Big data, key part of innovation processes at Munich Re
Digitalisation and new technologies mean that far greater volumes of data are becoming available for evaluation within a much shorter time frame. Data analysis can be used to examine client portfolios to reveal trends, improve processes, optimise holdings, and provide targeted support to sales. The more global and comprehensive the data basis, the more valuable the data will be. The new dimensions of data and their analysis require some competences that not all insurance companies have. New competitors may be able to analyse data sets more quickly and apply the results in new applications - thus placing traditional insurers under pressure.
So there is also a strategic dimension to big data. "The most important aspects are the will and ability to invest in sufficient resources and work together with the right partners", explained Ludger ARNOLDUSSEN, member of the Board of Management of Munich Re, at the Baden-Baden Meeting 2016.
"That is exactly what we are doing when building up our own know-how and IT structures", he added In order to be able to harvest information more quickly, the topic of big data is a key part of innovation processes at Munich Re. "It means new, clearly defined and more flexible insurance solutions and support services for our clients. We are seizing these opportunities - with our own resources, and supported by external specialists. We are also regularly involving the clients at an early stage in order to develop perfectly customised solutions and applications that can also be adopted at a global level."
There are already examples of how big data tools can be used to improve the pooling of information and make processes more efficient so as to create customised or totally new insurance solutions:
A fully automated monitoring of 7,000 digital news channels with a daily volume of 250 gigabytes allows fire losses in the United Kingdom and the USA to be recorded more quickly and cheaply. Comparing this data with the risks in portfolios allows for better identification of risk patterns, so that claims management can be faster and more effective.