How AI is enhancing the management of climate-related risks in the insurance industry

Artificial Intelligence (AI) has emerged as a powerful tool to help insurers and reinsurers better assess, price, and manage climate-related risks. From advanced predictive modelling to real-time claims processing, AI is transforming the way the industry operates. However, its integration is not without challenges. AI is neither a silver bullet nor a replacement for fundamental risk expertise – it is an enabler that, when used wisely, strengthens decision-making and enhances resilience.

AI could enhance risk modelling

One of the most promising applications of AI in reinsurance is its ability to improve risk modelling. Machine learning algorithms analyse vast and complex datasets – including satellite imagery, climate projections, and real-time sensor data – to detect patterns and predict future risks with greater accuracy. AI-driven models have enhanced the industry’s ability to anticipate wild-fires, floods, hurricanes, and droughts, leading to better pricing and risk selection.

However, while AI-based climate models offer more precision, they are not infallible. Climate change is introducing new risk dynamics that even the most sophisticated algorithms may struggle to capture. AI remains dependent on the quality and completeness of the data it processes, and biases in datasets can lead to misjudged risk assessments. For instance, AI models trained on historical flood data may underestimate the impact of changing precipitation patterns caused by global warming.

Moreover, transparency is a challenge. Many AI models operate as black boxes, meaning insurers may not fully understand how certain predictions are generated. This lack of interpretability can complicate regulatory compliance and erode trust in AI-driven decision-making.

AI can streamline operations, but it won’t replace underwriting expertise

Beyond risk modelling, AI is transforming insurance operations, particularly in claims management and underwriting. Automation and image recognition technologies allow insurers to process claims faster, reducing administrative burdens and improving client experience. In agriculture insurance, for example, AI-driven remote sensing can automatically assess drought or flood damage, enabling quicker payouts and reducing disputes.

In underwriting, AI helps analyse vast amounts of structured and unstructured data to refine risk selection. Advanced algorithms can integrate climate risk indicators, such as real-time wildfire risk scores, into underwriting decisions, leading to more responsive pricing models.

Yet, AI has its limitations. Insurance and reinsurance rely not only on data-driven assessments but also on human judgment and market expertise. AI can enhance underwriting, but it cannot fully replace the nuanced understanding of risk developed by experienced professionals. The challenge for the industry is to find the right balance – leveraging AI to augment, not replace, underwriting expertise.

Furthermore, the adoption of AI raises ethical and regulatory concerns. Automated underwriting decisions must remain fair and transparent, avoiding unintended biases that could lead to unequal access to coverage. Regulators are increasingly scrutinizing the use of AI in financial services, meaning insurers must ensure their AI-driven processes align with compliance and governance standards.

AI as a strategic asset bold?

While AI offers immense potential, the industry must approach its implementation strategically. The most successful insurers and reinsurers will not be those that merely adopt AI for AI’s sake, but those that integrate it in a way that reinforces their fundamentals – solid underwriting, strong capital management, and deep risk expertise.

One of the key challenges in scaling AI solutions is data access and collaboration. AI models thrive on high-quality, diverse datasets, yet our industry often operate with fragmented data. Industry-wide collaboration – through open data platforms or partnerships with climate re-search institutions – will be essential to unlock AI’s full potential in climate risk management.

Another challenge is ensuring that AI does not create a false sense of precision. While AI models can provide granular risk insights, they must be constantly validated against real-world events. The industry must resist the temptation to over-rely on AI-driven outputs without human oversight and stress testing.

Conclusion: a pragmatic path forward

AI is undeniably transforming the way the insurance and reinsurance industry manages climate-related risks. From improving predictive modelling to streamlining claims and underwriting, it offers a powerful set of tools to enhance resilience in an era of climate volatility.

However, its adoption must be guided by pragmatism rather than trends. AI is not a substitute for risk fundamentals, nor is it a shortcut to better underwriting. The real value of AI lies in its ability to reinforce and complement human expertise, enabling insurers and reinsurers to make more informed, data-driven decisions while maintaining the critical judgment and experience that have always defined the industry.

This aligns with what we believe at Hannover Re: that success in reinsurance is built on “somewhat different thinking”, where innovation is used to reinforce – not replace – the fundamentals of risk management. AI is a strategic asset, but only if deployed with a long-term vision, discipline, and a commitment to supporting clients in a changing world.

Published on 23 April 2025