  Forecasting the outstanding claim liabilities to set adequate reserves is
critical for a nonlife insurer's solvency. Chain-Ladder and
Bornhuetter-Ferguson are two prominent actuarial approaches used for this task.
The selection between the two approaches is often ad hoc due to different
underlying assumptions. We introduce a Dirichlet model that provides a common
statistical framework for the two approaches, with some appealing properties.
Depending on the type of information available, the model inference naturally
leads to either Chain-Ladder or Bornhuetter-Ferguson prediction. Using claims
data on Worker's compensation insurance from several US insurers, we discuss
both frequentist and Bayesian inference.
