GLM on continuous variable not showing 100% A/E I am fitting a gamma GLM on insurance claims predicting severity. Using log( claim count) as offset and ultimate claim amount as target variable and a gamma error structure. Model converges but Actual / Predicted claim amount does not converge to 100% in the modeling data.
Per my understanding, when a GLM converges the within sample model prediction is always 100%. Am I missing something here?
I also tried using an intercept only model expecting 100% A/ E on overall level but still unable get it.
Is it some additional arguments I need to provide, or is gamma not a good fit.
I then went to lognormal fit but with not much success


Per my understanding, when a GLM converges the within sample model prediction is always 100%.

This is not correct. For instance, you may have multiple observations with the exact same predictor values, but different target values. Since the model has only identical predictors to work with, its fit can't hit both (different) target values.
Also, if your model does have an extremely high in-sample fit, it is almost certainly overfitting. In-sample accuracy is a notoriously poor indicator to out-of-sample performance.
