<p>Insurance products often cover substantial claims arising from diverse sources. To accurately model these losses, actuarial models must account for high-severity claims. An effective approach is to use a mixture model that fits a distribution to losses below a certain threshold, while modelling excess losses using extreme value theory. However, selecting an appropriate threshold remains a key challenge, as existing methods are sensitive to this choice and lack a universally accepted criterion. Bayesian Model Averaging (BMA) offers a promising solution by allowing the simultaneous consideration of multiple thresholds. In this paper, we demonstrate that an error integration BMA algorithm provides a flexible framework accounting for threshold uncertainty by combining models across multiple candidate values. This approach improves model accuracy by capturing the full loss distribution while mitigating sensitivity to any single threshold choice. When interpretability or tail-specific inference is needed, the method can also identify the most likely threshold supported by the data. We illustrate the usefulness of the proposed framework through simulation studies and an application to an automobile claims dataset from a Canadian insurer. We also examine a setting without predictive variables and compare our method to conventional threshold selection procedures based on goodness-of-fit tests applied to an actuarial dataset.</p>