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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my-usim-ddms-127112024-05-29T04:42:29Z Bayesian Statistical Modeling Of Claim Frequency For Auto Insurance Muhammad 'Afif Bin Amir Husin 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. Universiti Sains Islam Malaysia 2018-04 Thesis en https://oarep.usim.edu.my/handle/123456789/12711 https://oarep.usim.edu.my/bitstreams/0d577f21-6485-4915-82d4-1154fcfe95b6/download 8a4605be74aa9ea9d79846c1fba20a33 Automobile insurance Computer-assisted instruction. Data processing. WAIC |
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English |
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Automobile insurance Computer-assisted instruction. Data processing. WAIC |
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Automobile insurance Computer-assisted instruction. Data processing. WAIC Muhammad 'Afif Bin Amir Husin Bayesian Statistical Modeling Of Claim Frequency For Auto Insurance |
| description |
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. |
| format |
Thesis |
| author |
Muhammad 'Afif Bin Amir Husin |
| author_facet |
Muhammad 'Afif Bin Amir Husin |
| author_sort |
Muhammad 'Afif Bin Amir Husin |
| title |
Bayesian Statistical Modeling Of Claim Frequency For Auto Insurance |
| title_short |
Bayesian Statistical Modeling Of Claim Frequency For Auto Insurance |
| title_full |
Bayesian Statistical Modeling Of Claim Frequency For Auto Insurance |
| title_fullStr |
Bayesian Statistical Modeling Of Claim Frequency For Auto Insurance |
| title_full_unstemmed |
Bayesian Statistical Modeling Of Claim Frequency For Auto Insurance |
| title_sort |
bayesian statistical modeling of claim frequency for auto insurance |
| granting_institution |
Universiti Sains Islam Malaysia |
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1812444693473001472 |