Data analytics in the insurance industry
A large volume of collated information alone does not guarantee deeper insights. To gain these and use raw data economically, complex analytical methods and intelligent processing are required. Munich Re employs data analytics in many areas and has developed applications from which insurers and insured persons benefit equally.
Data analytics – Applications in the insurance industry
“Examples of artificial intelligence (AI) can be found in various areas of the insurance industry today – for example in underwriting and claims”, says Wolfgang Hauner, Chief Data Officer at Munich Re. “AI can support consultants in their work, helping with fact-checking and recommending courses of action, for instance.” By contrast, processes in underwriting are becoming smarter: “All the available data and sources are called upon here, improving processes and the customer experience.” According to Wolfgang Hauner, things are also getting smarter in claims. “Reaction times are getting shorter and insurers can more glean more information – which in turn leads to efficiency gains and better decisions.”
Business opportunities arising from data analytics
For example, big data and analytics allow the efficient processing of losses with low volumes and high demand rates. Thanks to innovative data technologies, insurance can also be offered that was previously considered unprofitable or difficult to assess in terms of its risk. In concrete terms, that could mean more attractive covers for certain types of diabetes or performance guarantees for wind farms. Furthermore, processes and information can be amalgamated in order to transfer some underwriting elements to automated risk management platforms. With the aid of AI workflows, it is also possible to develop applications which enable the early recognition of losses, e.g. fire damage.
Best practise for data analytics
Munich Re has already successfully piloted such an application: “Our Early Loss Detection (ELD) platform continuously evaluates over 16,000 news sources and searches for previously defined loss events. Using geocoded risk and loss information, the system checks whether insured properties may be affected”, explains Wolfgang Hauner. “If this is the case, those responsible for the system automatically receive a message containing all the relevant information. Property losses can thus be recognised swiftly, and appropriate measures taken enabling us to drastically reduce the scope of claims management”, says David Feghelm, Senior Solutions Manager Claims at Munich Re.
But ELD’s functions go beyond the rapid identification of loss events. All individual losses are stored and collated in a database; every case brings with it new information on causes and impacts. Using this data, future loss trends can be recognised earlier and modelled with greater precision. ELD is thus a powerful tool helping insurers reduce costs by getting ahead on claims management, while also gathering information that will be of use when modelling new covers in the future.
More Munich Re data analytics and AI projects
Alongside ELD, Munich Re has also developed further prototypes and products which are being tested or are already in use. One of these is Vision Intelligence, an AI-based loss-recognition system using photographs. This is based on neural networks (also used in areas such as language recognition systems and medical diagnostics) and supports insurers in the categorisation of property losses due to natural disasters, for example, and in ascertaining the approximate scope of losses.
The risk management platform M.I.N.D., another successful pilot project already successfully in use on the market, helps insurers, reinsurers and loss adjusters to create transparency on risks together and better appraise these. In this context, AI also helps identify potential cases of insurance fraud. M.I.N.D. documents historic information on losses and compares this with current pictures of damage. Attempted fraud can thus be more easily identified and better prevented, and consequential losses can be compared with loss events that have already occurred.