scholarly journals Database Structure for a Multi Stage Stochastic Optimization Based Decision Support System for Asset Liability Management of a Life Insurance Company

2014 ◽  
Author(s):  
Harish Rao ◽  
Dutta Goutam ◽  
Sankarshan Basu
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Stochastic Optimization ◽  
Support System ◽  
Life Insurance ◽  
Insurance Company ◽  
Life Insurance Company ◽  
Liability Management ◽  
Multi Stage ◽  
Database Structure

scholarly journals Development of an Intelligent Decision Support System for Attaining Sustainable Growth within a Life Insurance Company

Mathematics ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 1369
Author(s):  
Mohammad Farhan Khan ◽  
Farnaz Haider ◽  
Ahmed Al-Hmouz ◽  
Mohammad Mursaleen
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Life Insurance ◽  
Support Vector Machine Model ◽  
Sustainable Growth ◽  
Support Vector ◽  
Insurance Company ◽  
Life Insurance Company ◽  
Machine Model

Consumer behaviour is one of the most important and complex areas of research. It acknowledges the buying behaviour of consumer clusters towards any product, such as life insurance policies. Among various factors, the three most well-known determinants on which human conjecture depends for preferring a product are demographic, economic and psychographic factors, which can help in developing an accurate market design and strategy for the sustainable growth of a company. In this paper, the study of customer satisfaction with regard to a life insurance company is presented, which focused on comparing artificial intelligence-based, data-driven approaches to classical market segmentation approaches. In this work, an artificial intelligence-based decision support system was developed which utilises the aforementioned factors for the accurate classification of potential buyers. The novelty of this paper lies in developing supervised machine learning models that have a tendency to accurately identify the cluster of potential buyers with the help of demographic, economic and psychographic factors. By considering a combination of the factors that are related to the demographic, economic and psychographic elements, the proposed support vector machine model and logistic regression model-based decision support systems were able to identify the cluster of potential buyers with collective accuracies of 98.82% and 89.20%, respectively. The substantial accuracy of a support vector machine model would be helpful for a life insurance company which needs a decision support system for targeting potential customers and sustaining its share within the market.

scholarly journals Application note: Crop-loss assessment monitor – A multi-model and multi-stage decision support system

2020 ◽  
Vol 175 ◽  
pp. 105619
Author(s):  
Pramod Aggarwal ◽  
Paresh Shirsath ◽  
Shalika Vyas ◽  
Ponraj Arumugam ◽  
Sheshakumar Goroshi ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Crop Loss ◽  
Loss Assessment ◽  
Multi Stage ◽  
Crop Loss Assessment
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