Dynamic online prediction model and its application to automobile claim frequency data
Prediction modelling of claim frequency is an important task for pricing and risk management in non-life insurance and needed to be updated frequently with the changes in the insured population, regulatory legislation and technology. Existing methods are either done in an ad hoc fashion, such as par...
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Main Authors | , , |
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Format | Journal Article |
Language | English |
Published |
08.01.2023
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Subjects | |
Online Access | Get full text |
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Summary: | Prediction modelling of claim frequency is an important task for pricing and
risk management in non-life insurance and needed to be updated frequently with
the changes in the insured population, regulatory legislation and technology.
Existing methods are either done in an ad hoc fashion, such as parametric model
calibration, or less so for the purpose of prediction. In this paper, we
develop a Dynamic Poisson state space (DPSS) model which can continuously
update the parameters whenever new claim information becomes available. DPSS
model allows for both time-varying and time-invariant coefficients. To account
for smoothness trends of time-varying coefficients over time, smoothing splines
are used to model time-varying coefficients. The smoothing parameters are
objectively chosen by maximum likelihood. The model is updated using batch data
accumulated at pre-specified time intervals, which allows for a better
approximation of the underlying Poisson density function. The proposed method
can be also extended to the distributional assumption of zero-inflated Poisson
and negative binomial. In the simulation, we show that the new model has
significantly higher prediction accuracy compared to existing methods. We apply
this methodology to a real-world automobile insurance claim data set in China
over a period of six years and demonstrate its superiority by comparing it with
the results of competing models from the literature. |
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DOI: | 10.48550/arxiv.2301.03005 |