ChainLadder

Help wanted!

This package is very early in its development cycle.

Interested in developing loss reserving techniques in Julia? Consider contributing to this package. Open an issue, create a pull request, or discuss on the Julia Zulip's #actuary channel.

Quickstart

using ChainLadder
using CSV
using Test
using DataFrames

csv_data = ChainLadder.sampledata("raa")
raa = CSV.read(csv_data,DataFrame)

t = CumulativeTriangle(raa.origin,raa.development,raa.values)

lin = LossDevelopmentFactor(t)

s = square(t,lin)

total_loss(t,lin)

outstanding_loss(t,lin)

Bundled sample data

Load sample data

csv_data =ChainLadder.sampledata("raa")
raa = CSV.read(csv_data,DataFrame)
t = CumulativeTriangle(raa.origin,raa.development,raa.values)

Available datasets (courtesy of Python's chainladder):

abc
auto
berqsherm
cc_sample
clrd
genins
ia_sample
liab
m3ir5
mcl
mortgage
mw2008
mw2014
prism
quarterly
raa
tail_sample
ukmotor
usaa
usauto
xyz