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Open AccessJournal ArticleDOI

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

TLDR
In this paper, an artificial intelligence-based decision support system was developed which utilized demographic, economic and psychographic factors for the accurate classification of potential buyers, which can help in developing an accurate market design and strategy for the sustainable growth of a company.
Abstract
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.

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Prediction of Consumer Behavior by Experts and Novices

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Indian Life Insurance Industry - The Changing Trends

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Journal ArticleDOI

On One- and Two-Dimensional α–Stancu–Schurer–Kantorovich Operators and Their Approximation Properties

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References
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Journal ArticleDOI

What is a support vector machine

TL;DR: Support vector machines are becoming popular in a wide variety of biological applications, but how do they work and what are their most promising applications in the life sciences?
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An Introduction to Logistic Regression Analysis and Reporting

TL;DR: In this paper, a set of guidelines for what to expect in an article using logistic regression techniques are discussed. But they do not cover the application of logistic methods to a data set in testing a research hypothesis.
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Logistic Regression Diagnostics

TL;DR: In this article, the authors developed diagnostic measures to aid the analyst in detecting such observations and quantifying their effect on various aspects of the maximum likelihood fit of a logistic regression model.
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Household energy use: Applying behavioural economics to understand consumer decision-making and behaviour

TL;DR: In this paper, the key cognitive biases and motivational factors that may explain why energy-related behavior so often fails to align with either the personal values or material interests of consumers are explored.
Journal ArticleDOI

SSVM: A Smooth Support Vector Machine for Classification

TL;DR: Smoothing methods are applied here to generate and solve an unconstrained smooth reformulation of the support vector machine for pattern classification using a completely arbitrary kernel, which converges globally and quadratically.
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