Avoid complexity for the sake of complexity.
Our approach to modeling is to aim for the least complicated model possible without sacrificing accuracy of results. Too many model providers seem to have gotten caught up in creating overly complex models in an attempt to be as close to “perfect” as possible.
Perfection, unfortunately, is never possible with financial modeling. Robust results for any model are constrained by the accuracy of underlying assumptions — and both capital market and liability assumptions are imperfect at best.
No capable investment professional would make the significant-digit mistake of carrying calculations to greater accuracy than that of the original data, but ERM and DFA models too often try to deliver results with three or more significant figures based on underlying capital market and liability assumptions with only one or two.
The more serious concern is that this pursuit of model perfection comes at substantial cost in terms of complexity – which, in turn, significantly increases opportunity for error: both model miss-specification and user error.
There are a number of models currently available to insurers. Most suffer from the same limitations: They are complex for the sake of complexity, opaque, unnecessarily time-consuming to use, and expensive. Perhaps the overly complex models can command higher prices, but they don’t better serve the client.
A better approach offers the advantages of transparency and ease-of-use without any real sacrifice in the robustness of results.