Commercial insurance post-COVID
Published on 13 de April de 2020
Commercial insurance post-COVID
It is true that the creation of insurance products has always been based on the analysis of the historical data available to the institutions, which is why the underwriting departments require old data in order to make changes in coverage or launch new products
The reality that we are experiencing these days means that this analytical procedure based on past data cannot be used to make modifications to products or, to some extent, predict future risks and damage. The question is how commercial insurance post-COVID
is going to be?
There are those who can say that in the past we have already experienced exceptional situations, which have not only had an economic impact on a global level, but also on changing consumer habits or the way of doing business, such as 9/11, the Fukushima nuclear disaster, hurricanes like Sandy, SARS and MERS, etc.
However, the impact of the COVID is much greater and more homogeneous, since despite the fact that previous events have had a great impact, its geographical impact has always been limited and never at a global level with levels of damage to the population, economies and similar businesses. In short, the economy has never stopped at a global level.
Therefore, the harsh reality is that the past is no longer the only predictor of what will happen in the future, so we will have to incorporate other types of data in order to continue advancing and improving the risk prediction capacity of our models, be more efficient and automate processes.
To visualize this, it is important to put as an example real use cases that will make possible to put in value the use of this external data and that from Wenalyze we have helped to solve with our open and external data platform.
To begin with, the first use case is to clean up existing client data on such basic information as the address of their clients, they have erroneous or inaccurate data in 30% of their portfolio. If these numbers seem implausible to you, we invite you to test them with us.
If the level of dirty data in something so fundamental is so high, we cannot help but think what levels it can reach in the exact determination of risks, for example, in portfolios that are renewed. In this sense, only a small percentage of the policies that are automatically renewed in the portfolios of the insurance companies are updated with the data of the risks or their coverage, exposing the insurance companies to greater losses, and the clients to coverage that is not aligned with the reality of their business.
Another use case is to increase underwriting efficiency by using this type of external data, automating underwriting and increasing efficiency by at least 20%. Being much more agile in the development of new products more adapted to the market and a long etcetera.
The insurance consumer is no stranger to the process and evolution of digitalization in which society finds itself due to confinement. If the sector does not respond with solutions for a new type of user and reality, the consumer will not understand why this has not been done.
Furthermore, if I have not convinced you with all the above, I would like to make a final argument: what would you be willing to pay to know the reality of the risk of the almost three million SMEs in Spain in an easy and automated way? And to know who will come out of this crisis stronger in their business models?
Insurance companies have in their hands the possibility of knowing the reality of the risks of all the SMEs in Spain. They also have the capacity to segment them and know which ones will emerge strengthened from this crisis. In short, I would like to ask you, which insurer is willing to apply data analysis as an action not only to reduce the economic impact, but also to improve its market position?
