Hannover Re on the role of claim causes in life insurance pricing

Hannover Re on the role of claim causes in life insurance pricing | Insurance Business Canada

Reinsurance

Hannover Re on the role of claim causes in life insurance pricing

Detailed data on claim causes ensures accurate policy pricing and payouts

Reinsurance

By
Kenneth Araullo

The causes of claims in life insurance products play a critical role in how these policies are designed and priced, according to insights from Hannover Re actuary Chessman Wekwete.

For example, double indemnity death policies, which pay out double the sum assured if a death is due to accidental causes, require an accurate determination of the claim’s cause to establish the correct payout.

According to a report, this necessity has led most life insurance companies to collect detailed cause-of-claim information as part of their claims processes. Additionally, state agencies and regulators worldwide also gather such data.

Wekwete highlighted that one of the main applications of cause-of-claim statistics is in the design and pricing of life insurance products. For instance, products with double or multiple indemnity provisions offer higher payouts if the cause of death is accidental.

These products are designed because accidental causes are generally perceived as less prone to risks like anti-selection and moral hazard. This perception allows insurers to offer higher sums assured with minimal underwriting.

Another application, Wekwete noted, involves initial exclusion periods for natural causes of claims. These policies only pay for natural causes after an exclusion period of typically one to two years, helping to mitigate anti-selection risks.

Cause-specific products, which provide coverage for particular risks such as cancer or disability, are also based on cause-of-claim data. These products are often targeted at specific groups to offer more affordable coverage tailored to their needs.

Wekwete also explained that cause-of-claim information is integral to other operational and risk control functions within life insurance companies. For example, enforcing exclusion clauses for self-inflicted injuries or monitoring for non-disclosure at the time of sale often relies on early claims data.

This data can trigger reviews of potential non-disclosure, particularly if certain causes of claims arise soon after the policy is issued.

The collection and use of high-quality claims data have significant implications for life insurance, Wekwete said. The importance of cause-of-claim information has, in some cases, influenced how causes of events are recorded. For instance, during periods when HIV was excluded from claim payments, there was a reluctance among medical professionals to list HIV as a cause of death.

Similarly, during the COVID-19 pandemic, deaths were often certified as COVID-19-related, especially when governments were offering financial and other support to affected individuals.

Other factors, such as socio-economic attributes and policyholder behavior, also influence the distribution of claim causes. Wekwete pointed out that differences in age, gender, and whether someone has insurance can affect claim causes. Additionally, the type of insurance product, level of underwriting, and duration since underwriting can all play a role.

Wekwete emphasized the importance of considering calendar time impacts, including medical advances and changes in government health initiatives, when analyzing claim causes. The economic conditions of a country and the emergence of pandemics can also influence these distributions.

He recommended that any analysis of claim causes be conducted within a specific context, taking into account the attributes of policyholders and the broader socio-economic environment.

Wekwete concluded that there is broad consistency between the population and insured lives in the data analyzed, suggesting that population data can be useful for applying insights to insured portfolios, depending on the type of benefit.

The differences between death benefits and disability benefits, as well as the impacts of gender and age, should be carefully considered in any analytic work.

What are your thoughts on this story? Please feel free to share your comments below.