Insurance Applications Research Articles

Impact of the First-Fourth Waves of the COVID-19 Pandemic on New Applications for Long-term Care Insurance in a Metropolitan Area of Japan.

Impact of the First-Fourth Waves of the COVID-19 Pandemic on New Applications for Long-term Care Insurance in a Metropolitan Area of Japan.

Factors impacting the intention to use digital social insurance application (VSSID) in Phan Thiet city, Binh Thuan province

Factors impacting the intention to use digital social insurance application (VSSID) in Phan Thiet city, Binh Thuan province

Factors impacting the intention to use digital social insurance application (VSSID) in Phan Thiet city, Binh Thuan province

The study aims to determine the factors affecting the intention to use digital social insurance (VssID application) in Phan Thiet City, Binh Thuan Province. The study applies a combination of qualitative and quantitative analyses. The research data were collected from a direct survey of 150 residents in Phan Thiet City. The results have proved that six factors that positively affect the intention to use the VssID application, including performance expectancy, ease of use expectancy, subjective norms, trust, innovation, and health consciousness. Among these factors, the performance expectancy factor has the most influence on the intention to use the VssID application in Phan Thiet City, Binh Thuan Province.

Comparative Analysis Performance of Naïve Bayes and K-NN Using Confusion Matrix and AUC To Predict Insurance Fraud

Based on claim submission data from year 2019 to 2021 can be seen that the percentage of claims in one province is much higher than other provinces. During that period, the percentage of claims in that province reached 22% while the highest percentage in other provinces was only 6%. It is suspected that there has been a claim fraud in the province. The fraud allegedly started when customer submits a policy issuance for the elderly insured with a low sum insured so that the premium is also low. The insured's health condition at that time may not be good but it is not explained in the insurance application letter. To increase the sum insured, the policy is usually added with additional coverage. Fraud claim creates big loss for insurance company since the company has to pay the claim that they should not pay. Insurance company need to have a mechanism to avoid the fraud claim. From this research, it is expected to find the best methodology to be able to predict the potential of insurance claim fraud early when customers apply for policy issuance so that additional checks can be carried out for suspected submissions. The initial data available for this research is 14,778 claim records with attributes are : the date of claim submission, policy effective date, sum assured, type of claim, cause of claim, province and fraud. In order to get the best methodology on the accuracy and performance aspect to fulfill the expectation, two methodologies (Naïve Bayes and K-NN) are compared. Both Naive Bayes and K-NN methods are used with a comparison of training data and testing data is 80:20. Several combinations were performed for each of these methods. By using Confusion Matrix and AUC to measure the accuracy and performance of the two methods, it can be concluded that the best one is Naive Bayes with accuracy is 90% and AUC is 0.761. The attributes used are province, sum assured, additional coverage and the insured is the policy holder.

Small Area Estimation Using a Semiparametric Spatial Model with Application in Insurance

Additional information and borrowing strength from the related sites and other sources will improve estimation in small areas. Generalized linear mixed-effects models (GLMMs) have been frequently used in small area estimation; however, the relationship between the response variable and some covariates may not be linear in many cases. In such cases, using semiparametric modeling, incorporating some nonlinear symmetric/asymmetric functions to the predictor seems more appropriate due to their flexibility. In addition, spatial dependence is observed between areas in many cases. Thus, using the semiparametric spatial models for small areas is of interest. This paper presents semiparametric spatial GLMMs and approximates the nonlinear component using splines to estimate the linear part. We apply our proposal for analyzing insurance data obtained from an Iranian insurance company. Our numerical illustrations will support the use of our proposal in situations where the spatial GLMMs may not be appropriate.

The Lindley Gompertz Model for Estimating the Survival Rates: Properties and Applications in Insurance

The Lindley Gompertz Model for Estimating the Survival Rates: Properties and Applications in Insurance

A simple Bayesian state-space approach to the collective risk models

The collective risk model (CRM) for frequency and severity is an important tool for retail insurance ratemaking, natural disaster forecasting, as well as operational risk in banking regulation. This model, initially designed for cross-sectional data, has recently been adapted to a longitudinal context for both a priori and a posteriori ratemaking, through random effects specifications. However, the random effects are usually assumed to be static due to computational concerns, leading to predictive premiums that omit the seniority of the claims. In this paper, we propose a new CRM model with bivariate dynamic random effects processes. The model is based on Bayesian state-space models. It is associated with a simple predictive mean and closed form expression for the likelihood function, while also allowing for the dependence between the frequency and severity components. A real data application for auto insurance is proposed to show the performance of our method.

