Medical or no medical?
The end of the dilemma

A mathematical model developed by Munich Re helps life insurers decide

A name, a proposal form and a signature: this is how the often lifelong partnership between a policyholder and a life insurer begins. Whether the relationship will be a costly risk for the insurer depends to no small extent on the applicant's health status. But when is the expense of medical testing worthwhile? A new decision model developed by Munich Re Life provides the answer.

Life insurance is purchased on a voluntary basis. Understandably, those who know that they present a substandard risk, e.g. because they suffer from a disease, tend to be particularly interested in obtaining cover. In the long term, this will result in inadequate premiums and losses for the insurer. For this reason, insurers routinely require applicants to provide medical information before issuing a policy, especially if the sums insured are high.

Medical examinations such as ECGs or laboratory tests involve costs, but trying to do without them may ultimately be even more expensive for the insurer. So, how do we solve the dilemma? Munich Re Life has the answer. They have developed a new decision model which defines exact thresholds for sums insured beyond which medical testing is worthwhile to protect a life insurer's bottom line.

Avoid decisions that are economically unsound

Although the risk factors and probabilities of most medical conditions are well-established, this is the first time that this dispersed information has been systematically combined to evaluate the costs and benefits of medical testing. Previously, life underwriters had to rely solely on their medical knowledge or greatly simplified mathematical models.

Dr. Achim Regenauer, Chief Medical Director Life and Head of Munich Re's Centre of Competence for Biosciences, sums up what this has meant in practice. “Whether to examine the health status of a potential customer in a medical has largely been decided by underwriters based on common sense.” This may lead to decisions that are not transparent, often uneconomic and sometimes barely plausible. Now this dilemma has been resolved.

The model developed by Dr. Jürgen Becher uses statistical methods to aid decision-making by systematically examining all of the relevant information, including the uncertainty surrounding that information. This is no coincidence, given Dr. Becher's background. The qualified physician, who works as a medical consultant for Munich Re, graduated in medical biometrics, a science that focuses on the statistical analysis of medical data.

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