In many respects, the easiest data to work with is a large group of losses of similar size. A large private passenger automobile book is a great example. There are bigger claims and smaller claims, but the bulk are small, and are in a pretty predictable range.

The polar opposite might be a small book of medical malpractice claims. Though such claims can be small, they can also easily range into seven and eight figures. Worker’s comp, especially for higher-risk businesses, can be similar.

Multi-million dollar losses would seem like enough to worry about. But no; there’s another problem here. When one or two large claims occur in a year with just a few smaller claims, the large claims are pushing the numbers around by themselves. Now it can become difficult to tell which claims are typical: the large ones or the small ones? And small claims, which might be settled privately and quickly, play out very differently than large claims, which have to wind slowly through the courts.

For those of us charged with estimating the likely future value of these losses, there are a few approaches available. One is to pull the large losses out of the data altogether. This makes sense when the large losses can be argued to be truly different from the small ones, and when there is good reason to think they will not be repeated. (Though the business side and the audit side often have different opinions about this!)

Another is to choose appropriate caps on the data. If development of losses on large claims is capped at a low level, their influence on the data can be diminished, and more fundamental patterns show up clearly.

Large losses may not jump out in the same way as zero-dollar years or single-digit claim counts. But they can have a huge impact on the bottom line, so it’s essential to keep them in mind.

With insurance losses, credibility problems take a number of forms. One of the most common is having too few years of data. When we can only look back to one or two years of losses, it’s hard to tell good years from bad. More technically, it’s hard to establish a credible estimate of likely future losses.

Standard methodologies might, numerically speaking, produce a result. But the meaning of that result may not be credible. If the business’s operations (and risk-management practices) have been consistent for five or ten years, however, it is easier place some trust in those calculations.

A related problem arises when there are too few losses. Practically speaking, of course, fewer losses is always better, but it makes the numerical work harder. If a typical year has only one or two losses—even if there are many years—it’s hard to say what a typical year would look like. In other words, the data may reflect those individual losses more than they reflect the underlying risk.

Both of these problems arise frequently with captive insurance companies and self-insurance programs, especially those of small or mid-sized firms. These companies may have few losses and a limited number of years of data. Though the captive or self-insurance arrangement may make business sense, it may nonetheless be hard to pin down credible actuarial estimates.

A number of approaches can be used in the face of credibility problems. I’ll take up some of these approaches in coming posts.