Life and Health
Private health insurers need to process applications promptly and systematically, while at the same time accurately assessing the risks. Munich Re offers them corresponding services that classify symptoms and, among other things, recommend whether applications should be accepted or rejected. Another tool combines different statistical methods and techniques to allow future illnesses to be predicted. This also includes estimating how health costs will develop, and which facilities will offer the best medical care in the long run.
More than other forms of insurance, life insurance, with its long-term character and intrinsic focus on the future, is subject to a variety of different developments and trends that can alter increasingly rapid. For the purposes of professional risk management in particular, relevant trends need to be identified at an early stage. We need to get to know the drivers behind them, their dynamic and dependencies. The better they understand the progress, interplay and the dynamics of such trend landscapes, the better life insurance will be able to protect itself against unwelcome surprises.
Challenges in the healthcare sector
Complex health risks are not only time-consuming for underwriters, but also require a great deal of experience, know-how and concentration. The greater the number of risk parameters that need to be taken into account on an illness, the greater the likelihood of committing errors. In developing and emerging countries, living standards are improving for broad sections of the population, and with them the number of people with health insurance. But this trend is being accompanied by numerous changes. Prosperity alters consumer behaviour, which can have an impact on health. For example, according to the World Health Organization (WHO), four times as many people now suffer from diabetes than in 1980 – the global figure now stands at 422 million.
Health and Big Data
An increasing number of "disease management programmes" (DMPs) are being offered specifically for common diseases such as diabetes, back pain or chronic cardiac insufficiency. The objective of these programmes is to increase the quality of care received by those suffering from these conditions, and to reduce insurance companies’ costs. Big data and business analytics help in the optimisation of these programmes: for example, after the analysis of a large volume of patient data such as weight and blood pressure, analytic solutions help insurance companies optimise the selection of programme participants. It also allows for a valid and undistorted measurement of the economic effects.
Many illnesses are still diagnosed too late. Yet data from smartphones can be used to identify illnesses before they occur. A joint venture with a start-up company has created a technology that can transform mobile data into health risks. This is how it works: every activity and environmental condition affects the risk of disease. Making these previously invisible factors visible and measurable, allows an early-warning system to emerge.