Data and Models… or Success and Ruin Await

Posted by Ivan Maddox on May 24, 2017 8:00:00 AM

Early last month RIMS published a great article called Flaws in the Data – it’s about data, analytics, and “data driven decisions” (another term for using data in analytics). From the start, it was begging for a blog post, but all the action on private flood (missed it? try this, this, and this … and even this) has consumed us here for the past month. Let’s take a look at what “flaws in the data” is about.

The crux of the article is that more and more businesses are investing (hugely) in collecting data, and then exploiting it to make better decisions. Pretty uncontroversial stuff:

Cutting-edge companies like Amazon, Google, Netflix and Uber have exploited data from the get-go, developing advanced models and algorithms to facilitate business decisions, and are leaders in the persistent use of data collection and analysis.


Ultimately, the effort can also help to put a business on the path toward the wider use of predictive analytics and automation systems, including machine learning and artificial intelligence, which could lead to faster and more efficient decision-making.

Right? Cool!

But here is where it gets interesting:

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Topics: Analytics, Data and Models

On Elephants and Blind Underwriting

Posted by Ivan Maddox on May 11, 2016 7:00:00 AM

Two weeks ago, we published a post on how analytics aren’t so smart after all. It has been a sensation (only our Cat Modeler’s Guide to the Protection Gap has been read more this year), so it is only natural that we pursue the topic further.

The blog that lit up a little corner of the web was about the limitations of analytics, specifically flood risk analytics. It turns out there is some interesting science and folklore about the topic, so let’s take a look.

Analytics are an attempt to measure an immeasurable phenomenon. Cat risk analytics (flood, quake, hail, etc.) are perfect examples, as they aspire to determine the chance of something happening in a given location during a given timeframe – it is not possible. However, by evaluating a combination of different types of information, they can begin to produce results that are useful for underwriters who need those unknowable answers.

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Topics: Flood Risk, Effective Underwriting, Analytics

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