Using Bayesian modelling techniques, we helped a general insurer improve their estimates of claims reserves despite high levels of uncertainty and changes in reserving philosophy.
Situation
An insurer with large commercial liability exposures was unhappy with standard actuarial techniques for estimating their reserves, wanting help using more advanced statistical approaches to the problem.
Solution
Through the use of Bayesian hierarchical modelling, we fit a growth-curve model based on the cumulative claims amounts across each accounting period. Our generative model allowed us to account for complications such as changes in reserving philosophy and changes is business mix.
Business Outcomes
Visual exploration of the policy data and concentrations of risk
Better understanding of uncertainties in estimates
Fuller understanding of implications of reserving changes
More efficient capital usage due to increased confidence of model outputs
Model Outputs
Key Model Features
Business knowledge naturally incorporated into the model
Model accounts for uncertainty
Generative modelling approach enables straightforward iteration of the model
Posterior distributions provide intuitive outputs
Implications
Pooling of data naturally results in more sensible outputs
Reduced need for manual data adjustments enhanced auditability
Reserves and a measure of their uncertanty
Key Benefits for Client
Increased confidence in model outputs
Explicit statement of assumptions improved awareness
Better understanding of output uncertainty improved decision making
Knowledge of the approach allowed its use in other business problems
