Insurance pricing is backwards and primitive, harking back to an era before computers. One standard (and good) textbook on the topic is Non-Life Insurance Pricing with Generalized Linear Models by Esbjorn Ohlsson and Born Johansson (Amazon UK | US). We have been doing some work in this area recently. Needing a robust internal training course and documented methodology, we have been working our way through the book again and converting the examples and exercises to R, the statistical computing and analysis platform. This is part of a series of posts containing elements of the R code.
We have created and managed analytics teams in commercial organizations (mainly telecommunications) across Europe. The teams were using SAS or SPSS. Our company now has a commercial analytics as a service offering and we mainly use R.
Commercial Analytics is the kind that makes money. From data to dollars, insights to income, this is all about how to run the business better. To do it and to do it well you need certain capabilities in place. This article builds a map of those business capabilities to help you assess, understand, and plan your business.
We have 20 years of experience of big data environments within a variety of industries including Research, Banking, Insurance, and Telecommunications. We have especially worked with customer data: Marketing, Risk Management, customer segmentation and -profitability, and customer-driven product development.
John Kay muses on interpreting statistical data:
save() function in the R platform for statistical computing is very convenient and I suspect many of us use it a lot. But I was recently bitten by a “feature” of the format which meant I could not recover my data.
Because it is Friday and because we collect quotes, here is one on statistics being the best and worst of disciplines. Which one of the two views are closest to your opinion?
For my sins, I have done more than my fair share of analysis in Excel. I am quite capable of building and maintaining 130Mb spreadsheets (I had a dozen of them for one client). Excel is pretty much installed everywhere, so it is sometimes the only way to get started getting commercial value of the data in the organisation. But I don’t like it and let’s have a look at one reason why. In order not to always pick on Microsoft, we use another application, but you get the same results with Excel.
The US$ 3 million Heritage Health Price competition is on so we take a look at how to get started using the R statistical computing and analysis platform.