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An Introduction to Insurance
Ideal Requisites for Insurability – Part 1
In this section you will learn the following:
Why so many risks cannot be insured by private insurance companies
The definition of insurable risks by private insurers
Why catastrophes such as floods are not insurable risks by private insurers
Soon after the devastation of Hurricane Katrina became known, the Mississippi attorney general filed a lawsuit against insurers claiming that the flood should be covered by homeowner’s insurance policies. The controversy over coverage was explored in the September 8, 2005, New York Times article, “Liability Issue: Wind or Water?” Is this question so open-ended?
Are all pure risks insurable by private (nongovernmental) insurers? No. The private insurance device is not suitable for all risks. Many risks are uninsurable. This unit is devoted to a discussion of the requirements that must generally be met if a risk is to be insurable in the private market.
As a practical matter, many risks that are insured privately meet these requirements only partially or, with reference to a particular requirement, not at all. Thus, in a sense, the requirements listed describe those that would be met by the ideal risk.
Nevertheless, the bulk of the risks insured fulfill-at least approximately-most of the requirements. No private insurer can safely disregard them completely.  A risk that was perfectly suited for insurance would meet the following requirements:
The number of similar exposure units is large
The losses that occur are accidental
A catastrophe cannot occur
Losses are definite
The probability distribution of losses can be determined
The cost of coverage is economically feasible
Many Similar Exposure Units
As noted, an insurance organization prefers to have a large number of similar units when insuring a possible loss exposure. The concepts of mass and similarity are thus considered before an insurer accepts a loss exposure.
Some insurance is sold on exposures that do not possess the requirements of mass and similarity, but such coverage is the exception, not the rule.
An example is insurance on the fingers of a concert pianist or racehorses.
When there are no masses of exposures, the coverage is usually provided by specialty insurers. Lloyd’s of London, for example, is known for insuring nonmass exposures such as Bruce Springsteen’s voice.
A major requirement for insurability is mass; that is, there must be large numbers of exposure units involved.
How large is a “large group”? For insurance purposes, the number of exposure units needed in a group depends on the extent to which the insurer is willing to bear the risk of deviation from its expectations.
For automobile insurance, there must be a large number of automobiles to insure. For life insurance, there must be a large number of persons.
An automobile insurance company cannot insure a dozen automobiles, and a life insurance company cannot insure the lives of a dozen persons.
Suppose the probability of damage to houses is 1/1,000. An insurer might assume this risk for 1,000 houses, with the expectation that one claim would be made during the year.
If no houses were damaged, there would be a 100 percent deviation from expectations, but such a deviation would create no burden for the insurer. On the other hand, if two houses were damaged, the claims to be paid would be twice the expected number.
This could be a severe burden for the insurer, assuming average or higher loss severities. By increasing the number of similar houses insured to 10,000, the expected number of losses increases to ten, but the stability of experience is increased. That is, there is a proportionately smaller deviation from expected losses than would exist with a group of 1,000 houses.
Similarly, if the group is increased to 100,000 houses, the variation between actual and expected losses would be likely to increase in absolute terms, but it would decline proportionately. One additional loss from 100,000 houses is proportionally less than one additional loss from 10,000 houses and even less than one additional loss from 1,000 houses.
The loss exposures to be insured and those observed for calculating the probability distributions must have similarities. The exposures assumed by insurers are not identical, no matter how carefully they may be selected.
Nevertheless, the units in a group must be reasonably similar in characteristics if predictions concerning them are to be accurate. For example, homes with brick sidings are similar for insurance purposes.
Moreover, probability distributions calculated on the basis of observed experience must also involve units similar to one another. Observing the occupational injuries and illnesses of a group of people whose ages, health conditions, and occupations were all different would not provide a basis for calculating workers’ compensation insurance rates.
No two houses are identical, even though physically they may appear to be.
They cannot have an identical location and, perhaps more important, they are occupied by different families.
For example, clerical work typically involves much lower probabilities of work-related loss than do occupations such as logging timber or climbing utility poles.
Estimates based on experience require that the exposure units observed be similar to one another. Moreover, such estimates are useful only in predicting losses for comparable exposures.
The risks assumed by an insurer must involve only the possibility, not the certainty, of loss to the insured. Insurable losses must be accidental or fortuitous; that is, they must be a matter of chance.
Ideally, the insured should have no control or influence over the event to be insured. In fact, this situation prevails only with respect to limited situations. Intangible and physical hazards influence the probability of loss.
Prediction of potential losses is based on a probability distribution that has been estimated by observing past experience. Presumably, the events observed were, for the most part, fortuitous occurrences.
The use of such estimates for predicting future losses is based on the assumption that future losses will also be a matter of chance. If this is not the case, predictions cannot be accurate.
Small Possibility of Catastrophe
The possibility of catastrophic loss may make a loss exposure uninsurable.
When an insurer assumes a group of risks, it expects the group as a whole to experience some losses-but only a small percentage of the group members to suffer loss at any one time. Given this assumption, a relatively small contribution by each member of the group will be sufficient to pay for all losses. It is possible for a large percentage of all insureds to suffer a loss simultaneously; however, the relatively small contributions would not provide sufficient funds.