Design and implementation of geographic information system with mobile terminal and 5G network

AbstractRemote insurance mainly completes quotation, insurance application and payment through the PC terminal and mobile terminal, and the premium directly enters the insurance account. This article takes remote insurance as an example. First, it analyses the requirements of the remote insurance and insurance system, and then collects data from the mobile terminal. The system geographic information system (GIS) server subsystem and GIS information management subsystem are designed in three aspects. GPS data and video image data are collected and processed in the data acquisition subsystem, forwarded by the GIS server, and sent to the GIS information management subsystem. Geographic information system information management realises online loss determination through basic information query, geographic location identification, scheduling, path planning, real‐time tracking, image information etc. on the mobile terminal, combined with insurance and insurance business. In terms of implementation, the GIS information management platform and Google Maps are used to realise the monitoring, scheduling and path planning of the mobile terminal GIS online platform. And conduct experimental research on the content‐centric 5G structure. The results show that the transmission interference time of the content‐centric network‐based 5G structure is 390 ms, which is much lower than the 800 ms demanded for GSM‐R system. It proves that the content‐centric network‐based 5G structure has good mobile performance; the test of the content‐centric network verifies that the content‐centric network has the advantages of balancing network load and preventing network congestion; meanwhile, the test of the proposed The functions and performance of the GIS system proposed in this paper are tested. The results meet the user requirements.

Prognostic importance of NT-proBNP following high-risk myocardial infarction in the PARADISE-MI Trial

Abstract Background/Introduction N-terminal pro-B-type natriuretic peptide (NT-proBNP) is a marker of ventricular wall stress and a potent predictor of death and heart failure (HF) across multiple populations (from healthy insurance applicants to various disease entities). Purpose To evaluate the prognostic importance of NT-proBNP in patients with acute myocardial infarction (AMI) complicated by left ventricular systolic dysfunction, pulmonary congestion, or both and ≥1 of 8 predefined risk-augmenting factors (age ≥70 years, diabetes, previous MI, eGFR <60 ml/min/1.73 m2, atrial fibrillation, LVEF <30%, Killip class III/IV, or ST elevation MI without reperfusion) enrolled in PARADISE-MI. Methods Patients were randomized to sacubitril/valsartan 200mg or ramipril 5mg twice daily within 0.5 to 7 days of presenting with an AMI. Patients with prior HF were excluded. NT-proBNP and high-sensitivity troponin T (hsTnT) were collected at randomization in a prespecified sub-study of 1129 patients. The primary endpoint of PARADISE-MI was a time-to-first composite of cardiovascular (CV) death or incident HF (hospitalization or outpatient symptomatic HF); secondary endpoints included all-cause death and the composite of fatal or non-fatal MI or stroke. Results Median NT-proBNP was 1757 pg/ml [interquartile range, 896–3462 pg/ml] at randomization (4.0±1.8 days after presentation with the index MI). Patients with higher NT-proBNP levels at baseline were older, more commonly women and more frequently had hypertension, atrial fibrillation, renal dysfunction, and pulmonary congestion at randomization (all p<0.001). NT-proBNP concentrations were only weakly correlated with levels of hsTnT at randomization (r=0.38, p<0.001). NT-proBNP at baseline was strongly associated with the primary composite endpoint (adjusted HR 1.45 per doubling NT-proBNP; 95% CI, 1.23–1.70), independent of clinical variables as well as hsTnT (Figure). NT-proBNP was also independently associated with all-cause death (aHR 1.74; 95% CI, 1.38–2.21) and fatal or non-fatal MI or stroke (aHR 1.24; 95% CI, 1.05–1.45). The relative effect of sacubitril/valsartan versus ramipril on the primary composite endpoint was not statistically different across the spectrum of NT-proBNP (p-interaction = 0.46). Conclusions When assessed within the first week of a high risk AMI NT-proBNP is not only associated with incident HF and death but also with atherosclerotic events and provides prognostic information that is independent of hsTnT in this post AMI population. Funding Acknowledgement Type of funding sources: None.

Investigating factors for using insurance apps by customers: a study of a developing country

With the fast development of insurtech, many researchers are doing research in understanding customer behavior regarding the adoption of insurance applications. However, there is a lack of research on the subject in developing countries such as Vietnam. The primary purpose of this article is to develop a predictive model for the intention to use the insurance application of individual customers in Vietnam. In this research, the quantitative research method is conducted based on survey data collected in Ho Chi Minh City. Data are tested using various methods, such as Cronbach’s Alpha, EFA, Pearson correlation test, and multiple linear regression. From the analysis results, some factors positively influencing the intention to use insurance applications are identified, including Trust, Ease of Use, and Usefulness. However, the impact of Risk on Usefulness is not confirmed based on the sample data. From the analysis results, some managerial implications are made to improve the intention to use insurance applications of customers in Vietnam.

