Review: AI-Driven Root Cause Analysis
We recently hosted our webinar on "AI-Driven Root Cause Analysis - The Future of Audit Investigations". In this review, we would like to recap the most important learnings and key points and provide you the recording of the webinar. The video provide great insight into the use of artificial intelligence in root cause analysis and the impact of this technology on the audit process.
Challenges: What are current challenges in audits and why makes data analytics the audit process much more efficient? In addition to requiring less manual effort, rule violations and process weaknesses are identified more reliably than using random sampling. However, it become clear that traditional methods often reach their limits when it comes to following up on analytic results. The complexity and the large number of individual line items in the results tables make it difficult to recognise the correlations between the results and identify the problem’s actual cause. When is an analytic result a finding? This is where AI-driven root cause analysis comes in and promises an effective solution to this challenge.
The role of AI-driven root cause analysis: AI-driven techniques help to analyse complex data and uncover hidden patterns. Advanced algorithms and machine learning make it possible to interpret results and identify root causes that are not apparent at first glance. This enables Internal Audit to find the root of a problem quickly and accurately.
Practical use cases: The webinar also presented practical examples in which this solution approach has already been used successfully. It was shown how flexibly the AI algorithms can be integrated into the usual analytic process and licensed as an additional function to our standardised analytics from the dab:AnalyticSuite. No detours in execution or the use of additional tools are required here. The root cause analysis results can be visualised in Power BI dashboard and shared with colleagues from the business unit. From the identification of compliance violations to the improvement of quality assurance, the AI-driven root cause analysis offers a wide range of possibilities here to optimise audit processes. In addition, it enables a faster response to emerging issues and supports companies in proactively avoiding risks.
Conclusion: The topic of the webinar "AI-Driven Root Cause Analysis - The Future of Audit Investigations" is very popular and present among business users currently. The technology promises to permanently change the way audit investigations are carried out. It remains interesting to observe how the use of artificial intelligence will develop further and what influence it will have on internal audit.