Insurance Premium Optimization: Perspective of Insurance Seeker and Insurance Provider

The paper proposes a utilitarian and newfangled design of life insurance premium payment. Insurance companies provide insurance to the policy holders and in turn the policy holders have to pay insurance premium periodically. In this paper, we study the important factors to be considered at the time of selecting the term of premium payment of insurance. The intention is to extend this study to a method which can machinate and assist users in deciding the term and amountof premium payment. Our aim here is twin-fold; one, to evoke a novel way to policy holder to decide the least premiums to be paid and second, to devise a manner for insurance provider to collect maximum premiums. With this twofold aim, we propose a hybrid soft computing optimization model using Neuro-Fuzzy approach and Particle Swarm Optimization.

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A Neuro-Fuzzy Expert System Trained by Particle Swarm Optimization for Stock Price Prediction

Cross-Disciplinary Applications of Artificial Intelligence and Pattern Recognition - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-61350-429-1.ch031 ◽

2012 ◽

pp. 633-650

Author(s):

Mohammad Hossein Fazel Zarandi ◽

Milad Avazbeigi ◽

Meysam Alizadeh

Keyword(s):

Particle Swarm Optimization ◽

Stock Market ◽

Soft Computing ◽

Stock Price ◽

Particle Swarm ◽

Swarm Optimization ◽

Stock Price Prediction ◽

Price Prediction ◽

Neuro Fuzzy ◽

Input Variables

In today’s competitive markets, prediction of financial variables has become a critical issue. Especially in stock market analysis where a wrong prediction may result in a big loss in terms of time and money, having a robust prediction is a crucial issue. To model the chaotic, noisy, and evolving behavior of stock market data, new powerful methods should be developed. Soft Computing methods have shown a great confidence in such environments where there are many uncertain factors. Also it has been observed through many experiments that the hybridization of different soft computing techniques such as fuzzy logic, neural networks, and meta-heuristics usually results in better results than simply using one method. This chapter presents an adaptive neuro-fuzzy inference system (ANFIS), trained by the particle swarm optimization (PSO) algorithm for stock price prediction. Instead of previous works that have emphasized on gradient base or least square (LS) methods for training the neural network, four different strategies of PSO are implemented: gbest, lbest-a, lbest-b, and Euclidean. In the proposed fuzzy rule based system some technical and fundamental indexes are applied as input variables. In order to generate membership functions (MFs), a robust noise rejection clustering algorithm is developed. The proposed neuro-fuzzy model is applied for an automotive part-making manufactory in an Asia stock market. The results show the superiority of the proposed model in comparison with the available models in terms of error minimization, robustness, and flexibility.

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PERHITUNGAN PREMI ASURANSI JOINT LIFE DENGAN MODEL VASICEK DAN CIR

E-Jurnal Matematika ◽

10.24843/mtk.2019.v08.i03.p260 ◽

2019 ◽

Vol 8 (3) ◽

pp. 246

Author(s):

I MADE WAHYU WIGUNA ◽

KETUT JAYANEGARA ◽

I NYOMAN WIDANA

Keyword(s):

Stochastic Process ◽

Interest Rates ◽

Life Insurance ◽

Insurance Premium ◽

Insurance Contract ◽

Insurance Company ◽

Vasicek Model ◽

Cir Model ◽

Premium Payment ◽

Premium Price

Premium is a sum of money that must be paid by insurance participants to insurance company, based on insurance contract. Premium payment are affected by interest rates. The interest rates change according to stochastic process. The purpose of this work is to calculate the price of joint life insurance premiums with Vasicek and CIR models. The price of a joint life insurance premium with Vasicek and CIR models, at the age of the insured 35 and 30 years has increased until the last year of the contract. The price of a joint life insurance premium with Vasicek model is more expensive than the premium price using CIR model.

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Performance comparisons of particle swarm optimization, echo state neural network and genetic algorithm for vegetation segmentation

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i1.1.9286 ◽

2017 ◽

Vol 7 (1.1) ◽

pp. 184

Author(s):

Rincy Merlin Mathew ◽

S. Purushothaman ◽

P. Rajeswari

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Particle Swarm Optimization ◽

Soft Computing ◽

Particle Swarm ◽

Band 3 ◽

Landsat 8 ◽

Swarm Optimization ◽

Landsat 7 ◽

Performance Comparisons

This article presents the implementation of vegetation segmentation by using soft computing methods: particle swarm optimization (PSO), echostate neural network(ESNN) and genetic algorithm (GA). Multispectral image with the required band from Landsat 8 (5, 4, 3) and Landsat 7 (4, 3, 2) are used. In this paper, images from ERDAS format acquired by Landsat 7 ‘Paris.lan’ (band 4, band 3, Band 2) and image acquired from Landsat 8 (band5, band 4, band 3) are used. The soft computing algorithms are used to segment the plane-1(Near infra-red spectra) and plane 2(RED spectra). The monochrome of the two segmented images is compared to present performance comparisons of the implemented algorithms.

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MENENTUKAN FORMULA CADANGAN PREMI ASURANSI JIWA LAST SURVIVOR MENGGUNAKAN METODE NEW JERSEY

E-Jurnal Matematika ◽

10.24843/mtk.2019.v08.i04.p263 ◽

2019 ◽

Vol 8 (4) ◽

pp. 264

Author(s):

I GUSTI AGUNG GEDE DWIPAYANA ◽

I NYOMAN WIDANA ◽

KARTIKA SARI

Keyword(s):

New Jersey ◽

Life Insurance ◽

Insurance Premium ◽

First Year ◽

Premium Payment ◽

Second Year ◽

Annual Premium

Last survivor life insurance is a type of life insurance for two or more people, with premium payment up to the last death of the insured and at that time also provide the benefit from the insurer. The purpose of this research was to determine the formula for last survivor life insurance premium reserve using New Jersey method. To calculate the reserve: first we determine the benefit, and then the annuity and finnaly the annual premium. The premium reserve value in the New Jersey method on first year is zero. The premium reserve in the New Jersey method starts in the second year, for years, with where n represents the term of the insurance participant’s contract.

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