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Добірка наукової літератури з теми "Insurance analytics"

Автор: Grafiati

Опубліковано: 11 березня 2023

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Статті в журналах з теми "Insurance analytics"

1

Embrechts, P. "Insurance Analytics." British Actuarial Journal 8, no. 4 (October 1, 2002): 639–41. http://dx.doi.org/10.1017/s1357321700003858.

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2

Frees, Edward W. "Analytics of Insurance Markets." Annual Review of Financial Economics 7, no. 1 (December 7, 2015): 253–77. http://dx.doi.org/10.1146/annurev-financial-111914-041815.

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3

Huizinga, Tylor, Anteneh Ayanso, Miranda Smoor, and Ted Wronski. "Exploring Insurance and Natural Disaster Tweets Using Text Analytics." International Journal of Business Analytics 4, no. 1 (January 2017): 1–17. http://dx.doi.org/10.4018/ijban.2017010101.

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This study explores twitter data about insurance and natural disasters to gain business insights using text analytics. The program R was used to obtain tweets that included the word ‘insurance' in combination with other natural disaster words (e.g., snow, ice, flood, etc.). Tweets related to six top Canadian insurance companies as well as the top five insurance companies from the rest of the world, including the new entrant Google Insurance, was collected for this study. A total of 11,495 natural disaster tweets and 19,318 insurance company tweets were analyzed using association rule mining. The authors' analysis identified several strong rules that have implications for insurance products and services. These findings show the potential text mining applications offer for insurance companies in designing their products and services.

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4

Mizgier, Kamil J., Otto Kocsis, and Stephan M. Wagner. "Zurich Insurance Uses Data Analytics to Leverage the BI Insurance Proposition." Interfaces 48, no. 2 (April 2018): 94–107. http://dx.doi.org/10.1287/inte.2017.0928.

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5

Infantino, Marta. "Big Data Analytics, Insurtech and Consumer Contracts: A European Appraisal." European Review of Private Law 30, Issue 4 (September 1, 2022): 613–34. http://dx.doi.org/10.54648/erpl2022030.

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The article investigates, from the European perspective, to what extent the enhanced availability of granular data to insurance companies and the growing sophistication of insurers’ processing capabilities through big data analytics (BDA) are fostering the increasing personalization of insurance products and services for consumers. To this purpose, the article first explores the very notion of ‘automated personalization’ in insurance, and then delves into the institutional, epistemic, economic and legal factors that, in Europe, work as a constraint, at least in the short-term, to paradigmatic shifts in insurance consumers contracts. The analysis will hopefully demonstrate that automated personalization in consumer insurance contracts, in Europe, is for the time being more a myth than a reality. What does exist, by contrast, is a no less problematic trend towards mass customization and robotization of consumer insurance contracts, which fully deserves lawyers’ attention.

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6

Kajwang, Ben. "IMPLICATIONS FOR BIG DATA ANALYTICS ON CLAIMS FRAUD MANAGEMENT IN INSURANCE SECTOR." International Journal of Technology and Systems 7, no. 1 (July 29, 2022): 60–71. http://dx.doi.org/10.47604/ijts.1592.

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Purpose: Because of the enormous financial burden that insurance fraud places on businesses, executives are moving quickly to implement big data analytics and other forms of cutting-edge technology in order to combat the issue. The purpose of the study is to assess the implications for Big data analytics on claims fraud management in insurance sector. Methodology: This was accomplished through the use of a desktop literature review. The use of Google Scholar was utilized in order to locate seminal references and journal articles that were pertinent to the study. In order to meet the inclusion criteria, the papers had to be no more than ten years old. Findings: The study concludes that Big Data Analytics in the insurance industry is becoming a promising field for gaining insight from very large data sets, enhancing outcomes, and lowering costs. It has tremendous potential, but there are still obstacles to overcome. The findings demonstrated that digital fraud detection had a positive and significant impact on insurers' underwriting procedures. Unique contribution to theory, practice and policy: The research suggests that insurers should always strive to automate their claim processes. In addition, the study suggests that insurers implement elements of constructing digital insurance control mechanisms. Before incorporating new technologies and analytical tools, they recommend organizations to conduct a thorough cost-benefit analysis and scenario planning to address unintended outcomes.

