Indemnity cat bonds – a geoscience PhD just isn’t enough

As the use of indemnity triggers grows, it is important that investors understand the additional risks that they are assuming

The cat bond market has experienced rapid changes in pricing, in the last 24 months. Changes in other terms and conditions are harder to measure but just as important. One such change has been the increased use of indemnity triggers. This style of trigger is popular amongst sponsors but Robert Medeiros warns that investors would be wise to make sure they have a firm grasp of the additional risks that they are accepting.

A catastrophe bond that uses an indemnity trigger will default if the actual claims paid by a sponsor exceed a given threshold. This is helpful in increasing the value of the hedge for the sponsor but it means that an understanding of meteorology or seismology is not enough to enable investors to quantify the risk. In addition, it is important to understand the effects of natural catastrophes on property and the way in which that particular insurance company pays claims.

The use of indemnity triggers in cat bonds has increased dramatically over the past two years and they have been used by a large majority of this year’s bonds. The mechanism is popular with cedents as it eliminates concerns over the basis risk that affects other trigger types. This trend is likely to continue as the market softens and cedents use cat bonds interchangeably with traditional reinsurance.

This article highlights the range of complexity of insurance portfolios and identifies two issues that investors should focus on – data quality at the time of underwriting and the claims paying process that following a loss.

Types of property insurer
The insurance market can be divided into three segments in order of increasing complexity: personal lines (homeowners primarily), commercial middle market, and commercial national and global accounts.

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Modelling challenges increase as the complexity of the subject business grows

Personal Lines have large numbers of individual dwellings with low to moderate values, simple construction, and relatively less generous insurance policies. The data that goes into the models should be accurate because there are fewer variables. These are favourable factors. On the negative side there is a significant regulatory risk in claims handling and most companies would rather “pay as much as you owe as fast as you owe” to avoid customer complaints.

Middle Market Commercial Property has higher values, multiple types of construction, and broader insurance policies. Catastrophe data should be good but there are more variables so more chance for error. Also, commercial policies usually have some type of business income coverage which is very hard to model accurately. Unlike personal lines there is very little regulatory risk.

National or Global Commercial Property is without a doubt the most volatile market segment. Insurance values are very high with complex business income scenarios. Policy forms are very broad including coverages that are not modelled, such as flood. Construction is complex, especially on businesses with large process equipment. In addition it is not unusual for large insurance programs to be written on a shared basis with other insurers, and this presents additional complexity.

Risk factor – Data Quality
Insurers and reinsurers use catastrophe models for individual account and portfolio pricing. This has driven the market to emphasise completeness of data (i.e. fewer “unknowns”) but it remains with the insurer to validate the accuracy of the data.

For example, every homeowner in Florida knows that a hip roof results in a lower premium. How does an insurer validate that the roof is actually hip? Also in Florida the problems with the pre-2012 wind mitigation forms are well known. Has the insurer re-inspected dwellings with those older forms and corrected the data?

On commercial insurance certain secondary modifiers are known to lower the loss expectancy. Does the insurer have a process for validating the primary and secondary modifiers? Submissions to cat bond investors should include statements detailing the insurer’s method of validating catastrophe data.

Risk factor – Quality of claims department.
The sheer volume of claims in major hurricane will seriously challenge any claim operation, leading to risks not contemplated in cat models. The homeowner’s market has a higher public profile and is more susceptible to these claim risks, including:

Competition for independent adjusters:
Smaller homeowners companies do not have adjusters for catastrophe claims and so rely on independent adjusters. These individuals may do work for a number of companies and are compensated on a fee per-claim basis. When demand is high, fees will increase, driving up loss adjustment expenses (a phenomenon often described as "post-event inflation").

Regulatory Risk:
Legislators and regulators are not reluctant to insert themselves as policyholder champions. Insurers will be under pressure to pay claims quickly, and this can lead to overpayments. In the words of one company claim executive the goal is, “To pay as much as you can, as fast as you can”.

Full roof replacement: Cat models may statistically calculate a partial roof loss but the reality is that most partial losses will result in full roof replacement.

We encourage investors in indemnity-triggered cat bonds to quantify the volatility associated with these “real world”, non-modelled risks. If price competition continues and margins come under further pressure, the ability to quantify and fully appreciate these risks may help investors avoid poorly-priced deals.


Robert Medeiros CPCU, ARe, AMIM, ASLI is the founder and President of Lighthouse Consulting, LLC,, which provides underwriting audit and consulting services to the off-shore reinsurance market. He can be contacted at

Posted: Monday, June 2nd, 2014