Does smart risk management provide more transparency and security? Where will AI be applied successfully in the future? How valid are the results or is the crux in the detail?

Can the interaction of “man and machine” positively influence the ability to plan and avoid or reduce future risk potentials? Will artificial intelligence revolutionise the risk manager’s toolbox?
In recent months, with its text-based dialogue system, Chatbot ChatGPT, Open AI has written a new chapter in the history of the “mass-market” use of artificial intelligence (AI). For many companies, however, AI has long been one of their key success factors. Digital twins, simulations or intelligent machines help to accelerate innovations, optimise quality management and production processes, or improve the efficiency and service life of entire plants.
However, does smart risk management provide more transparency and security? Where will AI be applied successfully in the future? How valid are the results or is the crux in the detail? These are just some of the questions risk managers should be asking themselves now.

Five reasons for the symbiosis of risk manager and AI

  • AI is used to enable the accurate analysis and assessment of existing risks. AI systems can identify complex data patterns and relationships in risk assessment. For example, an AI system can be used to determine systematic risks by using pattern recognition tools and machine learning. This better identifies the likelihood of events that increase risk.

    In practice: AI-based supply chain management can detect risk events for suppliers and predict future supply chain outcomes by monitoring a variety of data sources.
  • AI helps make risk management processes more efficient by providing automated alerts, predicted warnings and automated decisions.

    In practice: By using AI, predictive maintenance of systems and structures, such as machines and buildings, can be carried out even before a problem occurs. This prevents or minimises disruptions or downtime.
  • AI can monitor the effectiveness of existing risk management processes by performing risk and cost analyses to determine the most appropriate risk mitigation measures.

    In practice: Specifically in finance, e.g. credit risks, large amounts of data about customers’ payment behaviour, their financial situation, historical lending practices and other factors can be analysed to optimise the lending process or identify deviations.
  • AI can identify and predict potential risks by using machine learning to forecast future risk areas.

    In practice: Dynamic risk modelling of climate risks can support strategic decisions – e.g. for site selection for the construction/acquisition of new key sites.
  • Finally, AI helps monitor potential risks by continuously looking for potential risks in the environment or triggering alerts when they are detected.

    In practice: AI can process and analyse data about the activities of employees in high-risk environments. This can be particularly useful to improve safety in  environments where dangerous or fatal accidents are imminent. AI algorithms can evaluate behavioural patterns that are detected prior to accidents. This can be used to run predictive scenarios that improve safety procedures and prevent incidents.

Data quality is key

While AI systems can recognise and process complex data patterns, their results are only fully comprehensible and valid if they can be traced back to a high-quality, correct, and meaningful database.
Verification of AI will be the future challenge for the risk manager, as AI systems can also make very complex and opaque decisions. The following six points should definitely be considered to verify and correctly interpret AI data:

  • Checking data quality: AI systems are only as good as the data on which they are trained. It is important to ensure that the data used to build the AI models is of a high quality and free from bias or manipulation.
  • Review the training processes: It is important to understand how AI models were trained and what parameters were used. This helps to ensure the integrity of the models.
  • Testing AI models: testing on different datasets can confirm the accuracy and predictive power of the model.
  • Using clarification methods: When AI models are opaque, clarification methods, such as decision trees, can be used to visualise and understand the models’ decisions.
  • Verify the results: The results of AI models should be regularly reviewed to ensure that the models continue to work correctly and factor in changes in business processes or data.
  • Review by independent experts: Finally, it may be useful to have the use of AI models reviewed by independent experts to confirm their accuracy and integrity


For risk managers, AI systems have become an important tool in their toolbox to support effective, efficient risk management. It enables them to act faster and more accurately, to identify and assess risks before they develop into a potential threat.

Furthermore, they must be able to understand and interpret the results of the AI systems to ensure that the results are comprehensible.
The insurance industry – and especially reinsurers with their R&D activities – is one of the industries that relies on AI and has recognised its enormous potential: AI-powered data analytics enables insurers and their clients to develop a much deeper understanding of risks so that they can be more effectively mitigated or covered to some extent by new insurance solutions, whether in natural catastrophes, healthcare or financial, ESG or geopolitical risks.

Michael Brunner

Johannes Vogl

General Manager GrECo Risk Engineering

T +43 5 04 04 – 160

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