Sometimes the concept of partial credibility is explicitly considered when predictive models are being built, for example through shrinkage methods or by insertion of credibility adjustments into a Baily Minimum Bias algorithm. More often, the concept of credibility is treated in a binary manner, with individual model parameters found to be either statistically significant (credibility = 1) or not (credibility = 0).

Whether credibility is explicit or implicit, understanding the complement of credibility is important and has real-world implications for model construction. This presentation will focus on this key actuarial concept and how it is often overlooked, with negative real-world consequences.