Some risks follow a very predictable pattern of claims, and if that's the case, we normally will see these claims reflect what we call a normal distribution, and this is a natural occurring distribution of risk that happens in many natural environments, for example, people's heights, and you can look at the mean people's heights and then spread in a frequency diagram the standard deviation of people's heights. Well, you can often do this in insurance. So for example, household and car insurance follow very predictable loss patterns. So this means they can be estimated mathematically with a really high degree of confidence. So this means, obviously, that insurance is easy to price. So highly predictable lines of insurance will follow normal distribution patterns and make it highly predictable and easy to price. However, there are some insurance risks which don't follow these predictable patterns, for example, terrorist insurance, and in those situations, because it's much more unpredictable, it's much more expensive to insure the risk. So if the risk is very predictable and follows a normal distribution of loss pattern, then pricing it is fairly easy, but if the risk is unpredictable and doesn't follow normal distribution patterns, that's gonna be a much more expensive policy to price. Furthermore, you often find the actual mechanics, the expenses of making those estimates is much cheaper where you have fairly predictable, normal distribution patterns and much more expensive, where the risks are more predictable, mainly because you need more analysts to actually understand and research the risks.