(How Internal Auditors can use AI technologies to provide greater assurance to their organizations)

By: Hesham El-Yafi

The ever-changing world of Artificial Intelligence (AI) has started to make its way into the field of internal audit, bringing with it technologies and strategies that are transforming how auditors work. In the past, many people have been skeptical of the role AI will play in their lives, thinking it would just lead to a rise in unemployment rates. However, we have slowly started to change this perception as economists and leading companies alike advocate for its development.

AI is an emerging technology that can be used for a variety of purposes. It is a set of computer-based systems that simulate human intelligence and through the use of robotic process automation, it can be used to imitate how human beings perform certain repetitive tasks, but with higher speed and efficiency. AI has been used in most industries including healthcare, education, and finance but in recent years, there has been a significant increase in its use in internal auditing.

AI will not replace auditors in their jobs; rather, it will empower them to conduct tasks that formerly had to be handled manually and proved to be cumbersome. AI can have a big role in changing the way internal auditors provide assurance to their companies. Using AI, internal auditors can gain key insights into how the business operates and uncover hidden patterns in data that would otherwise be difficult or impossible to detect using traditional methods. In the following sections, use cases in which internal auditors can leverage the power of AI.

Enhanced risk identification

The use of AI-powered tools allows internal auditors to automate many routine tasks involved in risk assessment such as data analysis, data aggregation, data storage, and data retrieval. These tools also provide easy visualization capabilities which allow internal auditors to quickly spot anomalies and make comparisons across different datasets to identify emerging risks at an early stage before they become critical issues. For example, an internal auditor may notice that certain controls are not consistently applied across different departments within the same organization or that certain processes are not always followed properly. By using machine learning algorithms to analyze this data and identify patterns and underlying data between different departments or processes, the internal auditor can recommend policies and procedures that will improve control effectiveness across the entire organization and reduce risk exposure over time.

Fraud detection

By identifying patterns and anomalies in financial data or by analyzing texts in internal and/or external documents, AI can help internal auditors defend their organization against fraud, waste, and abuse. Machine learning-based fraud detection systems can learn from past behavior patterns and flag suspicious transactions as they occur. They can also learn from new datasets as they become available, enabling them to constantly update their knowledge base with each new transaction or dataset. These types of systems are already being used by some companies today to detect fraudulent behavior within their organizations and prevent losses before they occur. According to the ACFE (Anti-fraud technology benchmarking report, 2022),more than 50% of organizations are currently adopting exception reporting and anomaly detection, as well as automated monitoring of red flags and business analysis as part of their anti-fraud programs, and the use of AI and machine learning in anti-fraud programs is expected to drastically increase in the next two years. Internal auditors can leverage technologies such as text analytics (a method that uses natural language processing (NLP) techniques to extract information from text) to identify fraud in organizations by analyzing internal and external documents, such as emails, reports, and transcripts. For example, an insurance company can use text analytics to identify claims fraud so that it can take appropriate actions. The insurance company may also use text analytics tools to identify claims with high levels of risk so that it can act before they become fraudulent.

Automating repetitive tasks

Robotic Process Automation (RPA) is a branch of Artificial intelligence that can replicate human actions quickly and accurately without requiring much training beforehand. It can help internal auditors improve efficiency by automating repetitive tasks such as gathering data from various sources, analyzing it, and preparing reports, saving time and resources while also increasing accuracy.

For example, an RPA tool can automate the process of gathering financial information from different systems and presenting it in a single dashboard for analysis by an internal auditor. This enables them to use time more effectively on higher-value activities such as analyzing financial trends and identifying areas of risk within the organization. RPA is also useful for automating routine tasks such as creating routine audit reports or performing repetitive audits over time, allowing internal auditors to become more efficient in the way they do their work and supporting them in providing better audit coverage over their organization’s processes. This efficiency further empowers the internal auditor to focus on more strategic projects that will help them improve their organization’s overall risk management capabilities and ultimately unlock the strategic value internal audit brings to the organization.

Conclusion

The world is at a point where many sectors are being revolutionized through artificial intelligence. There are already significant deployments in finance, national security, health care, criminal justice, transportation, and smart cities that have altered decision-making, business models, and risk mitigation.

With the practical use of AI and the benefits that it can provide to internal auditors, we can expect a future where artificial intelligence and robotic process automation become a major part of the internal audit process thereby transforming the way audits are performed. Audits will become more efficient and internal audit functions will be able to provide greater audit coverage. Auditors should not fear that they are the first ones to be replaced by technology by the introduction of machine learning and artificial intelligence, but in fact, they need to get ready for the change, reshape their role, and acquire new skills and knowledge in adapting to meet the new expectations of assurance.

No matter what the future brings, there will always be an assurance role for auditors. AI merely offers internal auditors a new innovative approach with which to carry out their responsibilities. AI alleviates the burden of laborious manual processes, enables internal auditors to evaluate massive datasets quickly, and assists in freeing up professionals for more value-added tasks. And lastly, AI not only can provide significant risk and governance insights, but it can also assist internal auditors to be at the forefront of delivering strategic suggestions and recommendations to the Board.

Learn more about AIGC’s Risk-Based Internal Audit service, here.

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Hesham is a Consultancy Supervisor at AIGC with 5 years of experience in the field of Risk Management, Governance, Data Analytics, and Internal Auditing. He is a Certified Internal Auditor (CIA), a Certified Fraud Examiner (CFE), and a Certified IDEA Data Analyst (CIDA). Hesham was heavily involved in Data Analytics and Process enhancement engagements in the MENA region, where he used his expertise in evaluating organizations’ portfolios and internal processes and recommending enhancement to areas of inefficiencies. He has also conducted several trainings on the use of data analytics in the field of audit.