Optimal Insurance Indemnity and Reinsurance Strategy for Health Insurance

Zhang, Yan; Wu, Yonghong; Yao, Haixiang

doi:10.1155/2021/3931230

Cited by 2 publications

(2 citation statements)

References 53 publications

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“…Te variational method is employed to characterize the optimal solution and obtain the optimal insurance contract. Zhang et al [4] obtained the optimal indemnity design for health insurance via the Lagrange multiplier method and optimal control technology.…”

Section: Introductionmentioning

confidence: 99%

Optimal Insurance Indemnity for the Insured with Low Risk Tolerance

Zhang

2022

Journal of Mathematics

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This study focuses on the optimal insurance for the insured with low risk tolerance from the perspective of rank-dependent utility. Dissimilar to the widely employed assumption that the initial wealth of the insured can sufficiently cover any incurred premium and maximal possible loss, we assume that the policyholder may not possess sufficient initial wealth, thus exhibiting a low risk tolerance. Hence, we establish a corresponding insurance model in which the insured’s initial wealth is designed in such a manner that it only needs to cover the premium. Further, the model considers an introduced risk level that is less than the maximal possible loss. Owing to the lack of the condition of the sufficiency of initial wealth, the original optimization issue could not be converted into a canonical quantile-optimization issue, as usual, and the quantile formulation could not be obtained directly. To overcome this challenge, we apply the calculus of variations method employing a variable upper limit that is related to the maximal risk level. Further, the explicit optimal solution bearing Yaari’s dual criterion is given, demonstrating that the optimal insurance policy with sufficient initial wealth represents a special case. Finally, numerical examples are presented to compare the indemnity with different risk levels.

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Section: Introductionmentioning

confidence: 99%

Optimal Insurance Indemnity for the Insured with Low Risk Tolerance

Zhang

2022

Journal of Mathematics

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“…Lastly, Zhang et al (2021) aimed to determine the insurance indemnity for health insurance using a linear regression algorithm and optimal control techniques. The study focused on developing a mathematical model to estimate the optimal insurance indemnity for a given patient based on their health information and history.…”

Section: Overview Of Noteworthy Studies In the Application Of Machine...mentioning

confidence: 99%

Insurance Premium Calculation Using Machine Learning Methodologies

Mohammad¹

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This research introduces a novel, data-driven optimization methodology for determining insurance premiums and illustrates it using an Iranian health insurance company as a case study. Using the optimal classification machine learning model, the insureds are divided into groups based on their potential to cause disruptions. Next, the indemnity of each group is estimated. Then a linear programming model is used to determine the premium for each group based on their estimated indemnification. Multi-criteria decision-making is utilized to identify the best machine learning algorithm after evaluating the performance of many algorithms using five metrics: accuracy, precision, recall, F1 score, and Matthews correlation coefficient. This study addresses the two common and significant disruptions in the insurance sector: errors in predicting possible losses and a decrease in the insurer's market share. The methodology discussed in this work can be applied to other insurance domains, expanding its practical applications.

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Optimal Insurance Indemnity and Reinsurance Strategy for Health Insurance

Cited by 2 publications

References 53 publications

Optimal Insurance Indemnity for the Insured with Low Risk Tolerance

Optimal Insurance Indemnity for the Insured with Low Risk Tolerance

Insurance Premium Calculation Using Machine Learning Methodologies

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