MODELING SHANKER-GAMMA CLAIM RISK AND BAYES PREMIUM CALCULATION
Accurate claims risk modeling is essential in actuarial science for predicting the size or severity of future claims that insurance companies might encounter. The severity of these claims is influenced by the risk profiles of policyholders, necessitating a flexible statistical model to represent...
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| Main Author: | |
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| Format: | Theses |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/83069 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Accurate claims risk modeling is essential in actuarial science for predicting the size
or severity of future claims that insurance companies might encounter. The severity of
these claims is influenced by the risk profiles of policyholders, necessitating a flexible
statistical model to represent these variations accurately. This study introduces the
Shanker-gamma (S-G) type II mixture model to address this requirement. Two types
of mixture distributions are explored: type I, which combines two distributions with
a specified mixing weight, and type II, which employs conditional random variables
with parameters treated as random variables. Additionally, a premium calculation
model is developed based on the S-G loss model, using expectations (pure premium)
and standard deviation (SDPP). These premium values are then compared with the
Bayes premium value, which provides a precise assessment of the policyholder’s risk
profile by incorporating non-constant parameters in the loss model. By utilizing these
advanced statistical methods, financial management in insurance can be significantly
improved, offering a more accurate approach to predicting and managing future
claims. |
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