Bayesian Statistical Modeling Of Claim Frequency For Auto Insurance
Auto insurance has become a necessity for Malaysian, making it important to model the claims data so that premiums can be derived with fair and equitable price. This study aims to investigate the best model to fit the data for claims frequency of Malaysia vehicle insurance which composed of 1.21...
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| 格式: | Thesis |
| 語言: | English |
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| 總結: | Auto insurance has become a necessity for Malaysian, making it important to model the
claims data so that premiums can be derived with fair and equitable price. This study
aims to investigate the best model to fit the data for claims frequency of Malaysia
vehicle insurance which composed of 1.21 million policies. These policies comprised
of three types of coverages namely Own Damage (OD), Third Party Property Damage
(TPPD) and Third Party Bodily Injury (TPBI). First, the frequency data is fit to
Bayesian regression models using Poisson, Negative Binomial and Generalized Poisson
distribution. After that, all three models are compared with their respective zero-inflated
models to analyze the effectiveness of zero-inflated model in handling auto insurance
claims data. For the purpose of measurement of good fit, two criteria have been chosen
which are Deviance Information Criteria (DIC) and Watanabe-Akaike Information
Criteria (WAIC). This research found that Generalized Poisson model outperformed
other models evaluated using DIC while Zero-Inflated Negative Binomial have been
superior to other models with respect to WAIC. |
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