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
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:
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.
General Manager GrECo Risk Engineering
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