By Waldheim Kazenango
Artificial Intelligence (AI) might sound complex, but it is about making machines SMART. Just like how humans can learn from experience and make decisions.
AI aims to build computer systems that can do similar things. For example, AI can help computers understand spoken commands, recognize faces in photos, or even play complex games like chess. Essentially, it is about teaching computers to “think” and “learn” in a way that mimics human intelligence.
The Growing Importance of Internal Audit in Modern Banking
The way of doing Banking is getting more complex than earlier because of technological advances and real time operation improvements. Currently, only large international Banks have easy access to AI.
By applying the use of Artificial Intelligence (AI) to their internal audit function, many Banks will gain a more competitive advantage. Artificial Intelligence (AI) can significantly enhance the internal audit function of Banks in several ways, broadening its scope and effectiveness.
Below is an example of the benefits:
• Increase in Audit Coverage and provide extended assurance – AI tools can analyse large amounts of data, allowing internal auditors to gain comprehensive insights into processes and controls.
• Enhanced Risk Identification: Risk management being a core pillar driving the Internal Audit Plan. An AI algorithm can be used to identify patterns and anomalies in data that will help in detecting potential risks and irregularities early, ensuring that auditors can focus on high-risk areas and provide extended assurance across a broader area of operations.
• Data-Driven Decision Making: With AI, auditors can base their conclusions on data-driven insights rather than solely on historical audit reports. This leads to more informed decision-making and can improve the overall reliability of the audit findings.
• Increase in Scope Audit Coverage Without Compromising on Audit Timeliness – AI tools can cover
more transactions and areas within the same timeframe, without sacrificing the depth or quality of the audit.
• Automation of Routine Tasks: Routine and repetitive audit tasks, such as issue tracking, can be automated using AI and using a tool such as PowerBI this information can also be visualised.
Use of AI Standard Bank Group
Although still at infancy stage and in response to the Standard Bank Group (SBG) digitization strategy, Group Internal Audit (GIA) has developed an enterprise automation platform. This platform is used for data analytics (scripts documented in SQL) and continuous monitoring and integrates to the Bank’s core banking system and the Bank’s peripheral systems.
When fully adopted this platform will enhance and enable continuous monitoring, foster collaboration between the three lines of defence and business. This will contribute to enhancing the control environment across the Group and its subsidiaries as real time monitoring and assessment of processes and controls to mitigate risks will be available to everyone in the Bank.
Some internal audit functions are already making use of Copilot ‘Chatbot developed by Microsoft’ in taking meeting minutes. This meeting minutes are retrieved and saved on an audit database such as TeamMate+. This process saves internal auditors a lot of time in running and documenting meeting minutes at the same.
When performing Customer Service audit, Internal Audit uses Python to source for customers complaints that are lodged through Facebook and X. These complaints are then themed and visualised. The themed complaints are sent to management to provide commentary on actions being taken to address these customer complaints.
To effectively address the opportunities presented by AI, internal audit functions must adapt their practices in several key areas:
Adapting Internal Audit Practices to AI
- Developing AI Expertise: Internal auditors should invest in training and education to gain a foundational understanding of AI technologies, their applications, and their potential impact on audit practices.
- Collaborating with IT and Data Teams: Internal auditors should establish close collaboration with IT and data teams to gain insights into AI implementations, data governance practices, and risk mitigation strategies.
- Evolving Audit Methodologies: Audit methodologies and tools should be updated to stay aligned with the rapidly evolving AI landscape.
*Waldheim Kazenango is Manager, Internal Audit at Standard Bank Namibia
