  We develop a claim score based on the Bonus-Malus approach proposed by [7].
We compare the fit and predictive ability of this new model with various models
for of panel count data. In particular, we study in more details a new dynamic
model based on the Harvey-Fernand\`es (HF) approach, which gives different
weight to the claims according to their date of occurrence. We show that the HF
model has serious shortcomings that limit its use in practice. In contrast, the
Bonus-Malus model does not have these defects. Instead, it has several
interesting properties: interpretability, computational advantages and ease of
use in practice. We believe that the flexibility of this new model means that
it could be used in many other actuarial contexts. Based on a real database, we
show that the proposed model generates the best fit and one of the best
predictive capabilities among the other models tested.