Similarly, a single very large loss would also require large contributions. Thus, a requisite for insurability is that there must be no excessive possibility of catastrophe for the group as a whole.
A catastrophic loss to an insurer is one that could imperil the insurer’s solvency.
Small Possibility of Catastrophe (Continued)
Insurers must be reasonably sure that their losses will not exceed certain limits. Insurers build up surpluses (net worth) and contingency reserves (funds for future claims) to take care of deviations of experience from the average, but such deviations must have practical limits.
If losses cannot be predicted with reasonable accuracy and confidence, it is impossible to determine insurance premium rates, the size of surpluses, or the net worth required.
Small Possibility of Catastrophe (Continued)
Catastrophic losses may occur in two circumstances. In the first, all or many units of the group are exposed to the same loss-causing event, such as war, flood, tornado, mudslide, forest fire, hurricane, earthquake, tsunami, terrorist attack, or unemployment.
Exposure units are susceptible to dependent loss when loss to one exposure unit affects the probability of loss to another. Thus, fire at one location increases the probability of fire at other homes in the area: their experience is dependent.
If one insurer had assumed the risk of damage by wind (hurricane) for all houses in the Miami, Florida, area, it would have suffered a catastrophic loss in 1992 when many structures were damaged simultaneously by Hurricane Andrew (and in fact several insurers were unable to withstand the losses).
The 2005 hurricanes, which caused the largest-ever insured losses, are an example of a megacatastrophe that affected many units. These are examples of exposure units that suffer from the same cause of loss because of geographic proximity.
In the early days of insurance in the United States, many fire insurance
companies concentrated their business in small areas near their headquarters.
This worked in New York City, for example, until a major fire devastated large sections of the city in 1835. Because of their concentrated exposures, several insurers suffered losses to a large percentage of their business.
The insurers were unable to pay all claims, and several went bankrupt.
Small Possibility of Catastrophe (Continued)
A second type of catastrophe exposure arises when a single large value may be exposed to loss. September 11, 2001, represents such catastrophic loss.
When insurers and reinsurers (the insurers of the insurance companies) see the peril as having a far higher probability than previously perceived, they know that they can no longer accurately predict future losses, and their immediate reaction is to exclude the peril.
Because of regulation and oversight , the industry cannot make policy changes instantaneously.  When private insurers can no longer provide coverage, a solution may be to create pools.
Tremendous value was concentrated in the towers of the World Trade Center. The possibility of a human-made catastrophe of such magnitude was not anticipated.
Private insurers stopped short of calling the terrorist attacks “acts of war”-which would have been excluded from coverage-and honored the policies covering the World Trade Center and the lives of the victims.
However, one consequence was the industry’s action to immediately exclude terrorism coverage from new policies until the Terrorism Risk Insurance Act (TRIA) of 2002 provided stop-gap coverage from the federal government.
Losses must be definite in time, place, and amount because, in many cases, insurers promise to pay in dollar amounts for losses if they occur during a particular time and in a particular geographical area.
One other reason the requirement of definiteness is essential is that it is necessary to accumulate data for future predictions. Unless such data can be accurate, they cannot provide the basis for useful predictions.
The contract may cover loss by fire at a specified location. For this contract to be effective, it must be possible to determine when, where, and how much loss occurred.
If this cannot be established, it is impossible to determine whether the loss is covered under the terms of the contract. The fact that pain and suffering is hard to measure in dollar terms increases the insurer’s risk when calculating rates for liability insurance
Determinable Probability Distribution
For an exposure to loss to be insurable, the expected loss must be calculable. Ideally, this means that there is a determinable probability distribution for losses within a reasonable degree of accuracy.
Insurance premium rates are based on predictions of the future, which are expressed quantitatively as expected losses. Calculation of expected losses requires the use of estimated probability distributions.
Probability distributions based on experience are useful for prediction; however, only when it is safe to assume that factors shaping events in the future will be similar to those of the past.
For this reason, mortality (death) rates during times of peace are inappropriate for estimating the number of insured deaths during times of war.
Similarly, the introduction of new technologies such as foam blanketing makes past experience of fire damage a poor indicator of future experience. Yet, because the technology is new and no theory exists as to what the losses ought to be, actuaries have little information on which to base lower rates.
The actuary must use subjective estimates as well as engineering information to develop proper rates.
Determinable Probability Distribution (Continued)
When the probability distribution of losses for the exposure to be insured against cannot be calculated with reasonable accuracy, the risk is uninsurable.
An example of purported uninsurability due to inability to predict losses is the nuclear power industry.
Insurance experts convinced government officials in 1957 that the risk of loss caused by an incident at a nuclear power site was too uncertain (because of lack of experience and unknown maximum severity) for commercial insurers to accept without some government intervention.
As a result, the government limited the liability of the owners of nuclear power plants for losses that could arise from such incidents.
END of Part 1 of UNIT
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