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7

Moradi, Mohsen, and Seyed Mohammad Fateminejad. "Sharing and Analyzing Data to Reduce Insurance Fraud." Journal of Management and Accounting Studies 5, no. 03 (August 10, 2019): 96–100. http://dx.doi.org/10.24200/jmas.vol5iss03pp96-100.

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Insurance fraud is a multi-billion-dollar problem. Fraudulent practices occur frequently and often repeatedly. Fraud can be detected and prevented if appropriate data is collected, analyzed and shared among insurance companies.Methodology:Appropriate decision support and analytics can be developed to routinize fraud detection. Creating these decision support capabilities involves addressing managerial, technological, and data ownership issues.This article examines these issues in the context of using new data sources and predictive analytics to both reduce insurance fraud and improve customer service. Results:Evidence suggests that appropriate sharing of proprietary company data among industry participants and combining that data with external data, including social media and credit history data, can provide advanced data-driven decision support.Conclusion:Cooperative development and deployment of predictive analytics and decision support should reduce insurance costs while improving claims service. A process model is developed to encourage discussion and innovation in fraud detection and reduction..

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Lee, Gee Y., Scott Manski, and Tapabrata Maiti. "ACTUARIAL APPLICATIONS OF WORD EMBEDDING MODELS." ASTIN Bulletin 50, no. 1 (October 22, 2019): 1–24. http://dx.doi.org/10.1017/asb.2019.28.

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AbstractIn insurance analytics, textual descriptions of claims are often discarded, because traditional empirical analyses require numeric descriptor variables. This paper demonstrates how textual data can be easily used in insurance analytics. Using the concept of word similarities, we illustrate how to extract variables from text and incorporate them into claims analyses using standard generalized linear model or generalized additive regression model. This procedure is applied to the Wisconsin Local Government Property Insurance Fund (LGPIF) data, in order to demonstrate how insurance claims management and risk mitigation procedures can be improved. We illustrate two applications. First, we show how the claims classification problem can be solved using textual information. Second, we analyze the relationship between risk metrics and the probability of large losses. We obtain good results for both applications, where short textual descriptions of insurance claims are used for the extraction of features.

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9

Senousy, Youssef, Abdulaziz Shehab, Wael K. Hanna, Alaa M. Riad, Hazem A. El-bakry, and Nashaat Elkhamisy. "A Smart Social Insurance Big Data Analytics Framework Based on Machine Learning Algorithms." Cybernetics and Information Technologies 20, no. 1 (March 1, 2020): 95–111. http://dx.doi.org/10.2478/cait-2020-0007.

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AbstractSocial insurance is an individual’s protection against risks such as retirement, death or disability. Big data mining and analytics are a way that could help the insurers and the actuaries to get the optimal decision for the insured individuals. Dependently, this paper proposes a novel analytic framework for Egyptian Social insurance big data. NOSI’s data contains data, which need some pre-processing methods after extraction like replacing missing values, standardization and outlier/extreme data. The paper also presents using some mining methods, such as clustering and classification algorithms on the Egyptian social insurance dataset through an experiment. In clustering, we used K-means clustering and the result showed a silhouette score 0.138 with two clusters in the dataset features. In classification, we used the Support Vector Machine (SVM) classifier and classification results showed a high accuracy percentage of 94%.

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10

Quan, Zhiyu, and Emiliano A. Valdez. "Predictive analytics of insurance claims using multivariate decision trees." Dependence Modeling 6, no. 1 (December 1, 2018): 377–407. http://dx.doi.org/10.1515/demo-2018-0022.

