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Big Community Interviews Chief Data Officer at Munich Re

Big Community had the previlege of an interview with Wolfgang Hauner, Chief Data Officer at Munich Re. This was ahead of the 2nd Asia Conference on Big Data and Analytics for Insurance held in Singapore. Wolfgang was gracious enough to be interviewed on his thoughts around how the insurance arena is being affected by Big Data Analytics and what is the future he foresees.

Wolfgang Hauner

  1. Currently Big Data analytics are being used mainly to increase market penetration. How do you see this trend working in favour for the industry?

Yes, market penetration and sales analytics has been a major application for big data.
For the insurance industry, I see the benefit of using big data analytics not only to enhance marketing and distribution, but also to retrieve new business potentials in other major areas of the value-chain.

First, Underwriting: for example predictive underwriting. We use big data analytics to enable more precise estimation for the risk, therefore also enables a more accurate price.

Second, Claim handling & risk management: for example we at Munich Re are applying cutting edge technologies like image recognition and natural language processing to optimize the claim process and significantly increase the customer experience. (potential example: visual claim for car insurance or natural catastrophe insurance. MIND platform)

  1. Property and casualty insurance is said to be using advanced data analytics more than other products offered in the industry. What is the reason for that and should there be effort to improve data analytics usage in other products as well?

Yes, property & casualty has been applying advanced data analytics for their business quite broadly. Data Analytics and Artificial Intelligence enable easier, fast, cheaper and deeper insights and Munich Re is already using leading edge technologies and analytical skills for clients and itself.

However, life and health insurance has be using advanced data analytics quite successfully already for longer. Like, using wearables to estimate policy holder’s fitness level. Health and Life insurers also apply advanced machine learning to optimize their underwriting questionnaire-list both for increasing customer user experience in online channel and for quick quotation in the traditional distribution (broker) channel.

Are the current data sources available for Big Data Analytics in insurance adequate? if not how could it be improved?Big Data heavily challenges the current and future insurance markets. The insurance/reinsurance industry has large treasury of data. Existing data sources often contain highly valuable information. To adequately utilizing this data advantage to provide better product, we are still facing several challenges.

  1. the data are usually stored in different departments in different line of businesses. It can be challenging to combine the information. One solution for that can be a data lake. Munich Re is currently setting up our own data lake to share and leverage our own treasury of data. Of course we are sharing only those data which is legally and ethically sharable, for example combine the weather data with the crop insurance, house-hold insurance or natural catastrophe insurance.
  2. The data may be stored in a un-structured way, like text and documents in paper or scanned form, image and photos. We are working on applying cutting edge artificial intelligence like natural language processing and image recognition to extract the structured information from unstructured data.
  3. In addition, new digital media like wearables and mobile apps is opening completely new channels for collecting information.
  1. We read in several articles that Big Data is transforming the Insurance industry as an example it is being used to set premiums. Are there ways that your company is working on areas like premiums? If so what would they be?

Yes, we at Munich Re are using big data analytics to set premiums for risk where usually not insurable – not price-able before. This can make the uninsurable insurable.

For example, for green energy / wind turbine, we are exploring the possibility of leveraging IoT and Big-Data to set the premium of the base risk, which was usually not insurable by us. Another example, people with diabetics above certain level have usually difficulty finding life insurance. We are exploring the possibility utilising medical apps to make them successively insurable again.

  1. Can Big Data be used to make policies more personalised or is there a danger that it will actually have the reverse effect?

We believe that the big data technology can make the insurance offers for some products more personalised. For example, IoT can make the insurance of your device tailored to the condition of your devices and the way your device is operated. Telematics opens the possibility of providing driving-behaviour-specific insurance cover. (Social Media and other public information enables the insurers to know their policy holder better, which sets the condition for a more personalized offer.)  Leveraging IoT, the insurer could also offer predictive maintenance service to individually prevent or reduce the potential damage. However, personalisation is also limited due to data protection law and regulation for certain products.

 

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