rippy 0.0.5

Reinsurance Pricing in Python

rippy

Reinsurance Modeling and Pricing in Python!

Getting Started

Install rippy from pypi:

pip install rippy

Introduction

Rippy is a simple, fast and lightweight simulation-based reinsurance modeling package. It has two main components:

1. Frequency-Severity, otherwise known as compound distribution simulation

   Rippy contains the FrequencySeverityModel class to set up and simulate from compound distributions with a variety of frequency and severity distributions

   from rippy import FrequencySeverityModel, Distributions
   claims = FrequencySeverityModel(Distributions.Poisson(5),Distributions.Pareto(0.5,100000)).generate()
   

2. Reinsurance contract calculation

   Rippy can then calculate the recoveries of a number of types of reinsurance contract for the simulated claims.

   from rippy import XoL
   xol_layer = XoL(name="Layer 1", limit=1000000, excess=100000, premium=1000)
   xol_layer.apply(claims)
   xol_layer.print_summary()
   

Rippy is based on the scientific python stack of numpy and scipy. It is designed for interoperability with numpy and ndarrays, so for example the simulated claims can be operated on with numpy ufuncs:

import numpy as np
capped_claims = np.minimum(claims,2000000)

Under the hood/bonnet, rippy represents the simulations of the Frequency-Severity model in sparse storage, keeping two lists of simulation indices and values:

sim_index	values
0	13231.12
0	432.7
2	78935.12
3	3213.9
3	43843.1
...	...

Frequency-Severity simulations can be aggregated (summed within a sim_index), which results in a standard np.ndarray

aggregate_claims = claims.aggregate()

np.array([13663.82,0,78935,12,47957.0,....])

Configuring the simulation settings

The global number of simulations can be changed from the config class (the default is 100,000 simulations)

from rippy import config
config.n_sims = 1000000

The global random seed can also be configured from the config class

config.set_random_seed(123456)

Rippy uses the default_rng class of the numpy.random module. This can also be configured using the config.rng property.

Project Status

Rippy is currently a proof of concept. There are a limited number of supported distributions and reinsurance contracts. We are working on:

• Adding more distributions and loss generation types
• Adding support for Catastrophe loss generation and reinsurance contracts
• Adding support for more reinsurance contract types (Surplus, Stop Loss etc)
• Grouping reinsurance contracts into programs and structures
• Stratified sampling and Quasi-Monte Carlo methods
• GPU support
• Reporting dashboards

Issues

Please log issues in github

Contributing

You are welcome to contribute pull requests