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AbstractBecause of its many advantages, the use of decision trees has become an increasingly popular alternative predictive tool for building classification and regression models. Its origins date back for about five decades where the algorithm can be broadly described by repeatedly partitioning the regions of the explanatory variables and thereby creating a tree-based model for predicting the response. Innovations to the original methods, such as random forests and gradient boosting, have further improved the capabilities of using decision trees as a predictive model. In addition, the extension of using decision trees with multivariate response variables started to develop and it is the purpose of this paper to apply multivariate tree models to insurance claims data with correlated responses. This extension to multivariate response variables inherits several advantages of the univariate decision tree models such as distribution-free feature, ability to rank essential explanatory variables, and high predictive accuracy, to name a few. To illustrate the approach, we analyze a dataset drawn from the Wisconsin Local Government Property Insurance Fund (LGPIF)which offers multi-line insurance coverage of property, motor vehicle, and contractors’ equipments.With multivariate tree models, we are able to capture the inherent relationship among the response variables and we find that the marginal predictive model based on multivariate trees is an improvement in prediction accuracy from that based on simply the univariate trees.

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Дисертації з теми "Insurance analytics"

1

Killada, Parimala. "Data Analytics using Regression Models for Health Insurance Market place Data." University of Toledo / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1501721348961437.

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2

Lyxzén, Ivan. "Connecting customers to the correct insurance through statistics and data analysis Helping insurance agents through data analytics." Thesis, Umeå universitet, Institutionen för datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-172273.

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Many companies have built platforms for analyzing user data then using this to extrapolate useful information about customer segments, this project built such a platformed aimed at helping insurance agents better understand their customers. The platform was built as part of JaycomAB’s services and was built to fit their design, both in terms of database, controller and interface. The platform handles two main features, one that matches customers to suitable insurances through a model that uses Bayesian statistics, and the second one that presents charts and graphs depicting statistics. Individual insurance agents gave feedback during the project which was adapted into expanded features before the launch. In the future even more improvements are possible such as a node network for the database. The platform can also be adapted to suit new markets such as private investments or private healthcare.

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3

COREA, FRANCESCO. "Essays on machine learning for economics and finance." Doctoral thesis, Luiss Guido Carli, 2017. http://hdl.handle.net/11385/201135.

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Анотація:

Econometrics and machine learning are quite close and related concepts. Nowadays, it is always more important to extract value from raw data, and distilling actionable insights from quantitative values as well as qualitative features. In order to deal with these topics, the first chapters (Chapter 1 - 4) are going to introduce the new wave called machine learning or big data and they will explain the most common techniques used in the field, respectively regression, clustering, model selection, and tree-based models (Chapter 2); time series analysis (Chapter 3); and eventually forecasting model with shrinkage methods (Chapter 4). Then, three applications are going to be provided. In Chapter 5, it is going to be shown an example of big dataset for the insurance vertical. Rothschild and Stiglitz ([30]) argued that people signal their risk profile through their insurance demand, i.e. individuals with a high risk profile would buy insurance as much as they can, while people who are not going to buy any insurance are the ones with a lower risk profile. This issue is commonly known as adverse selection. Even if their prediction seems to work quite well in a lot of different markets, Cutler et al. ([13]) proved that there exist some insurance markets in United States in which the expected result is completely different. In the wake of this study, we provide empirical evidences that there are some European insurance markets in which the low risk profile agents are the ones who buy more insurance. In Chapter 6, a second application is going to be provided. It has been studies the effect of behavioural biases on entrepreneurial choices to insure their firms against kinds of corporate risks. It has been used a large sample of Italian Small and Medium sized - finding that they under-insure themselves. The dataset allows to link corporate insurance choices with the personal traits of the entrepreneur and his household’s financial choices. In Chapter 7, finally, an application to financial markets is going to be shown. Bollen et al. ([10]) reintroduced the idea of formulating prediction based on the general sentiment of the investors, even if they originally exploited microblogging data. The purpose of this study is to verify whether social data may have a predictive power for the stock prices, returns, and volumes. The analysis has been implemented for different large technology companies, and the robustness has been tested through a ten-days rolling window. The evidence shows that there is some intrinsic value in these new features, and that both the sentiment and the amount of tweets posted online can improve the forecast given by a baseline autoregressive model. Some additional variations have been tested eventually with the same dataset.

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4

Velenyi, Edit V. "Modeling demand for community-based health insurance : an analytical framework and evidence from India and Nigeria." Thesis, University of York, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.550247.

