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R In Actuarial Pricing Teams. Chibisi Chima-Okereke Mango Solutions. Agenda. Actuarial Survey Geographical Area. UK Actuaries & CAS (Casualty Actuarial Society). Source Palisade ( @Risk ): http://www.palisade.com/downloads/pdf/Pryor.pdf. Main Areas Of Work.

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r in actuarial pricing teams

R In Actuarial Pricing Teams

Chibisi Chima-Okereke

Mango Solutions

slide3

Actuarial Survey

Geographical Area

UK Actuaries & CAS (Casualty Actuarial Society)

Source Palisade ( @Risk ): http://www.palisade.com/downloads/pdf/Pryor.pdf

main areas of work
Main Areas Of Work

UK Actuaries & CAS (Casualty Actuarial Society)

Source Palisade 2006 ( @Risk ): http://www.palisade.com/downloads/pdf/Pryor.pdf

slide5

Main area of work in

which software is used

UK Actuaries & CAS (Casualty Actuarial Society)

Source Palisade ( @Risk ): http://www.palisade.com/downloads/pdf/Pryor.pdf

percentage of respondents using each package
Percentage of respondents using each package

UK Actuaries & CAS (Casualty Actuarial Society)

Source Palisade ( @Risk ): http://www.palisade.com/downloads/pdf/Pryor.pdf

percentage of statistical package users using individual packages

Use of Statistical Packages

Percentage of statistical package users using individual packages

UK Actuaries & CAS (Casualty Actuarial Society)

Source Palisade ( @Risk ): http://www.palisade.com/downloads/pdf/Pryor.pdf

r is the programming language of statistics
R is the programminglanguage of statistics

Spreadsheets are unstructured computer programs:

The Risks Of Using Spreadsheets for Statistical Analysis (IBM White Paper): http://public.dhe.ibm.com/common/ssi/ecm/en/imw14297usen/IMW14297USEN.PDF

current actuarial r packages
Current Actuarial R Packages

http://cran.r-project.org/web/packages/

example data
Example Data

Data Source (Simulated): Modern Actuarial Risk Theory Using R: Kaas, Goovaerts, Dhaene, and Denuit.

dynamic sql query example

require(RODBC)

doMyAnalysis <- function(myYear = 2001){

sqlString <- paste("SELECT * FROM policyClaims WHERE Year='",myYear,"'", sep = "")

myData <- sqlQuery(channel = odbcConnect(dsn = "InsuranceData"), query = sqlString)

odbcCloseAll()

myGlm <- glm(noclaims ~ age + bonusmalus + region + mileage, data = myData, offset = log(exposure), family = poisson(link = "log"))

myCoeffs <- summary(myGlm)$coeff

theNames <- colnames(myCoeffs)

myCoeffs <- data.frame(myCoeffs)

myCoeffs <- data.frame(rownames(myCoeffs), myYear, myCoeffs)

colnames(myCoeffs) <- c("Coeff", "Year", theNames)

print(myYear)

return(myCoeffs[1,])

}

analysisOutPut <- lapply(2001:2010, doMyAnalysis)

analysisOutPut <- do.call(rbind, analysisOutPut)

rownames(analysisOutPut) <- 1:nrow(analysisOutPut)

Dynamic SQL Query Example
plotting analysis

myFun<- function(x){

hist(x$GrossIncurred, col = "blue", xlab = "GIC", main = paste("Histogram of GIC for bonus malus \n group ", x$BonusMalus[1], " and year ", x$Year[1], sep = ""))

}

pdf(file = paste(myFolder, "myPlots.pdf", sep = ""), width = 7, height = 7)

by(policyTable, list("Year" = policyTable$Year, "BonusMalus" = policyTable$BonusMalus), FUN = myFun)

dev.off()

Plotting Analysis

C:\Users\cchima-okereke\Documents\R\RScripts\ActuarialPricing\tmp\myPlots.pdf

effects package
Effects package

effects package from John Fox: http://www.jstatsoft.org/v08/i15/paper