The Reality of Takaful Insurance in Algeria: A Study of the Experience of the Salama Insurance Company in Algeria

The aim of this study is to present the reality of takaful insurance in Algeria, as it was addressed through Salama Insurance Company, which is considered the only one in Algeria so far. We have concluded that the application of Takaful insurance in Algeria faces many obstacles, the basis of which is the legal challenge that constitutes the biggest obstacle to the development of this industry, aswell as the traditional legal environment that often does not comply with Islamic Sharia regulations, as well as the weak efforts of the state in regulating the Takaful insurance industry, The failure to enact laws in line with its requirements, the weak technical and legal training of the human factor, the weak culture and insurance awareness of the Algerian public, all of which stand in the way of the development and growth of Takaful insurance

Automated GDPR Contract Compliance Verification Using Knowledge Graphs

In the past few years, the main research efforts regarding General Data Protection Regulation (GDPR)-compliant data sharing have been focused primarily on informed consent (one of the six GDPR lawful bases for data processing). In cases such as Business-to-Business (B2B) and Business-to-Consumer (B2C) data sharing, when consent might not be enough, many small and medium enterprises (SMEs) still depend on contracts—a GDPR basis that is often overlooked due to its complexity. The contract’s lifecycle comprises many stages (e.g., drafting, negotiation, and signing) that must be executed in compliance with GDPR. Despite the active research efforts on digital contracts, contract-based GDPR compliance and challenges such as contract interoperability have not been sufficiently elaborated on yet. Since knowledge graphs and ontologies provide interoperability and support knowledge discovery, we propose and develop a knowledge graph-based tool for GDPR contract compliance verification (CCV). It binds GDPR’s legal basis to data sharing contracts. In addition, we conducted a performance evaluation in terms of execution time and test cases to validate CCV’s correctness in determining the overhead and applicability of the proposed tool in smart city and insurance application scenarios. The evaluation results and the correctness of the CCV tool demonstrate the tool’s practicability for deployment in the real world with minimum overhead.

Sine F-Loss Family of Distributions: Properties and Insurance Applications

Sine F-Loss Family of Distributions: Properties and Insurance Applications

Analisis Efektivitas Pendaftaran Calon Nasabah Baru pada PT Asuransi Jiwa Syariah Bumiputera Cabang Medan

Bumiputera Sharia Life Insurance is a privately owned company engaged in Life Insurance. The existence of insurance today is not a foreign thing for the world community. See the development of this insurance. make the existence of information systems difficult to ignore. Prospective customer registration system at PT. Bumiputera Syariah Life Insurance uses two systems, namely a manual registration system and an online registration system. While in the manual system, namely by writing data on prospective customers on the Life Insurance Application Letter form. This is considered less effective and efficient in supporting work productivity in the company. In this problem, the idea emerged in the form of a proposal to provide information and input in the process of registering new prospective customers who can use online and manual systems that can be useful for prospective customers to register for life insurance applications without having to come directly to the company. By implementing an online-based system in the prospective customer registration process, it will certainly make it easier for prospective customers to register. In this development, of course, a model is needed that can help analyze the online system. The existence of a prospective customer registration system through an online-based system is expected to make it easier for prospective customers who want to register themselves in insurance without having to go directly to the insurance company. also makes it easier for administrators to manage prospective customer data. And the registration process is made easier, faster and more accurate.
 Keywords: Information Analysis, Registration, Bumiputera Sharia Life Insurance, Manual, Online

Optimization Strategy Analysis of Intelligent Product Service System Based on Computer Simulation Technology

In order to solve the needs of a large number of users and the efficiency of user service requirements, an optimization strategy analysis method of intelligent product service system based on computer simulation technology is proposed. Aiming at the two challenges of massive requests and efficient service in the network intelligent service system, the characteristics of the network intelligent service system are analyzed, and the network intelligent service system is formally modeled as an agent system. The performance modeling, performance evaluation, and performance optimization methods of the network intelligent service system are proposed. In order to solve this massive demand, the performance modeling of the network intelligent service system is carried out based on queuing theory. This paper proposes a task assignment algorithm for the network intelligent service system. Through the application of these results, the processing efficiency of the entire medical insurance application system has been greatly improved, and the average completion time of all businesses has been shortened by 59%, providing new ideas for optimizing service methods and improving service quality.

Adoption of incentive-based insurance applications: the perspective of psychological ownership

Incentive-based health insurance applications have been implemented to trigger lower insurance premiums when the individual follows healthy living habits. It has been recognised that this benefit notwithstanding, consumers’ perceptions related to such applications in the life-insurance field and health tracking are not necessarily positive. Recent research has recognised that psychological ownership, a mental state wherein one feels a technology or application to be his or her own, plays a crucial role in individuals’ willingness to adopt and use new technologies. It has been suggested that many digital applications and platforms possess unique empowering and co-creative features that offer special potential to facilitate the emergence of psychological ownership and satisfy the underlying needs. The aim of our study, proceeding from these premises, was to identify and thematize hurdles to take-up of incentive-based health insurance applications with regard to meeting needs that drive development of psychological ownership. We achieved this by conducting a thematic analysis of perceptions of consumers who were not willing to adopt a specific application of the relevant type. The resulting framework, which recognises 14 thematized hurdles in all, holds important implications for scientific and managerial use both.