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The thesis offers three primary contributions to the evidence base on demand for community-based health insurance (CB HI): (i) a review of the literature; (ii) an extended analytical framework to guide empirical investigations of demand for CBHI; and (iii) applied analyses to test the hypothesis regarding the relevance and fit of the proposed extension, and explore central positive and normative questions related to demand for CBHI by low-income groups in India and Nigeria. Chapter 2 offers an appraisal of the empirical and theoretical literature on demand for CBHI. Consequently, it proposes an extended analytical framework, which includes vectors of covariates at the household, CBHI, community, and state levels. More importantly, it proposes to test the relevance of social capital in models for demand estimation of CBHI. This extension places the central thrust of the thesis at the intersection of insurance theory and development economics. Chapter 3 exploits cross-sectional household data to apply the proposed extended framework to draw inferences on the nature of demand for micro insurance in India. Results from discrete choice and linear models show that the additional vectors have an impact on choice. While our social capital measures are not robust, the model statistics suggest that the community vector plays a role in demand. Chapter 4 explores demand to understand the market potential of a pilot in Lagos. The analysis draws on household and provider data. The results are more robust in terms of the number of significant covariates and their economic effects than those found in India. As a result, there is stronger and more decomposed evidence on the importance of the extended sets of covariates. Heckman, bivariate and multivariate models show significant effects for the CBHI and community vectors that have larger marginal effects than those observed in the household vector. The investigation offers a methodological insight into the double bounded dichotomous choice contingent valuation method. The evidence from these empirical analyses corroborates the relevance of the extended framework. We found that using the individual and household-level vector alone to estimate demand for CBHI is detached from reality and leads to model misspecification. Although the analyses are hampered by data limitations, the economic effects of the additional vectors are substantial. Understanding the role of social capital could improve the impact of community-based interventions. While there is evidence of interest in insurance even among the poor, the economic size of contributions from low-income groups in absolute terms is limited. However, their individual and household efforts are not negligible, as the stated reservation prices constitute a significant share of their household consumption. These facts imply that, while low-income households value insurance and coverage is demanded, their financial constraints may constitute a price barrier if the premiums are not subsidized. The thesis identifies critical gaps for future investigation: (i) combining analytical approaches (ii) improving measurement of factors; (iii) expanding the geographic scope of research on CB HI, especially in countries where community-based resource mobilization is a policy priority, in order to improve the external validity of findings and, consequently the value of information for design and policy making.

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5

Hájek, Jan. "Oceňování nemovitostí pro potřeby pojišťovnictví - RD v Brně poškozený sněhem." Master's thesis, Vysoké učení technické v Brně. Ústav soudního inženýrství, 2014. http://www.nusl.cz/ntk/nusl-232906.

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The aim of the thesis is to determine the amount of indemnity for damage to the family house caused by excessive snow loads, calculation of material value (time value) immovable assets immediately before the insured event cost method using analytical methods wear, determining the cost of putting immovable in working condition, the calculation of substantive value of intangible assets for the repairs. In this thesis, the emphasis on the clarification process when the risk to the family house and a practical example of an insured event the immovable. At the same time the analysis of the results, which display graphs show how the event affected the development of insurance rates immovable.

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6

Brandes, Alina Christa Annemarie Verfasser], and Wolf [Akademischer Betreuer] [Rogowski. "External validation of decision-analytic models based on claims data of health insurance funds / Alina Christa Annemarie Brandes. Betreuer: Wolf Rogowski." München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2016. http://d-nb.info/1101343907/34.

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7

Motužienė-Marcinkevičiūtė, Živilė. "Gyvybės draudimo rinkos analizė socialiniu ir ekonominiu požiūriais." Master's thesis, Lithuanian Academic Libraries Network (LABT), 2009. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2009~D_20090608_160551-04018.

