Multi-Year Non-Life Insurance Risk of Dependent Lines of Business in the Multivariate Additive Loss Reserving Model

2016 ◽  
Author(s):  
Lukas Josef Hahn
Keyword(s):  
Life Insurance ◽  
Insurance Risk ◽  
Loss Reserving ◽  
Additive Loss

scholarly journals The Reduction of Initial Reserves Using the Optimal Reinsurance Chains in Non-Life Insurance

Mathematics ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 1350
Author(s):  
Galina Horáková ◽  
František Slaninka ◽  
Zsolt Simonka
Keyword(s):  
Life Insurance ◽  
Continuous Time ◽  
Ruin Probability ◽  
Proportional Reinsurance ◽  
Insurance Risk ◽  
Optimal Reinsurance ◽  
Insurance Contracts ◽  
Time Period ◽  
Different Types ◽  
Capital Injections

The aim of the paper is to propose, and give an example of, a strategy for managing insurance risk in continuous time to protect a portfolio of non-life insurance contracts against unwelcome surplus fluctuations. The strategy combines the characteristics of the ruin probability and the values VaR and CVaR. It also proposes an approach for reducing the required initial reserves by means of capital injections when the surplus is tending towards negative values, which, if used, would protect a portfolio of insurance contracts against unwelcome fluctuations of that surplus. The proposed approach enables the insurer to analyse the surplus by developing a number of scenarios for the progress of the surplus for a given reinsurance protection over a particular time period. It allows one to observe the differences in the reduction of risk obtained with different types of reinsurance chains. In addition, one can compare the differences with the results obtained, using optimally chosen parameters for each type of proportional reinsurance making up the reinsurance chain.

scholarly journals Penalising Unexplainability in Neural Networks for Predicting Payments per Claim Incurred

Risks ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 95 ◽  
Author(s):  
Jacky H. L. Poon
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Life Insurance ◽  
Predictive Power ◽  
Real Life ◽  
Model Framework ◽  
Key Stakeholders ◽  
Machine Learning Model ◽  
Loss Reserving ◽  
Risk Pricing

In actuarial modelling of risk pricing and loss reserving in general insurance, also known as P&C or non-life insurance, there is business value in the predictive power and automation through machine learning. However, interpretability can be critical, especially in explaining to key stakeholders and regulators. We present a granular machine learning model framework to jointly predict loss development and segment risk pricing. Generalising the Payments per Claim Incurred (PPCI) loss reserving method with risk variables and residual neural networks, this combines interpretable linear and sophisticated neural network components so that the ‘unexplainable’ component can be identified and regularised with a separate penalty. The model is tested for a real-life insurance dataset, and generally outperformed PPCI on predicting ultimate loss for sufficient sample size.

scholarly journals INSURANCE RISK WITH VARIABLE NUMBER OF POLICIES

2008 ◽  
Vol 22 (2) ◽  
pp. 213-219
Author(s):  
Ivo Adan ◽  
Vidyadhar Kulkarni
Keyword(s):  
Life Insurance ◽  
Poisson Model ◽  
Variable Number ◽  
Insurance Fund ◽  
Insurance Company ◽  
Insurance Risk ◽  
Constant Rate ◽  
Remarkable Result ◽  
New Policies ◽  
The One

In this article we consider an insurance company selling life insurance policies. New policies are sold at random points in time, and each policy stays active for an exponential amount of time with rate μ, during which the policyholder pays premiums continuously at rate r. When the policy expires, the insurance company pays a claim of random size. The aim is to compute the probability of eventual ruin starting with a given number of policies and a given level of insurance fund. We establish the remarkable result that the ruin probability is identical to the one in the standard compound Poisson model where the insurance fund increases at constant rate r and claims occur according to a Poisson process with rate μ.

Sign in / Sign up

Export Citation Format

Share Document