How can claims from different types of insurance be optimally bundled and efficiently linked? This question was pursued by Despoina Makariou (University of St. Gallen) together with George Tzougas (Heriot-Watt University) in the recently published article “The multivariate Poisson-Generalized Inverse Gaussian claim count regression model with varying dispersion and shape parameters” in the journal Risk Management and Insurance Review. In it, they presented a claim count regression model with varying dispersion and shape to model different types of claims in non-life insurance. The implementation of the model was demonstrated using bodily injury and property damage count data from a European motor insurer.