  This paper focuses on modelling loss reserving to pay outstanding claims. As
the amount liable on any given claim is not known until settlement, we propose
a flexible model via heavy-tailed and skewed distributions to deal with
outstanding liabilities. The inference relies on Markov chain Monte Carlo via
Gibbs sampler with adaptive Metropolis algorithm steps allowing for fast
computations and providing efficient algorithms. An illustrative example
emulates a typical dataset based on a runoff triangle and investigates the
properties of the proposed models. Also, a case study is considered and shows
that the proposed model outperforms the usual loss reserving models well
established in the literature in the presence of skewness and heavy tails.
