In an era of rapid technological progress, one innovation stands out for its transformative influence on the field of internal auditing: artificial intelligence (AI), which brings extensive potential for internal auditors. By leveraging its capacity for enhanced data analytics, risk assessment, tracking of regulatory changes or audit report generation. AI is transforming the auditing process by enhancing efficiency, accuracy, and effectiveness. The integration of AI in internal audit processes promises to unlock new opportunities and streamline operations. In this article, we focus on the transformative impact as well as challenges of artificial intelligence on the field of internal audit.
Traditional internal audit processes have struggled with challenges and limitations that impact their effectiveness for a long time. Manual data entry, time-consuming analysis, and the inability to analyze extensive data sets comprehensively are just a few of the challenges that internal auditors are facing. These limitations do not only impact efficiency of performing internal audit but also raise possibility for potential errors and oversights. Recognizing the need for innovation, the internal audit profession can think about adopting artificial intelligence as a way of overcoming these challenges.
Artificial intelligence (AI) brings a great number of key capabilities that can be utilized across all stages of internal auditing, from planning and execution to reporting and continuous monitoring.
The feature that provides auditors powerful tools to proactively identify and mitigate risks within planning phase is AI-driven risk assessment models, along with AI-assistance in the creation of audit test plans and procedures. Using AI, internal auditors can develop sophisticated risk assessment models that consider a wide range of variables and scenarios, enabling them to assess risk exposure more accurately. AI algorithms can analyze historical data, identify patterns, and extract insights to forecast potential risks and their potential impact on the organization. By leveraging its predictive analytics, auditors can anticipate emerging risks, enabling timely intervention and mitigation strategies. This proactive approach enhances the organization’s ability to timely prevent and address risks. AI-driven risk assessment models and predictive analytics empower internal auditors to make data-driven decisions and focus their efforts on areas of the highest risk.
The primary strengths of AI in the execution phase are its ability to automate repetitive tasks and conduct data analysis, allowing auditors devoting more time for decision-making, focusing on high-risk areas, and providing insights that drive strategic business outcomes. By analyzing historical data, AI algorithms can identify patterns, anomalies, and correlations that might be overseen by human auditors. Furthermore, AI algorithms can continuously monitor data streams in real-time, providing proactive detection of unusual patterns that may indicate fraud or non-compliance. This enables auditors to gain a comprehensive understanding of the organization’s operations, risks, potential areas of concern and making more informed decisions.
The AI-powered tools can generate comprehensive audit reports by consolidating data, analyzing findings, and producing structured reports. They can also assist in formulating proposals for management action plans. This automation significantly reduces the time internal auditors spend on reporting phase.
The ongoing changes of regulatory landscape, keeping up with new guidelines, and industry standards is another challenge for auditors. AI-enabled monitoring and tracking of regulatory changes plays a crucial role in enhancing compliance procedures and reporting within the continuous monitoring aspect of internal auditing. AI tools can quickly scan and analyze regulatory documents, identifying relevant updates and changes in real-time. This enables auditors to stay up to date with the latest compliance requirements and adapt their procedures accordingly. By leveraging AI, auditors can streamline compliance reporting, generate accurate and timely reports, and detect potential compliance gaps or deviations more efficiently. This not only enhances the organization’s ability to meet regulatory requirements but also strengthens its reputation and trustworthiness.
Contrary to the positive aspects of AI on internal auditing, the integration of AI in internal auditing opens the risk universe. Challenges like insufficient data quality, encompassing erroneous, incomplete, or biased data, pose significant risks, potentially leading to incorrect conclusions by internal auditors. Additionally, the lack of transparency in certain AI models can undermine the credibility of internal audit findings due to challenges in models’ interpretations. The utilization of AI-powered tools also poses significant risks to data security and privacy protection due to potential vulnerabilities in algorithms and systems. These risks increase the need for legislative frameworks changes, like recently agreed European Union AI Act, which aim to ensure AI systems in the European Union are safe, transparent and are overseen by people to prevent harmful outcomes. The objective of the Act is to establish coordinated regulations that oversee the development and utilization of AI within the EU. Another challenge posed by the utilization of AI tools is that heavy reliance on technology may result in a loss of proficiency in traditional audit methods. Considering all the challenges that the new technology brings, the human validation remains crucial for guaranteeing the accuracy and reliability of outputs generated by AI.
Deloitte is actively seeking AI-powered solutions on a global scale to address future requirements and enhance the efficiency of everyday tasks. Deloitte is being proactive in its approach and possesses the AI-powered tools that can be leveraged in various critical work areas of internal auditing within planning and execution phase.
AI-Based Predictive Analytics for Audit Planning tool helps increase effectiveness in the planning phase by providing insights into potential risks and issues through historical data analysis and predictive models creation. For example, in the initial stage of the audit, the team employs an AI-based predictive analytics tool that aggregates data from disparate sources, including financial systems, ERP platforms and external sources to identify patterns, trends and assesses the likelihood and potential impact of various risks. Based on the risk assessment and scenario analysis, the AI tool helps auditors prioritize areas to be audited.
By utilizing an AI-Powered Fraud Detection and Prevention tool capable of analyzing extensive datasets and detecting patterns or irregularities, fraudulent activities can be identified. By employing machine learning algorithms, AI can learn from historical data and identify emerging fraud patterns or anomalies. For instance, unusual spikes in transaction volumes, frequent round amounts, transactions occurring outside regular business hours, or irregular employee expense claims. This approach enables auditors to anticipate potential threats and enhance risk mitigation strategies.
Another great example of AI-powered enhancement developed in-house is Intelligent Document Review for Compliance tool, which employs natural language processing to analyze large volumes of documents swiftly and accurately, providing users with key information promptly. For example. The internal auditors responsible for contractual agreements compliance review can use AI-powered intelligent document review to assess contract terms and conditions. The AI-powered tool can review contracts, amendments, and related documents to identify any deviations from agreed-upon terms or potential contractual risks. This tool serves a dual purpose: it facilitates identification of non-compliance and eliminates the need for manual document review.
The adoption of AI technology has experienced a notable increase, with a concurrent rise in the complexity of algorithmic decision-making carried out by AI systems. The extent to which AI technology is adopted differs significantly among countries, industries and companies due to a combination of industry-specific factors, internal resources, external pressures, and the organization’s unique circumstances.
In conclusion, the future of AI in internal audit presents both the great potential to transform the internal auditing profession and challenges associated with the adoption of this new technology. As AI technology continues to evolve, we can anticipate several key developments. First, AI will become increasingly integrated into auditing processes and automating routine tasks. Additionally, AI-powered predictive analytics will enable auditors to foresee risks and trends, providing proactive insights for risk management and decision-making, whereas risks associated with the adoption of AI-powered tools need to be recognized and effectively managed. Internal audit teams must assess and decide on a suitable approach based on the organization’s tolerance for risk and the pace of technology adoption.
However, these advancements will also bring implications for the role of internal auditors and their skill requirements. Auditors will need to enhance their understanding of AI algorithms and ensure the ethical use of AI in auditing processes. With AI handling routine tasks and data analysis, auditors will have more time and resources to focus on value-adding activities, such as providing insights on emerging risks, assessing the effectiveness of internal controls, and assisting in strategic decision-making processes. By embracing these changes, internal auditors can maximize the benefits while mitigating the risks, and guide organizations toward effective risk management, enhanced performance, and sustainable growth.
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