Impact of Artificial Intelligence on Audit in Kenya

Artificial Intelligence (AI) has already made significant strides in financial reporting and audit in Kenya. According to a recent article in the Journal of Accountancy, AI is used to analyze vast amounts of economic data, identify anomalies, and predict trends. These capabilities are invaluable for auditors who must sift through complex datasets to detect fraud or errors efficiently.

Moreover, AI has enabled real-time data processing, allowing for continuous monitoring of financial transactions. This shift from periodic to constant auditing represents a significant leap forward, enhancing the ability to detect and address issues promptly. Insights on AI in financial reporting highlight that AI tools can now perform tasks such as journal entry testing and transaction validation, which traditionally require extensive manual effort.

The future of audit in Kenya will see a greater emphasis on strategic advisory services. With AI handling routine tasks, auditors will have more bandwidth to focus on providing value-added services, such as risk management, financial planning, and compliance consulting. This shift will elevate the role of auditors from compliance enforcers to strategic partners.

Forthwith, let’s understand how AI has revolutionized the audit process:

Planning and Scoping

Applying artificial intelligence to risk assessments and scoping can lead to a more comprehensive plan focusing on the most critical risks. Using AI allows auditors to factor in different metrics more efficiently.

• Audit Risk Assessments

In the past, audit risk assessment relied heavily on subjective data gathered during extensive, time-consuming in-person interviews. Over the years, internal auditors have adapted the interviews into self-assessments to reach more stakeholders and collect data more efficiently while incorporating concrete metrics like financial and operational data. With artificial intelligence audit planning, auditors have two popular options. First, auditors can prompt natural language processing (NLP) tools to provide a list of risks to expect in an area. Many will leverage this option when approaching a process for the first time. Next, auditors can use AI to analyze large datasets to identify patterns, trends, and anomalies that may indicate potential risks to help prioritize their focus areas. By setting up a continuous data ingestion model along with thresholds based on key risk indicators, the audit team moves from periodic to continuous risk assessment. Then, by incorporating machine learning (ML), the system can learn which threats to elevate and which are false positives.

• Audit Planning and Scheduling

Building an audit plan that accurately reflects the results of all the data collected during the risk assessment stage can be challenging, especially in large, complex organizations. Artificial intelligence in auditing can produce a first draft of an audit plan based on all available data. As many audit teams embrace an agile way of working, the option to have AI gather current data, parse commentary provided during a risk self-assessment, and suggest a prioritized audit plan more frequently can mean the difference between success and failure. Taken even further, by incorporating audit team member skills and availability along with a potential audit plan, artificial intelligence could produce a potential audit schedule, alleviating the workload for the audit leadership team.

Audit Fieldwork

During the fieldwork phase, artificial intelligence in auditing can improve efficiency, enhance risk identification, and provide deeper insights into an organization’s control environment.

• Increased Efficiency and Automation

The use of artificial intelligence in auditing is most often associated with data analytics, and for good reason. The ability to leverage AI to perform repetitive tasks allows auditors to focus on the interpretation of data instead of tedious tasks. AI-powered tools can handle data extraction, document review, and other time-consuming yet crucial tasks, freeing up auditors for more strategic analysis and judgment. AI can quickly scan large datasets, identify trends and anomalies, and flag potential areas of concern, saving auditors valuable time sifting through information. By offloading these routine tasks to AI, auditors improve their efficiency while auditing full data sets and spend more time reviewing rather than pulling data.

• Enhanced Risk Identification and Detection

Using emerging technology like artificial intelligence for data analysis can evolve into continuous monitoring. AI tools can monitor transactions and controls in real-time and produce exception reporting, enabling auditors to react and address potential issues as they arise. Many artificial intelligence tools also involve machine learning. AI algorithms can learn from historical data and identify patterns that might indicate fraud, errors, or control weaknesses. By incorporating risk tolerances or key risk indicators, artificial intelligence in auditing can be pushed back down to the process owners as a monitoring control. Using machine learning allows auditors to focus on high-risk areas and allocate resources more effectively while providing control monitoring tools for the first and second lines of defense.

• Improved Audit Quality and Insights

Over time, auditors will mature beyond common data analytical procedures in favor of deeper data analysis. AI can go beyond basic calculations and explore complex relationships within data, providing auditors with a more comprehensive understanding of the organization’s control environment. For example, many internal auditors will start by using simple AI to perform a user access review, ensuring a system’s users are all current employees with permissions matching their roles. With artificial intelligence in auditing, the next step could be to perform real-time monitoring instead of point-in-time testing, such as monitoring terminations and job movement to alert the control owner when an individual’s system access should be reviewed. Even further, with machine learning, AI can perform a deeper analysis to compare an individual’s access across all organization systems for fraud detection, potential conflict of interest, or separation of duties violations.

Insight Reporting

Auditors can use AI to produce data-driven insights and visualizations for audit committee and board reporting. AI can generate reports and visualizations that present complex information, allowing auditors to communicate findings and recommendations more effectively to stakeholders. A future state for audit team reporting could use AI to develop predictive models that estimate the likelihood of future risks based on current results, facilitating deeper discussions between audit leaders and key stakeholders.

Conclusion

In conclusion, artificial intelligence is rapidly becoming more accessible to end users. Organizations employ these capabilities daily to work more efficiently and effectively, and internal auditors can do the same. Auditors in Kenya can embrace this emerging technology to keep up with the demands and evolving risks, dig deeper into processes, and produce more meaningful insights for their organizations.