Credibility Based Smoothing Using Ghost Trend

Many actuarial tasks, such as analysis of pure premiums by amount of insurance, require an analysis of data that is split among successive “buckets” along a line. Often, there is also significant randomness in the data. That results in process error volatility that affects the (usually average) values of the data within the buckets, so some smoothing of these values is needed if they are to be truly useful. The “ghost trend” approach allows for a high-quality smoothing of those values. Therefore, it helps to produce smoothed values that are more useful relativity factors, loss distributions for pricing aggregate losses, etc. An enhanced approach, integrating the ghost trend approach with other smoothing approaches is also provided. That composite approach provides additional flexibility in dealing with large datasets and datasets that are greatly affected by random differences from point to point.