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#31




Quote:
An advantage of C/AY is that if you are going to incorporate any trend or loss development, that tends to fit more naturally with accident years than policy years. Also makes it easier to reconcile the loss and exposure data in the model to financial statements/reports. 
#32




Quote:
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#33




Resource I came across for the CSPA Exam 3 material. Contains R code for doing the following:
https://thecasinstitute.org/wpconte...n01162019.pdf
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#34




So after underwriters make their adjustments to the factors, how can you easily compare the original model with the new model?
I can do it manually in Excel by exporting the most recent credible policy year, creating a "code" column that contains the variable name concatenated with the variable value, using a Vlookup to pull in coefficients, and then manually multiplying the factors together to get a prediction. Then from there, can calculate gini and lift charts. I can replicate that process in R too with some more work, but is there any easier way I'm overlooking? 
#37




data.table >> dplyr so long as the data fits in RAM.
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#39




Quote:
There are some insurance data sets in Kaggle, but they usually have generic named fields. 
#40




Personally, I find the data.table syntax easier to understand than the dplyr set of verbs, but that may be familiarity. It certainly is more compact, and can "do more with less"
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