middle East, designed to help improve workplace efficiencies. Furthermore, Deloitte has been using Argus for the extraction of data from PDF documents and transforming it into a tabular format for further analysis and risk sensing.

Role of AI in the external audit

By automating traditionally performed tasks, AI holds the potential to revolutionize external audits. Efficient handling of data entry, validation, and pattern recognition tasks by AI algorithms allows auditors to shift their focus to more complex analyses and strategic decision-making. The examination of vast datasets extracted from several sources, such as accounting software, bank statements, or transactions records, for anomalies or trends through machine learning algorithms significantly enhances the accuracy of financial analysis.

Most prominent AI use cases involve automation of repetitive tasks, minimizing the risk of human error, predicting potential financial trends based on historical data, and analyzing unstructured financial data, such as text in financial reports. Furthermore, AI can identify unusual patterns or behaviors indicative of fraudulent activities within financial records by pinpointing suspicious patterns or behaviors such as accounting irregularities, unauthorized transactions, or misappropriation of assets. As AI streamlines these processes, auditors increasingly expand their role to invaluable partners, redirecting their expertise to interpret results, assess risks, and provide valuable insights in the audit process. 

Challenges and concerns

The integration of AI in external audits brings forth a spectrum of challenges and concerns that demand careful consideration. It is not a one-size-fits-all transformation; its impact varies across different regions and industries where some regions may swiftly embrace AI while others might face challenges related to infrastructure or regulatory frameworks. Similarly, industries with complex financial structures may experience different outcomes compared to those with simpler financial ecosystems. 

One primary apprehension is the potential for job displacement as AI takes over routine tasks traditionally performed by auditors. This shift raises questions about the evolving role of human professionals in the audit process. Ethical and legal implications are significant as well, such as instances where regulatory bodies may not have established clear guidelines or standards for the use of AI in external audit. 

Moreover, additional concerns have arisen, including algorithmic bias. For example, an algorithm may disproportionately identify transactions related to derivatives, such as swaps or options, as high-risk, while overlooking similar transactions in other asset classes. Transparency and accountability are also coming to the forefront, highlighting issues such as the unclear understanding of how AI algorithms operate and the opacity surrounding their decision-making processes. 

Lack of audit trails and of regular monitoring and validation can also be challenges that auditors have to overcome. Accordingly, auditors and stakeholders must sustain continuous efforts to ensure that AI systems are fair, free from discrimination, properly reviewed and validated, and align with ethical standards. 

As the profession navigates these complexities, finding a balance between reaping the benefits of AI and addressing these concerns becomes paramount for the future of external audits.

Required upskill for auditors

The evolution of skills for auditors is imperative in the era of AI integration. As technology transforms traditional audit practices, auditors must undergo a profound skills evolution, including the need to continuously upskill, to stay relevant. While automation and AI can streamline certain tasks, auditors should recognize the enduring value of specialized skills that cannot be easily replaced. Expertise in areas such as complex valuation processes where contextual understanding is vital, ethical dilemmas, forensic analysis, and industry-specific regulations remains indispensable. 

Beyond foundational accounting expertise, auditors now benefit from proficiency in data analytics, understanding AI algorithms, and interpreting results generated by machine learning models. The ability to leverage advanced technologies to extract meaningful insights from complex datasets becomes a pivotal skill. Moreover, auditors need to elevate critical thinking and analytical reasoning to interpret AI-driven outputs and make informed decisions. Effective communication skills remain equally crucial, as auditors must continue to articulate complex findings and insights to stakeholders in a clear and comprehensible manner. In this dynamic landscape, continuous learning and adaptability become integral, forming the foundation of a modern auditor's skill set. This ensures they navigate the intersection of audit practices and technological advancements with competence and confidence, while remaining professionally skeptical.

In summary and beyond

In conclusion, the trajectory of AI within the audit profession is a journey marked by both unprecedented opportunities and formidable challenges. The current landscape showcases AI's potential to reshape traditional audit methodologies, offering auditors powerful tools for enhanced accuracy and efficiency. The role of AI in automating routine tasks holds promise for auditors to increasingly expand their role to invaluable partners, redirecting their expertise towards interpreting complex data analyses. However, this evolution demands a concurrent upskilling of auditors to ensure relevance and proficiency in the face of advancing technology. Specialized skills in areas such as complex valuation and industry-specific expertise remain indispensable, underscoring the enduring human contribution to the audit process. 

The global perspectives and varied industry impacts illuminate the nuanced adaptation of AI in different regions and sectors, emphasizing the need for adaptable approaches. Despite these transformative possibilities, challenges persist, particularly regarding job displacement, ethical considerations, and regulatory adjustments. Striking a delicate balance between leveraging AI benefits and addressing ethical and regulatory concerns is paramount for the sustained success of the audit profession in this era of technological advancement. As auditors navigate these complexities, their ability to continuously learn, adapt, and maintain a vigilant eye on ethical standards will shape the future landscape of external audits.

By Rouba Abou Daher, Director, Audit & Assurance, Deloitte Middle East

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