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Showing 2 results for K-Means Clustering

M. Manteqipour, A.r. Ghafari Hadigheh, A. Safari,
Volume 15, Issue 1 (4-2018)
Abstract

Fair premium pricing is one the main concerns in actuarial science. In this paper, by considering the demand functions, costs of claims and values at risks in homogenous risk groups of cargo insurance policies, we determine the optimum premiums. Higher prices lead to have higher income from policy, meanwhile the number of customers will be reduced. Therefore, optimizing the prices is necessary. In this paper, the estimated demand functions are exponential. Hence, the proposed price optimization problem is nonlinear with a nonlinear constraint. The constrain, leads to have higher prices for bad risks and lower prices for good ones. In addition, the proposed model makes it possible to control the average of the values at Risks. Calculations show that for these datasets, the values of elasticities are lower for good risks. Moreover, increasing the average of values at risks reduces the optimum prices and increases the income. Meanwhile, increasing the average of values at risks for higher than special values, does not increase the income.
 


A. Agha Gholizade Sayyar, M. Motadel, A. Pourebrahimi,
Volume 17, Issue 2 (5-2020)
Abstract

ATMs are one of the most important cash-distribution channels, their inventory management is some of the most important tasks of banks. This paper provides a dynamic and optimized model for managing ATM inventory, based on the time and location of the devices. The number of devices examined is 368 in Tehran, which were surveyed during the quarterly period in 2018, which includes 189657 records. This model with data clustering succeeds in learning the pattern in the data, and then the proposed decision tree can predict the number of costumers for each scenario. By simulating the obtained scenarios, the system costs are determined, and finally, by optimizing the proposed model for each scenario, the average cost of the whole system is reduced by 12%.

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