Concrete recommendations for insurers

Depending on the medical condition, as many as 10 to 15 parameters may be involved. Munich Re's experts individually select those that have a major impact, and include them in the decision model. But some residual uncertainty remains, as estimates are never 100% accurate. For example, disease prevalence, a measure used to indicate how frequently a disease occurs in the population, may not be representative of the case rates among insurance applicants. Social and economic factors, for example, may result in considerable differences.

Example: Prevalence of diabetes mellitus by socio-economic class

There is a striking difference in prevalence between people from upper and lower socio-economic classes, the latter group having more than double the risk of developing diabetes mellitus.

"For us, the challenge lies in ascertaining the influence this uncertainty has on the result", Becher explains. This is why the result is never a single figure but always a range that is used to make justified recommendations for or against a medical. By way of example, the diagram below illustrates the uncertainties involved in estimating disease prevalence.

Factors that may influence disease prevalence among applicants

It becomes clear that the proportion of insurance applicants affected by a given disease may differ from the proportion affected in population as a whole. For example, there is what occupational health physicians call the "healthy worker effect", a kind of natural selection process. People who are seriously ill will usually not be able to work, resulting in a higher proportion of healthy people among the working population.

Significant increase in transparency and cost-effectiveness

"Primary insurers have expressed great interest in our model", says Dr. Regenauer. This is not surprising, given that such a tool has not previously been available to the insurance industry. Rather, threshold sums have evolved historically through competition in the marketplace. Putting it simply, Munich Re's decision model is able to "think ahead". All the possible decision options are factored in, along with the potential outcomes. The result is a clear increase in transparency and cost-effectiveness for medical underwriting, and consequently for the life insurance industry as a whole.

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