Discovering dynamic adverse behavior of policyholders in the life insurance industry

Islam, Rafiqul; Liu, Shaowu; Biddle, Rhys; Razzak, Muhammad Imran; Wang, Xianzhi; Tilocca, Peter; Xu, Guandong

doi:10.1016/j.techfore.2020.120486

Cited by 27 publications

(14 citation statements)

References 54 publications

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“…While the study needs more data to make better explicit predictions, our model could help to forecast Confirmed COVID-19 cases if the spread of the virus does not change dramatically (means beyond explanation). Therefore, implementation of advanced machine learning, deep learning algorithms and visualisation techniques could help to forecast and visualise confirmed COVID-19 cases accurately [15][16][17][18]. However, to the best of our knowledge, the proposed model is highly effective at the time of writing this paper.…”

Section: Discussion Of Forecasting Resultsmentioning

confidence: 99%

Analyzing the Impact of Demographic Variables on Spreading and Forecasting COVID-19

Sharif

Islam

Hasan

et al. 2021

J Healthc Inform Res

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The aim of this study is to analyse the coronavirus disease 2019 (COVID-19) outbreak in Bangladesh. This study investigates the impact of demographic variables on the spread of COVID-19 as well as tries to forecast the COVID-19 infected numbers. First of all, this study uses Fisher's Exact test to investigate the association between the infected groups of COVID-19 and demographical variables. Second, it exploits the ANOVA test to examine significant difference in the mean infected number of COVID-19 cases across the population density, literacy rate, and regions/divisions in Bangladesh. Third, this research predicts the number of infected cases in the epidemic peak region of Bangladesh for the year 2021. As a result, from the Fisher's Exact test, we find a very strong significant association between the population density groups and infected groups of COVID-19. And, from the ANOVA test, we observe a significant difference in the mean infected number of COVID-19 cases across the five different population density groups. Besides, the prediction model shows that the cumulative number of infected cases would be raised to around 500,000 in the most densely region of Bangladesh, Dhaka division.

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Section: Discussion Of Forecasting Resultsmentioning

confidence: 99%

Analyzing the Impact of Demographic Variables on Spreading and Forecasting COVID-19

Sharif

Islam

Hasan

et al. 2021

J Healthc Inform Res

Self Cite

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“…• Adverse behavior identification: Adverse behavior identification, the process of understanding and monitoring with the help of interactive visualization, is very important for business authority to solve real-world problem. Many researchers have applied pattern mining techniques in diverse sectors and achieved the expected outcome [49]. However, with the advanced development of DVA, exploring and visualizing adverse behavior will give more adequacy, especially in data analysis.…”

Section: Future Directionsmentioning

confidence: 99%

Deep Visual Analytics (DVA): Applications, Challenges and Future Directions

Islam¹,

Akter²,

Ratan³

et al. 2021

HCIS

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Visual interactive system (VIS) has been received significant attention for solving various complex problems. However, designing and implementing a novel VIS with the large scale of data is a challenging task. While existing studies have applied various visual analytics (VA) to analyze and visualize insightful information, deep visual analytics (DVA) have considered as a promising technique to provide input evidences and explain system results. In this study, we present several deep learning (DL) techniques for analyzing data with visualization, which summarizes the state-of-the-art review on (i) big data analysis, (ii) cognitive and perception science, (iii) customer behavior analysis, (iv) natural language processing, (v) recommended system, (vi) healthcare analysis, (vii) fintech ecosystem, and (viii) tourism management. We present open research challenges for emerging DVA in the visualization community. We also highlight some key themes from the existing literature that may help to explore for future study. Thus, our goal is to help readers and researchers in DL and VA to understand key aspects in designing VIS for analysing data.

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“…The authors argue that households increase their life insurance costs by 18.5% during the year corresponding to the zodiac sign of the year in which these persons were born, and in other years such an increase does not occur. Although life expectancy in the world has increased from 80.3 to 83.9 years over the last 20 years (2000 to 2020) [32], which could be an additional stimulus to purchase life insurance policies, there are still exist various barriers for the growth of this market, in particular, related to education, retirement age, single households, age, marriage, lack of dependent children [33], and self-discipline [34,35]. However, the literature does not deny that with climate change, the risks of death from natural disasters also increase.…”

Section: The Literature Analysismentioning

confidence: 99%

The influence of climate change on the life insurance in the EU: A panel data approach

et al. 2021

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The financial sector, as one of the most sensitive economic sectors, is alert to all trends and changes in the environment. The aim of the article is to study the impact of climate change on the life insurance market using panel data from 28 countries of the European Union (EU) for the last 9 years. This study is based on a panel model, where the amount of premiums under life insurance contracts is defined as a function of the fundamental factor of climate change - greenhouse gas emissions. According to empirical findings, an increase in greenhouse gas emissions per thousand tons leads to an increase in the amount of life insurance premiums by 0.1786 million euros. It has also been found that an increase in greenhouse gas emissions per thousand tons leads to an increase in deaths in the European Union by 1.0442 people, and these consequences are statistically significant. In general, our results suggest that the life insurance market as well as the non-life insurance market is dependent on climate change. The empirical results of this study provide valuable insight into how greenhouse gas emissions affect mortality in the European Union.

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Discovering dynamic adverse behavior of policyholders in the life insurance industry

Cited by 27 publications

References 54 publications

Analyzing the Impact of Demographic Variables on Spreading and Forecasting COVID-19

Analyzing the Impact of Demographic Variables on Spreading and Forecasting COVID-19

Deep Visual Analytics (DVA): Applications, Challenges and Future Directions

The influence of climate change on the life insurance in the EU: A panel data approach

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