Predictive analytics as a response to the challenges of climate change

Predictive analytics as a response to the challenges of climate change

The climate change, which can no longer be scientifically denied, also poses great challenges for the insurance industry. The usage of artificial intelligence will become a key role here.

Horror scenarios will turn into reality for the insurance industry in connection with the changes to the global climate and the extreme weather conditions resulting from this. Entire regions will no longer be insurable according to current standards. On the other hand, there are voices that can be heard that assume that the consequences of the climate change will not be as one-dimensional and negative for insurance companies. Because at the places where something is now being done actively to protect against flooding, it can be anticipated that the damages caused by flooding will in turn be reduced. The consequences of climate change for the insurance industry are just as volatile as the calculation models from the scientists.

Forecasts are becoming more and more important

It will be important for insurance companies to appropriately react to the ever difficult conditions in the property and special insurance sectors caused by climatic change. Based on the questions of what kind of damage exactly occurred and why, forecast methods must be developed that state what kind of damage will occur where and with what probability. A look back at the past can be determined from data sets using classic business intelligence reporting instruments. A look into the future will be significantly more complex technically.

Using experiences from Predictive Analytics

One way is to use the previous promising results that insurance companies have gathered in the life and health insurance sectors through the use of artificial intelligence and other methods, including machine learning. Modern predictive methods are already used successfully for fraud prevention and detection: individual risks of insured parties can be examined automatically using health data or the chances of success are determined by programs for health prevention.

Due to reasons of the profitability of a business model, it will become a necessity to offer individual rates, for example, for residential building insurance. The more granular this data is, the better it will be for the insurer. In order to be able to make valid predictions regarding risks, it is not sufficient to merely have intelligent forecasting models, but rather different data sources will be needed as well. Information about micro weather conditions, satellite imagery and coordinates, prepared values and information from scientific climate models.

Action must be taken now

The scientific forecasts are not very optimistic. The climate is changing. The people only have a partial impact on how strong this change will be. A new degree of complexity in the calculations shows that the effects of the change could mutually strengthen each other.

The industrial nations only have a relatively small window of time to implement important measures to achieve their agreed upon climate goals. And insurance companies must also react in a tight time frame to quickly prepare for the challenges of the future: the creation of interfaces to different data sources and vendors, the implementation of AI methods to process such data.

This is in turn associated with the actuarial calculation of risks and the development of rates that consider the climatic risks at any given site. The list of tasks is long. And that is why action should be taken today to not end up in a world that cannot be insured.


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