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Artificial intelligence (AI) is the technological capabilities which allows computers and machines to simulate human intelligence and problem-solving tasks. In carrying out the tasks, the technology learns from the information it has analyzed and applies those lessons in future tasks to make reasoned judgments and solve problems. This capability is called machine learning, and it is key to the AI applications described in this article.
AI is being used in a variety of fields to automate operations that were previously done manually. Internal audit, as the Third Line of Defense (LOD), struggles heavily to meet stakeholders’ demands and assurance requirements. Internal audit performance-based assessments serve the goal of reporting to the audit committee and senior management related to the organization’s governance, risks, and
controls which are quite an extensive area to cover.
With the large documentation and slow audit responses, it remains a challenge with the auditors and Chief Audit Executives (CAEs) to provide efficient audit reports that can be well understood and accepted by the management and audit committee. All the data visualization and reporting techniques developed by AI will present new prospects for the transformation of corporate governance.
Almost every aspect of internal audit activity is now possible to leverage AI. Let’s explore how AI Can be used in the internal audit function one by one. At first, AI is able to play a significant role in implementing the International Professional Practices Framework (IPPF) standards, IPPF is the conceptual framework that organizes authoritative guidance promulgated by The Institute of Internal Auditors (IIA), USA, enhancing the efficiency and effectiveness of internal audit functions in a number of ways. AI can process vast amounts of data quickly and accurately, identifying patterns and anomalies that might be missed by human auditors. This capabilitysupports the IPPF’s goal of providing risk-based and objective assurance. Further, it enables continuous auditing by monitoring transactions and activities in real-time. This aligns with the IPPF’s emphasis on timely and relevant insights, helping organizations respond swiftly to emerging risks.
In addition to above, AI tools can predict potential risks by analyzing historical data and current trends. This predictive capability helps internal auditors provide more proactive and forward-looking advice, in line with the IPPF’s mission to enhance and protect organizational value. Automating routine tasks with AI frees up internal auditors to focus on more complex and strategic activities. This improves overall productivity and allows for a more thorough implementation of IPPF standards. And, AI can also help ensure compliance with various regulations and standards by continuously checking for adherence to policies and procedures. This supports the IPPF’s framework for effective governance and control.
After that, AI is not only enhancing the quality and efficiency of audits but also supports the strategic objectives of organizations by providing deeper insights and more timely information, such as; AI enhances the audit process which can automatically gather and process large volumes of data from various sources, reducing the time and effort required for manual data collection. AI algorithms can analyze complex datasets to identify trends, anomalies, and potential risks. This helps auditors focus on areas that require deeper investigation. By leveraging machine learning, AI can predict future risks and trends based on historical data. This allows auditors to provide more proactive and forward-looking recommendations. Moreover, it enables continuous monitoring of transactions and activities, providing real-time insights and alerts. This ensures that any irregularities are detected and addressed promptly.
AI reduces the likelihood of human error in data analysis and reporting, leading to more accurate and reliable audit outcomes. By automating routine tasks, AI frees up auditors to focus on more strategicand high-value activities, improving overall efficiency and effectiveness. AI can help ensure compliance with regulatory requirements by continuously monitoring adherence to policies and procedures. Then comes another major area of Risk “Fraud Risk”, it is one of the key risk areas in Internal Audit activity which AI is transforming in parallel to other areas through enhancing the detection, analysis, andprevention of fraudulent activities. AI algorithms can analyze large datasets to identify unusual patterns and behaviors that may indicate fraud. By establishing a baseline of normal activity, AI can flag deviations for further investigation. It can map relationships between entities (e.g., individuals, accounts, transactions) to uncover complex fraud networks. This helps investigators visualize connections and identify suspicious patterns. Machine learning models can predict potential fraud by analyzing historical data and identifying trends. This proactive approach allows organizations to mitigaterisks before they escalate. AI can automate the collection and processing of data from various sources, making it easier to compile evidence and identify fraudulent activities quickly. AI systems can
continuously monitor transactions and activities in real-time, providing immediate alerts when suspicious behavior is detected. This enables faster response times and reduces the impact of fraud. AI tools can assist forensic accountants in uncovering complex financial fraud schemes by analyzing transactional data and identifying patterns that would be difficult to detect manually.
After that, AI is increasingly being integrated into audit software to automate audit processes which makes it easier for improved data analytics by integrating the audit software with the core application/s of the company, enhancing the capabilities and efficiency of auditing processes in various ways asfollows. AI-enabled audit software not only improves the accuracy and efficiency of audits but also
provides deeper insights and more timely information, supporting better decision-making and risk management.
Automated Data Analysis: AI-powered audit software can automatically analyze large volumes of data, identifying patterns, anomalies, and trends that might indicate potential issues or areas for furtherinvestigation.
Risk Assessment: AI algorithms can assess risk by analyzing historical data and current trends, helping auditors prioritize their efforts on high-risk areas. This ensures a more focused and effective audit process.
Continuous Auditing: AI enables continuous monitoring of transactions and activities, providing real- time insights and alerts. This allows auditors to detect and address issues as they arise, rather than relying solely on periodic audits.
Natural Language Processing (NLP): AI-driven NLP can analyze unstructured data, such as emails and documents, to identify relevant information and potential risks. This expands the scope of data that can be included in audits.
Predictive Analytics: AI can predict future risks and trends based on historical data, allowing auditors to provide more proactive and forward-looking recommendations.
Fraud Detection: AI can enhance fraud detection by identifying unusual patterns and behaviors that mayindicate fraudulent activities. This helps auditors uncover fraud more effectively and efficiently.
Enhanced Reporting: AI can automate the generation of audit reports, ensuring they are accurate, comprehensive, and tailored to the specific needs of stakeholders.
Resource Optimization: By automating routine tasks, AI allows auditors to focus on more strategic and high-value activities, improving overall productivity and effectiveness.
Finally, AI is significantly enhancing the preparation of the Audit Universe and the overall audit process through the following ways:
Defining the Audit Universe: AI can integrate data from various sources, providing a comprehensive view of the organization’s operations. This helps in accurately defining the Audit Universe by identifying all auditable entities and processes. AI algorithms can analyze historical data, industry trends, and financial ratios to assess risks. This data-driven approach helps auditors prioritize high-risk areas within the Audit
Universe.
Audit Planning: AI can optimize resource allocation by predicting the time and effort required for different audit tasks. This ensures that resources are efficiently used, focusing on areas with the highest risk. AI can assist in scheduling audits by considering factors such as auditor availability, audit complexity, and organizational priorities.
Data Analysis: AI can analyze large datasets to identify unusual patterns and anomalies that may indicate potential issues. This helps auditors focus on areas that require deeper investigation. AI enables continuous auditing by monitoring transactions and activities in real-time, providing immediate alerts for any irregularities.
Risk Identification and Assessment: AI can predict future risks based on historical data and current trends, allowing auditors to provide more proactive and forward-looking recommendations. AI can simulate various scenarios to assess the potential impact of different risks, helping auditors develop more robust risk management strategies.
Reporting and Documentation: AI can automate the generation of audit reports, ensuring they are accurate, comprehensive, and tailored to the specific needs of stakeholders. AI-driven NLP can analyze unstructured data, such as emails and documents, to identify relevant information and potential risks.
The writer is the former Chief Audit Executive (CAE) of ASA International Group Plc, and Former VP of HSBC. He can be reached at: [email protected].
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