Structural models for fog computing based internet of things architectures with insurance and risk management applications

Cybersecurity risk modeling and pricing are becoming a spotlight in actuarial science and operational research. This paper pertains to the analysis of the cybersecurity risks involved in the fog computing technology which has been intensively deployed in assorted Internet of Things (IoT) applications. To this end, a class of structural models are established to study the inherent cyber risk propagation process. Under the smart home applications, we manage to compute the compromise probabilities of individual nodes explicitly. Applications of the proposed structural models in the context of cyber insurance pricing are thoroughly explored. Finally, we propose an interval method for estimating the compromise probabilities of fog network’s elements, which can be used to efficiently identify weak nodes for cybersecurity risk management.

Bayesian inference for the parameters of mortality rate in the models of dependent lives with application in life insurance

In this paper, the Bayesian inference of the model of dependent lives is considered. We use the bivariate Gompertz (BGP) distribution. This new bivariate survival model and its extension have recently been proposed by Shoaee and Khorram. This model is more flexible and can be applied in the actuarial science of life insurance, and reliability theory in competing risks and shock models. As we know, the maximum likelihood estimates do not always exist and therefore cannot always be calculated. So, the Bayesian estimations are considered using the squared error loss function and a priori distributions that create a dependency between the hyper-parameters. But given the assumptions, one can see that explicit expressions cannot be obtained for Bayesian estimations. The importance sampling method is proposed to calculate the Bayes estimations and also to create the corresponding HPD credible intervals of the unknown parameters. The Monte Carlo simulation studies and analysis of a real data set are also performed to evaluate the proposed method for estimating parameters. Finally, a generalization of the dependent lives models is presented and the Bayesian estimate for this new bivariate family distribution is introduced and a comparison is made between the models of this new family.

The impact of Covid-19 on older workers' employment and Social Security spillovers.

The COVID-19 pandemic represents a major threat to health and economic well-being in the USA, especially for older and disabled workers, and may spill over onto Social Security. We use individual-level from the Current Population Survey, state-level monthly Social Security administrative data on disability benefit applications, and national-level monthly data on Social Security retirement benefit applications to assess the impact of the pandemic on older adults’ employment and benefit claiming. State-level monthly Google Trends data are used as a leading indicator of future claiming in the population. We find that employment for older workers dropped substantially more than would have been predicted prior to the pandemic: employment for 50–61-year-olds was 5.7 pp (8.3 percent) lower, while employment for 62–70-year-olds was 3.9 pp (10.7 percent) lower. We find declines in labor force exit due to disability (4–5 percent), applications for disability insurance (15 percent), the average age of disability program applicants, and Google searches for disability (7 percent). We contrast with prior periods of economic downturn and explore potential mechanisms, finding evidence for both supply- and demand-side explanations.Supplementary informationThe online version contains supplementary material available at 10.1007/s00148-022-00915-z.

Association of Liver Function Tests with Mortality in an Insurance Applicant Population.

-Determine the relationship between liver function test (LFT) results (GGT, alkaline phosphatase, AST, ALT and albumin) and all-cause mortality in life insurance applicants. -By use of the Social Security Master Death File, mortality was examined in 15,272,955 insurance applicants for whom blood samples were submitted to the Clinical Reference Laboratory. There were 268,593 deaths observed in this study population, after an average follow-up time of 10.9 years. Results were stratified by sex and by age less/greater than 60, creating 4 groups. Liver function test values were grouped using percentiles of their distribution within these age/ sex groups - so as to update the results generated in prior publications. Additional models were fit using different exclusions and percentile groups within single year age groups. Also, LFTs were treated as continuous variables and included in Cox models with age and smoking status. -Using the risk of the middle 50% of the population by distribution as a reference, relative mortality observed for GGT and alkaline phosphatase was linear with a steep slope from very low to high values. AST showed a J-shaped association with mortality. ALT showed a low-magnitude inverse correlation with mortality. Albumin demonstrated a higher-magnitude inverse correlation with mortality, especially at values below the median. The overall risk associated with LFTs was durable over at least 10 years of follow-up. -Liver function tests show a strong and durable correlation to mortality in a large group of insurance applicants. The durability over time suggests that even older values of LFTs found in medical records could be of use in mortality risk prediction.