PURE PREMIUM MODEL IN MARINE CARGO INSURANCE USING GENERALIZED ESTIMATING EQUATION (GEE)
Shipping services can be a solution for those who prefer an easy and practical way of delivering goods over distances that are not easily reachable on their own. One method of goods transportation is through maritime routes, encapsulated in sea freight cargo. Sea freight cargo is the process of ship...
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| Format: | Theses |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/81571 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Shipping services can be a solution for those who prefer an easy and practical way of delivering goods over distances that are not easily reachable on their own. One method of goods transportation is through maritime routes, encapsulated in sea freight cargo. Sea freight cargo is the process of shipping goods using maritime transportation. This research aims to evaluate the correlation between marine cargo insurance and influencing factors, implement the GEE model in the longitudinal data analysis of marine cargo insurance, and determine pure premiums using the analysis results. Additionally, the study involves per-period analysis using the GEE model and formulating strategies for premium determination and per-period premiums based on GEE analysis results. Marine cargo insurance serves as financial protection for cargo owners, safeguarding them from the risks of loss during sea journeys. The best GEE model, Model B with an "Exchangeable" correlation structure, is selected based on the lowest QIC and CIC values and significant estimations for four parameters. The model reveals relationships between variables, indicating a positive correlation between the increase in general selling prices (?????) and average travel time (?????) with the insured value, while the increase in shipping prices (?????) and travel distance (?????) shows a negative correlation. Premiums are calculated by multiplying the output of the best GEE model by the company's rate, resulting in an average pure premium of Rp.190,819. Per-period analysis highlights the flexibility of the GEE model in adapting to the dynamic relationships between variables over time. Recommended premium-setting strategies involve identifying contributing variables, adjusting premiums according to business goals, monitoring per-period patterns, and considering risk factors. Descriptive statistical analysis of per-period premiums provides insights into variations and characteristics over 12 periods, With the highest premium price in Period 8 (Rp.331,386) and the lowest in Period 1 (Rp.104,070). |
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