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Tyrimo objektas – gyvybės draudimas. Tyrimo dalykas – gyvybės draudimo analizės požiūriai. Tyrimo tikslas – išanalizavus gyvybės draudimo teorinius pagrindus ir identifikavus problemas, pasiūlyti gyvybės draudimo analizės metodiką, išanalizuoti gyvybės draudimą Lietuvoje ekonominiu ir socialiniu požiūriais bei pateikti išvadas. Uždaviniai: 1. Išanalizuoti gyvybės draudimo teorinius pagrindus ir identifikuoti problemas. 2. Nustatyti gyvybės draudimo analizės kriterijus, atrinkti rodiklius, geriausiai atspindinčius pasirinktus kriterijus ir sudaryti gyvybės draudimo analizės metodiką. 3. Išanalizuoti gyvybės draudimą Lietuvoje ekonominiu ir socialiniu požiūriais bei pateikti išvadas. Tyrimo metodai – mokslinės literatūros ir juridinių dokumentų analizė, loginė ir analitinė analizė, loginis abstraktus modeliavimas, ekonominiai – statistiniai duomenų rinkimo ir analizės metodai; statistinei informacijai apdoroti ir sisteminti panaudoti grupavimo, palyginimo ir grafinio vaizdavimo būdai. Tyrimo rezultatai: · Pirmoje darbo dalyje išnagrinėta gyvybės draudimo esmė ir samprata, gyvybės draudimo rūšys, funkcijos bei ekonominis ir socialinis požiūriai. Pateikiama gyvybės draudimo klasifikavimo kriterijų schema ir išskiriamos gyvybės draudimo analizės problemos. · Antroje darbo dalyje išanalizuoti gyvybės draudimo vertinimo metodai, išskirti gyvybės draudimo analizės kriterijai ir pateikta gyvybės draudimo analizės ekonominiu ir socialiniu požiūriais metodika. · Trečioje dalyje... [toliau žr. visą tekstą]
The object of research – life insurance. The object of research – life insurance analytical point of views. The aim of research – to desing a framework of life insurance analysis, to analyze economical and social point of views of life insurance in Lithuanian and to provide with conclusions. The objectives are: 1. To analyze theoretical point of views of life insurance and to identify their problems. 2. To determine the list of life insurance analysis criteria, to select indicators for these criteria and to desing a framework for analysis of life insurance. 3. To analyse economical and social point of views of life insurance in Lithuania and provide with conclusions. Methods of research: analysis of scientific papers and legal documents, logical analysis, logical abstractive modeling, economic – statistical data collection methods, data grouping, comparison and graphical representation. Research resuts: · Author analyses concept of life insurance, its types in the first part of the paper. Author emphasizes economical and social point of views of the life Insurance. Authors provides with a scheme of classification criteria of life insurance. Also problems of life insurance analysis are listed in the first part of the paper. · Second part of the paper provides with review of methods of analysis of life insurance in the scientific literature. Author developed a list of criteria used by researchers in the literature. Based on these review author propose a method for analysis of... [to full text]

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8

Kraus, Jan. "Ocenění výše škody způsobené přívalovým deštěm na rodinném domě v obci Nesovice." Master's thesis, Vysoké učení technické v Brně. Ústav soudního inženýrství, 2015. http://www.nusl.cz/ntk/nusl-233111.

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The thesis focuses on the valuation of damage caused by torrential rain to the family house in the village Nesovice. In the first part the basic terms and assessing methods are defined. In the second part, there are applied methods of appraisal of damage on the family house. There is the value of the property calculated before the insurance event. In the itemized budget, there are quantified the cost of repairs of damaged constructions and then detected current value after repairs. The aim of the thesis is to dedicate readers to the problem of assessment.

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9

Maggi, Piero. "Enhanced web analytics for health insurance." Master's thesis, 2020. http://hdl.handle.net/10362/101010.

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Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics
Nowadays companies need invest and improve on data solution implementation within most of the business workflows and processes, in order to differentiate the offer and stay ahead of their competitors. It’s becoming more and more important to take data driven decisions to boost profitability and improve the overall customer experience. In this way, strategies are defined not anymore on common beliefs and assumptions, but on contextualized and trustful insights. This reports describes the work that has been made during a 9-month internship, in order to provide the business with a new and improved solution for enhancing the web analytics tasks and supporting the improve of the online user digital experience. User-level data related to the website activity has been extracted at the highest granularity level. Afterwards, raw data have been cleaned and stored in an Analytical Base Table with which an initial data exploration has been made. After giving initial insights to the digital team, a predictive model has been developed in order to predict the probability of the users to buy the insurance product online. Finally, based on the initial data exploration and the model’s results, a set of recommendations has been built and provided to the digital department for their implementation in order to make the website more engaging and dynamic.

