Earlier this month I published a post on LinkedIn about underwriting “challenging” flood in California’s Central Valley. It generated a decent readership and some likes, but, most importantly, it generated some comments. One of the comments posted was a very prescient piece of commentary, and it deserves a blog post to explore the topic it raised: The limitations of analytics.
The comment came from Mr. Tim Pappas (a VP at Gen Re), and I am grateful to him for raising this important topic. Here are the points he raises that this post will address:
Assessing the risk of flood is a messy business – there is no debate or dispute about that. The real-world variables that determine where flood waters (churned by the incredible forces of unsettled nature) will ultimately go are impossible to predict, just as it is impossible to predict when floods will happen. But it is this complete unknowability that makes analytics necessary to work with flood risk (and all risk, really) – analytics are an imperfect substitute when measurements are not possible.
Imperfect? Absolutely – all models are wrong, but some are useful (thanks again to Dr. Box). A caveat must be understood by anyone underwriting flood: the models and analytics are not 100% accurate, ever…not even close. Below is a list (with links to supporting material) of some ways flood models and data are flawed:
Thus, to address squarely the first point above – it is true that an insurer’s results can be harmed by assuming their data and analytics will be entirely accurate. The remaining points to be addressed suggest ways to handle this fact.
For the remaining three points, it is helpful to explore what an analytic actually is. Wikipedia states:
Analytics is the discovery, interpretation, and communication of meaningful patterns in data.
In the context of flood-risk-assessment analytics for underwriting, I would define it thus:
The automated interpretation of multiple datasets to estimate the relative risk of flooding at a specified location.
From this definition, it is possible to outline the key points that address the limitations of analytics:
With all of this in mind, here are the remaining three limitations addressed:
Finally, Mr. Pappas mentions the importance of the distinction between precision and accuracy. For underwriters, accuracy answers: “what’s the risk at this location?” Precision answers: “how dependable is that answer?”
These are all important things for users of underwriting analytics to understand, and I agree when Mr. Pappas says it is important for providers of analytics (like me) to help.