In this paper, the Automated Actuarial Loss Reserving Model is developed and extended using machine learning. The traditional actuarial reserving techniques are no longer compatible with the increase in technological ...In this paper, the Automated Actuarial Loss Reserving Model is developed and extended using machine learning. The traditional actuarial reserving techniques are no longer compatible with the increase in technological advancement currently at hand. As a result, the development of the alternative Artificial Intelligence Based Automated Actuarial Loss Reserving Methodology which captures diverse risk profiles for various policyholders through augmenting the Micro Finance services, Auto Insurance Services and Both Services lines of business on the same platform through the computation of the Comprehensive Automated Actuarial Loss Reserves (CAALR) has been implemented in this paper. The introduction of the four further types of actuarial loss reserves to those existing in the actuarial literature seems to significantly reduce lapse rates, reduce the reinsurance costs as well as expenses and outgo. As a matter of consequence, this helps to bring together a combination of new and existing policyholders in the insurance company. The frequency severity models have been extended in this paper using ten machine learning algorithms which ultimately leads to the derivation of the proposed machine learning-based actuarial loss reserving model which remarkably performed well when compared to the traditional chain ladder actuarial reserving method using simulated data.展开更多
The issue of burst losses imposes a constraint on the development of Optical Burst Switching （OBS） networks. Heavy burst losses strongly affect the Quality of Service （QoS） intended by end users. This article pres...The issue of burst losses imposes a constraint on the development of Optical Burst Switching （OBS） networks. Heavy burst losses strongly affect the Quality of Service （QoS） intended by end users. This article presents a QoS aware Routing and Wavelength Allocation （RWA） technique for burst switching in OBS networks. The RWA problem is modeled as a bi-objective Integer Linear Programming （ILP） problem, where objective functions are based on minimizing the number of wavelengths used and the number of hops traversed to fulfill the burst transmission requests for a given set of node pairs. The ILP model is solved using a novel approach based on a Differential Evolution （DE） algorithm. Analytical results show that the DE algorithm provides a better performance compared to shortest path routing, which is a widely accepted routing strategy for OBS networks.展开更多
文摘In this paper, the Automated Actuarial Loss Reserving Model is developed and extended using machine learning. The traditional actuarial reserving techniques are no longer compatible with the increase in technological advancement currently at hand. As a result, the development of the alternative Artificial Intelligence Based Automated Actuarial Loss Reserving Methodology which captures diverse risk profiles for various policyholders through augmenting the Micro Finance services, Auto Insurance Services and Both Services lines of business on the same platform through the computation of the Comprehensive Automated Actuarial Loss Reserves (CAALR) has been implemented in this paper. The introduction of the four further types of actuarial loss reserves to those existing in the actuarial literature seems to significantly reduce lapse rates, reduce the reinsurance costs as well as expenses and outgo. As a matter of consequence, this helps to bring together a combination of new and existing policyholders in the insurance company. The frequency severity models have been extended in this paper using ten machine learning algorithms which ultimately leads to the derivation of the proposed machine learning-based actuarial loss reserving model which remarkably performed well when compared to the traditional chain ladder actuarial reserving method using simulated data.
文摘The issue of burst losses imposes a constraint on the development of Optical Burst Switching （OBS） networks. Heavy burst losses strongly affect the Quality of Service （QoS） intended by end users. This article presents a QoS aware Routing and Wavelength Allocation （RWA） technique for burst switching in OBS networks. The RWA problem is modeled as a bi-objective Integer Linear Programming （ILP） problem, where objective functions are based on minimizing the number of wavelengths used and the number of hops traversed to fulfill the burst transmission requests for a given set of node pairs. The ILP model is solved using a novel approach based on a Differential Evolution （DE） algorithm. Analytical results show that the DE algorithm provides a better performance compared to shortest path routing, which is a widely accepted routing strategy for OBS networks.
