Natural catastrophes in 2017 came as a rude awakening to the global (re)insurance industry, as the disasters clocked the second-highest annual loss ever in recent history. Munich Re Member of the Board of Management, Dr Torsten Jeworrek says it is time that new coverage concepts are needed.
2017 was a wake-up call. After a number of relatively benign years, natural disasters in 2017 caused overall losses of US$340 billion. This was the second-highest annual loss ever and almost double the previous year’s level – a figure roughly equivalent to the annual GDP of Denmark, Egypt or Israel. Insurers had to pay out a record US$ 138 billion in losses.
It is crucial that insurers and reinsurers take account of statistically rare loss events in their risk management calculations. One such rare event was the torrential rainfall and severe flooding that Hurricane Harvey brought to the Houston area in August. The series of three extreme hurricanes that struck within the space of just a few weeks – Harvey, Irma and Maria – is also indeed rare but by no means impossible, especially as experts predict that certain types of extreme weather events are likely to become more frequent in the future as a result of climate change. 2017 therefore gave us a foretaste of what we can expect in the future.
Once again, significantly less than half the losses were covered by risk transfer solutions: the share of insured losses was higher than the previous year, but at 41% still well below the 50% mark. And this even though more than four-fifths of all losses were in North America, with its high level of insurance density.
I believe these facts and figures not only highlight the business opportunities available to insurers. They also show the enormous economic challenges that people, companies and public institutions face in tackling the consequences of disasters. Given these circumstances, insurers are almost obliged to develop new covers that better meet clients’ needs. The use of data from sensors or satellites and systems incorporating elements of artificial intelligence now make it possible to offer entirely new insurance concepts. Here is one example: Crop insurance for farmers in regions where it is difficult to estimate losses using traditional methods of damage assessment. One positive effect of such a system is faster payouts by insurers, which help victims to get back on their feet more quickly following a disaster. Studies have shown that emerging countries in particular are able to recover more quickly after extreme catastrophes if insurance density is high, as the international insurance community carries a higher proportion of the risks spread across many shoulders.
In order to develop new types of cover, we need in-depth knowledge of the risks and how they are changing. We have been analysing the effects of climate change on severe weather events for several decades now. Currently top of our agenda is to achieve a better understanding of which regions and weather hazards are already subject to changed risk patterns due to global warming. And we are also looking at ways in which risk prevention can help limit losses. With all the knowledge at our disposal, we want to help ensure that insurance can play an even broader role in society than before and so help the economies of developing and emerging countries to recover more quickly from the effects of disasters.