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As every risk manager knows, misfortune blows his or her way every so often. Sometimes, there's even a run of bad luck. Natural events, like hurricanes, have no memory. The next big one to happen is just as likely as the last.

In certain businesses, namely larger ones or those that have greater exposures to a single natural event--a store chain with multiple locations, for example--the problem is compounded. Or we could have a large product-liability loss, even a cluster of losses, based on a "batch" defect. This all factors into the risk finance decision.

Every risk manager knows that cost-effective insurance programs usually entail some degree of retention of loss on the firm's own account. While a variety of "rationalizations" of the process exist, most experienced risk managers make the selection based on a simple rule of thumb: Retain frequent losses.

This deceptively simple rule embodies some basic truths about what insurance is most effective at, as well as our abilities to say anything precise about the complex world of accidental losses.

Where risk managers get into trouble with retentions is by trying to be too "scientific" with the decision. They (or their advisors) attempt to introduce easily measurable factors, like insurance premiums, that have nothing to do with the decision.

By doing so, they unwittingly tie their risk finance programs to the volatile insurance market. The result is economic imbalances within the program that could come back to haunt them.

CYCLICAL RETENTION STRATEGIES

Efficiency frontiers, optimization techniques, probability density functions--all this technical apparatus is tempting, lending at least an air of credibility to the risk finance process. Seeking this added credibility, risk managers sometimes succumb to the misguided application of economic cost-benefit analysis to the retention decision.

The rule goes something like this: If the premium charged by the insurer for the retained loss is greater than the expected or average value calculated from loss data, retain. Otherwise, insure. The problem with this scientific-sounding rule is that the insurance premium will always be greater than the expected value of loss.

Insurers add a premium loading for taxes, administrative costs and profits. Economic optimization in terms of cost-benefit applied to the expected value of loss to seek optimum retentions just doesn't work, in principle.

Premiums increase continuously to infinity, theoretically, while expected losses increase the same way, just below them. The difference between the two is expenses.

Based on this view we would never buy insurance, as expected value is always less than the premium.

Applying cost-benefit on the buying side assumes that insurers always set premiums based on scientifically determined averages, expected values or loss. In the dynamic world of interdependent competition, the insurer's economic equation must be expanded to include the effects of competitors' pricing.

Interdependence in insurance leads to the alternating periods of price "war" and "peace" that we call the underwriting cycle. Insurers can, in loose concert with one another, raise premiums so as to result in considerable profits. The periods of high profit (price peace, or hard markets) are usually short-lived, as competitors start to reduce prices to try to get a bigger piece of a very profitable pie.

The result is price warfare, or soft markets, that can actually drive premiums below expected cost levels, at least temporarily. All this strategic maneuvering makes sense if in the long run it provides greater profits to the insurers than some purely competitive strategy based solely on the insurers own cost and demand conditions.

So, in hard markets insurers price considerably above expected cost, making increased retentions based on simplistic cost-benefit calculations attractive. As we slide into a soft market, with premiums driven to levels below average losses, cost-benefit selection of retentions starts to work in the opposite direction.

Soft markets tend to delay increases in deductibles that may otherwise be driven by the increasing size of the exposures as measured by, say, growth in assets, sales or total insured property values.

The nominal level of the retention stays the same, but due to growth in the exposure base, retention is actually going down relative to the increase.

There is nothing about the firm's risk profile that dictates the change. It is a product of the cyclical insurance market!

As we can see, insurance cycles force risk managers into a tail-chasing game of "follow the cycle." Over the long run, the savings wash out. What retention level do we return to over the long run? The same one suggested by our simple rule: Retain frequent losses.

The savings from this cyclical retention strategy are, long term, nil. There clearly are, however, administrative and other costs associated with trying to follow the cycle.

If a captive insurance company is used to support the retentions, we may be distorting its financial viability. Where retained losses have a differential effect on how we use loss prevention and control measures, other disruptions to rational risk management strategies can occur.

For example, with high retentions, there may not be credible statistical data available to manage loss prevention efforts internally. When such losses are insured, insurers may offer loss prevention advice and services based on experience of the wider pool of insureds.

In the long run, the cyclicity of the market costs us money. Of course, the risk manager may gain some temporary accolades in the meantime: "Look, we are reducing costs by increasing retentions!" There could, nonetheless, be real benefits forgone when the same risk manager enters a soft market with retentions that are too high (as measured by the frequency rule of thumb).

Can risk managers do anything about this disruptive tail-chasing exercise?

BREAKING THE CYCLE

The best option is to ignore outside factors. Set retentions at that level where frequency of loss drops off, regardless of the market. In this way, retention levels are responsive only to the individual risk profile of the firm. This approach requires some degree of fortitude in forgoing the apparent savings from raising retentions in a hard market. In the long run, it provides the highest value in terms of sensible retention strategy against a background of cyclical insurance markets.

Another strategy that is sometimes suggested is to set retentions at the catastrophe level. That is, set them as high as you can afford.

While this strategy has the effect of avoiding high-cost insurance in hard markets, it also forgoes the benefits in more stable times. Using this strategy, we essentially sacrifice the smoothing function of insurance altogether.

Investors, and operational management, rely on smoothing against accidental loss. Firms who use insurance only for catastrophe protection may be underutilizing the resource, to their detriment. Internal financing mechanisms, like captives, do not replace the smoothing function. At best, they can only provide such smoothing at the subentity level. High retentions may also increase the chance of serious financial difficulties when a series of losses occurs.

The most effective option would be elimination of the strategic hanky-panky that results in insurance cycles. Not only are cycles disruptive to the proper function of risk management, there is evidence they raise insurer profits above those levels that might otherwise exist. Recognizing the problem is one thing. Doing something about it is another.

As yet, regulators have not been able to determine an effective way to do so, despite the obvious disruptions such competitive cycles cause. Competition in the industry, while imperfect, may be at least workable. That means, for the foreseeable future at least, it is up to the risk manager to work with it. On the bright side of all of this is the fact that the hard-market phase of the cycle may have the positive effect of pushing some firms to more appropriate retentions.

In general, we may note that many businesses set their retentions too low. Much of this is based on a fear of going to management with an uninsured loss, even though higher retentions make the most sense in the long run. This "catching up" on retentions during the hard market seems like a very ad hoc approach to managing financial risks.

Risk managers should continuously review retention levels with the intuitive retention rule in mind. The frequency rule ignores volatile market premiums, focusing instead on a firm's own risk profile. This makes it a stable, as well as accurate, risk management strategy for reducing costs while at the same time managing volatility of loss.

If you feel you must "play the game," at least use the frequency rule as a guidepost to how much you may be bending the rules of rational risk management to do so. It may turn out that your retentions have not kept pace with the firm's loss frequency profile, and it's time to move them up. If the swings in a firm's retention policy become large, you can be assured that they probably entail large inefficiencies as well.

This discussion suggests that the insurance cycle must itself be a factor in retention decisions, as it is (unfortunately) in most other risk management decisions that relate to commercial insurance.

Perhaps, in the longer term, we can all work together to eliminate disruptive cycles and focus on risk management methods that properly address our individual risk profiles. In the meantime, risk managers need to take a realistic look at how we use the risk management tools at our disposal.

MARK JABLONOWSKIis a risk manager with more than 25 years of experience in property/casualty insurance underwriting, underwriting management, risk analysis and risk management.

March 1, 2006

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