hardhat

Workers' compensation, retrospective and experience rating

Introduction

hardhat aims to bring workers' compensation calculations into a single Python package by converting the algorithms found in the CAS Exam 8 material into code. We will start with Table M construction (since it's one of the easier papers).

Examples

Table M Construction

from hardhat import TableM
from hardhat.utils.utility_functions import load_sample

df = load_sample(key='brosius')

table_m = TableM(
    data=df, 
    experience='Actual Loss', 
    index="Risk"
)

print(table_m)

      Actual Loss  Entry Ratio
Risk                          
1           20000          0.2
2           50000          0.5
3           60000          0.6
4           70000          0.7
5           80000          0.8
6           80000          0.8
7           90000          0.9
8          100000          1.0
9          150000          1.5
10         300000          3.0

print(table_m.phi(r=1.2))

0.21000000000000002

Lee Diagram

table_m.lee_diagram().show()

Relevant Papers

Table M Construction - Brosius

Experts needed!

If you happen to have experience in workers' compensation (*cough*, ahem), your participation will be greatly appreciated. This is intended to be a collaborative effort open to the general public.