The data set under study is seen to violate the conditions for application of the CL in a number of ways. The difficulties of adjusting the CL to allow for these features of the data are noted (Sections 3).
Regression, and particularly GLM regression, is introduced as a structured and rigorous form of data analysis. This enables the investigation and modelling of a number of complex features of the data responsible for the violation of the CL conditions. These include superimposed inflation and changes in the rules governing the payment of claims (Sections 4 to 7).
The development of the analysis is traced in come detail, as is the production of a range of diagnostics and tests used to compare candidate models and validate the final one.
The benefits of this approach are discussed in Section 8.
Keywords: chain ladder, generalised linear model, GLM, loss reserving, regression, superimposed inflation.