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10

Chen, Jen-Ling, and 陳貞伶. "Big Data Analytics on Population Ageing Influence to National Health Insurance in Taiwan." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/78822293253822675599.

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碩士
東吳大學
經濟學系
105
Facing the current trend of the global aging population structure, Taiwan will give priority to face numerous tests from this phenomenon in short years. This study thinks that the first challenge is the change in supply and demand on the national medical healthcare. Therefore, this study will explore the influence of elderly population over the age of 65 to the National Health Insurance in Taiwan. The data on medical healthcare related to the National Health Insurance in Taiwan is a big data, which has accumulated for more than 20 years. This study searches for complete data that can be used and is suitable for this research issue from numerous and messy information. Eventually, this study adopts time series data and panel data to be used for exploration and analysis. The empirical results show that the growth of the elderly population has a significant influence to the National Health Insurance in Taiwan. In the time series data regression model, when the number of beneficiaries over the age of 65 and the elderly population is increased, the disposable income and the consumer price index have positive correlation. In the panel data regression model, increasing or decreasing of the outpatient medical expenses in each of the 22 cities/counties of Taiwan depends on the degree of population aging, the average employee number of each medical institution, and the unemployment rate. They are proportional to each other.

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Книги з теми "Insurance analytics"

1

Boobier, Tony. Analytics for Insurance. Chichester, UK: John Wiley & Sons, Ltd, 2016. http://dx.doi.org/10.1002/9781119316244.

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2

Gravelle, H. S. E. An exposition of some basic analytics of observability in insurance contracts. London: Queen Mary and Westfield College. Department of Economics, 1989.

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3

service), SpringerLink (Online, ed. Business Analytics for Managers. New York, NY: Springer Science+Business Media, LLC, 2011.

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4

service), SpringerLink (Online, ed. R for Business Analytics. New York, NY: Springer New York, 2013.

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5

Deventer, Donald R. van. Financial risk analytics: A term structure model approach for banking, insurance and investment management. Chicago, Ill: Irwin Professional Publ., 1997.

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6

Wickramanayake, J. Sri Lanka's financial system, 1960-1987: An analytical survey. Bundoora, Vic., Australia: School of Economics, Faculty of Social Sciences, La Trobe University, 1994.

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7

Mpuku, Herrick Chota. Theory and policy in export credit insurance and finance: An analytical and empirical study. Birmingham: University of Birmingham, 1992.

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8

Financial journals and serials: An analytical guide to accounting, banking, finance, insurance, and investment periodicals. New York: Greenwood Press, 1986.

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9

Fisher, William. Financial journals and serials: An analytical guide to accounting, banking, finance, insurance, and investment periodicals. New York: Greenwood Press, 1986.

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10

Zhuan xing qi Zhongguo yi liao bao xian ti xi zhong de zheng fu yu shi chang: Ji yu cheng zhen jing yan de fen xi kuang jia = The role of the government and the market in China's health insurance system during the transition period : an analytical framework based on urban experience. Beijing Shi: Beijing da xue chu ban she, 2010.

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Частини книг з теми "Insurance analytics"

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Huang, Wayne. "Transforming Insurance Business with Data Science." In Financial Data Analytics, 345–67. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-83799-0_12.

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Huang, Wayne. "Transforming Insurance Business with Data Science." In Financial Data Analytics, 345–67. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-83799-0_12.

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Prabhu, C. S. R., Aneesh Sreevallabh Chivukula, Aditya Mogadala, Rohit Ghosh, and L. M. Jenila Livingston. "Big Data Analytics for Insurance." In Big Data Analytics: Systems, Algorithms, Applications, 267–70. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0094-7_11.

