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All Journal Operations Research: International Conference Series International Journal of Quantitative Research and Modeling
Adib, Andhita Zahira
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Environmental Science Mathematics Physics

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Aggregate Loss Models to Calculate Risk Measures Rahmawati, Septi; Adib, Andhita Zahira; Rusyn, Volodymyr
Operations Research: International Conference Series Vol. 5 No. 2 (2024): Operations Research International Conference Series (ORICS), June 2024
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v5i2.314

Abstract

The concept of aggregate loss models pertains to a stochastic variable representing the total sum of all losses encountered within a set of insurance policies. In the non-life insurance sector, it is employed to assess the potential losses that an insurance company may face when claims made by policyholders exceed the allocated claim reserves. The purpose of studying aggregate loss models is to ascertain risk measures such as standard deviation of premium principles, value at risk (VaR), and conditional tail expectation (CTE). These steps aid insurance companies in the management and quantification of risks associated with aggregate losses. The standard deviation of premium principles is calculated analytically by substituting expected values and variances, while VaR is estimated using the Monte Carlo method to determine quantile values and confidence intervals. CTE is evaluated by computing the average losses that surpass the VaR threshold. These distributions and parameters require the Pareto distribution, which characterizes claim sizes, and the Poisson or Negative Binomial distribution, which factors in the number of claims. It is crucial to carefully consider the selection of the appropriate distribution, as it plays a significant role in determining the accuracy and reliability of the model. Furthermore, other influencing factors, such as loading factors and confidence intervals, should also be taken into account. These factors have the potential to significantly impact the quantification of risk arising from the model.
Analysis of Pet Owners' Willingness to Pay for Pet Insurance Premiums in DKI Jakarta Using Logistic Regression Model Adib, Andhita Zahira; Riaman, Riaman; Subartini, Betty
International Journal of Quantitative Research and Modeling Vol. 5 No. 2 (2024)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v5i2.578

Abstract

Pets provide many benefits to their owners, both physically and mentally. Pet lovers are increasingly aware of the importance of proper health and care for their beloved animals. This has led pet enthusiasts to consider pet insurance. In participating in insurance, there are factors that influence the willingness of pet owners to pay premiums. The objective of this research is to determine the premium for pet insurance and analyze the factors influencing the Willingness To Pay (WTP) of pet owners. This study utilizes choice modeling format by conducting surveys to identify the factors influencing the purchase of pet insurance. Subsequently, binary logistic regression model analysis using the Maximum Likelihood Estimation (MLE) method and the Newton-Raphson Iteration approach is employed to analyze the factors influencing the magnitude of WTP. The research results show that the average willingness to pay for pet insurance premiums is IDR128,574.76 per year. Factors influencing the decision of pet owners include the number of family dependents and awareness of the importance of participating in pet insurance. The likelihood of cat owners being willing to pay pet insurance premiums is 0.8691 or 86.91%.
Co-Authors Betty Subartini RAHMAWATI, SEPTI Riaman Riaman, Riaman Rusyn, Volodymyr