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The Chain Ladder method is an actuarial technique used for projecting incurred insurance claims to their ultimate loss values. The data exists as claims triangles where the claims for each accounting year increments down the rows and the claims for each development period increments along the columns. This claims triangle can be represented in a triangular upper matrix (along the anti-diagonal) and the Chain Ladder technique works by filling in the lower part of the matrix using ratios of claims in previous accounting years and development periods.

In this example, we show how an implementation in R is sped up by calling an equivaluent implementation in C++ from R using the Rcpp interface.

```// [[Rcpp::depends(RcppArmadillo)]]
#include <iostream>
#include <vector>
#include <iterator>
#include <algorithm>  // most of the algorithms
#include <numeric> // some numeric algorithm

using namespace std;
using namespace arma;
using namespace Rcpp;

// Code for the age-to-age factor when the column index is given
double GetFactor(int index, mat mTri) {
int nRow = mTri.n_rows;
mat subMat = mTri.submat(0, index, nRow - (index + 2), index + 1);
rowvec colSums = arma::sum(subMat, 0);
double inFact = colSums(1)/colSums(0);
return inFact;
}

// Code for getting all the factors from the triangle
vec GetFactors(mat mTri) {
int nCol = mTri.n_cols;
vec dFactors(nCol - 1);
for (int i=0; i < nCol - 1; ++i) {
dFactors(i) = GetFactor(i, mTri);
}
return dFactors;
}

// This is code for the cumulative product of a vector
vec cumprod(vec mvec) {
int nElem = mvec.n_elem;
double cprod = mvec(0);
for (int i = 1; i < nElem; ++i) {
cprod *= mvec(i);
mvec(i) = cprod;
}
return mvec;
}```

The following function returns the fully projected triangle

```// [[Rcpp::export]]
SEXP GetChainSquareCpp(SEXP mClaimTri) {
NumericMatrix nMat(mClaimTri);
int nRow = nMat.nrow(), nCol = nMat.ncol();
mat armMat(nMat.begin(), nRow, nCol, FALSE);

vec dFactors = GetFactors(armMat);
mat revMat = fliplr(armMat);
vec dAntiDiag = diagvec(revMat);
dAntiDiag = dAntiDiag.subvec(1, nCol - 1);
double dMult;
vec prodVec;
for (unsigned int index = 0; index < dAntiDiag.n_elem; ++index) {
dMult = dAntiDiag(index);
prodVec = dFactors.subvec(nCol - index - 2, nCol - 2);
prodVec = cumprod(prodVec);
armMat(index + 1, span(nCol - index - 1, nCol - 1)) = dMult*prodVec.st();
}
return wrap(armMat);
}```

The preceding C++ code is used by the following R code for simulating the claims triangles

```# Age-To-Age Factors
ageFact <- seq(1.9, 1, by = -.1)

# Inflation Rate
infRate <- 1.02

# Function to reverse matrix columns
revCols <- function(x) {
x[,ncol(x):1]
}

# Similar to jitter()
shake <- function(vec, sigmaScale = 100) {
rnorm(n = length(vec), mean = vec, sd = vec/sigmaScale)
}

# Row generation funtion
GenerateRow <- function(iDev, dFactors = cumprod(ageFact),
dInflationRate = 1.02, initClaim = 154) {
shake(initClaim)*shake(c(1, dFactors))*(dInflationRate^iDev)
}

# Function to generate a claims matrix
GenerateTriangle <- function(iSize, ...) {
indices = 1:iSize
mClaimTri = t(sapply(indices, GenerateRow, ...))
# Reverse columns to get the claims triangle
mClaimTri = revCols(mClaimTri)
# Assign nan to lower triangle
mClaimTri[lower.tri(mClaimTri)] = NA
mClaimTri = revCols(mClaimTri)
return(mClaimTri)
}```

We then use R code for projecting the traingle

```# Get claims factor at a particular column index
GetFactorR <- function(index, mTri) {
fact = matrix(mTri[-c((nrow(mTri) - index + 1):nrow(mTri)), index:(index + 1)], ncol = 2)
fact = c(sum(fact[,1]), sum(fact[,2]))
return(fact[2]/fact[1])
}

# Function to carry out Chain Ladder on a claims triangle
GetChainSquareR <- function(mClaimTri) {
nCols <- ncol(mClaimTri)
dFactors = sapply(1:(nCols - 1), GetFactorR, mTri = mClaimTri)
dAntiDiag = diag(revCols(mClaimTri))[2:nCols]
for(index in 1:length(dAntiDiag)) {
mClaimTri[index + 1, (nCols - index + 1):nCols] =
dAntiDiag[index]*cumprod(dFactors[(nCols - index):(nCols - 1)])
}
mClaimTri
}```

We now run a timed test comparing chain ladder running in R natively and being called from C++ functions using the Rcpp interface

```library(microbenchmark)
x <- GenerateTriangle(11)
microbenchmark(GetChainSquareR(x), GetChainSquareCpp(x), times = 10000L)

Unit: microseconds
expr     min      lq  median     uq     max neval
GetChainSquareR(x) 235.106 245.836 252.348 258.65 31378.5 10000
GetChainSquareCpp(x)   5.984   6.946   9.573  11.99   852.8 10000
```