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Gray, Thomas R. "Medical Liability Insurance Data Analytics." In Health Informatics, 407–15. New York: Productivity Press, 2022. http://dx.doi.org/10.4324/9780429423109-26.

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Szaniewski, Daniel. "Big data analytics in insurance." In The Digital Revolution in Banking, Insurance and Capital Markets, 190–203. London: Routledge, 2023. http://dx.doi.org/10.4324/9781003310082-16.

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Piesio, Michał, Maria Ganzha, and Marcin Paprzycki. "Applying Machine Learning to Anomaly Detection in Car Insurance Sales." In Big Data Analytics, 257–77. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-66665-1_17.

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Scriney, Michael, Dongyun Nie, and Mark Roantree. "Predicting Customer Churn for Insurance Data." In Big Data Analytics and Knowledge Discovery, 256–65. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59065-9_21.

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Sai Bhavana, A., and P. L. Srinivasa Murthy. "Automated Member Enrollment: Health Insurance Agency." In Learning and Analytics in Intelligent Systems, 97–106. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9293-5_8.

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Saporta, Gilbert. "From Conventional Data Analysis Methods to Big Data Analytics." In Big Data for Insurance Companies, 27–41. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2018. http://dx.doi.org/10.1002/9781119489368.ch2.

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Gibson, Teresa B., Zeynal Karaca, Gary Pickens, Michael Dworsky, Eli Cutler, Brian J. Moore, Richele Benevent, and Herbert Wong. "Young Adults, Health Insurance Expansions and Hospital Services Utilization." In Advanced Data Analytics in Health, 135–49. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77911-9_8.

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Тези доповідей конференцій з теми "Insurance analytics"

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Franke, Ulrik, and Per Hakon Meland. "Demand side expectations of cyber insurance." In 2019 International Conference on Cyber Situational Awareness, Data Analytics And Assessment (Cyber SA). IEEE, 2019. http://dx.doi.org/10.1109/cybersa.2019.8899685.

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Gunadi, Farhan, Muhammad Fauzi, Bagas Firdaus, and Afrida Helen. "Preprocessing Application for Car Insurance Claim Classification Model." In 2021 International Conference on Artificial Intelligence and Big Data Analytics (ICAIBDA). IEEE, 2021. http://dx.doi.org/10.1109/icaibda53487.2021.9689717.

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Li, Zengxiang, Zhe Xiao, Quanqing Xu, Ekanut Sotthiwat, Rick Siow Mong Goh, and Xueping Liang. "Blockchain and IoT Data Analytics for Fine-Grained Transportation Insurance." In 2018 IEEE 24th International Conference on Parallel and Distributed Systems (ICPADS). IEEE, 2018. http://dx.doi.org/10.1109/padsw.2018.8644599.

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Vo, Hoang Tam, Lenin Mehedy, Mukesh Mohania, and Ermyas Abebe. "Blockchain-based Data Management and Analytics for Micro-insurance Applications." In CIKM '17: ACM Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3132847.3133172.

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Lu, Jiaqi, Benjamin C. M. Fung, and William K. Cheung. "Embedding for Anomaly Detection on Health Insurance Claims." In 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 2020. http://dx.doi.org/10.1109/dsaa49011.2020.00060.

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Subudhi, Sharmila, and Suvasini Panigrahi. "Effect of Class Imbalanceness in Detecting Automobile Insurance Fraud." In 2018 2nd International Conference on Data Science and Business Analytics (ICDSBA). IEEE, 2018. http://dx.doi.org/10.1109/icdsba.2018.00104.

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Ahmad, Shakil, and Charu Saxena. "Internet of Things and Blockchain Technologies in the Insurance Sector." In 2022 3rd International Conference on Computing, Analytics and Networks (ICAN). IEEE, 2022. http://dx.doi.org/10.1109/ican56228.2022.10007267.

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Shamsuddin, Siti Nurasyikin, Noriszura Ismail, and Nur Firyal Roslan. "A bibliometric analysis of insurance literacy using bibliometrix an R package." In The 5th Innovation and Analytics Conference & Exhibition (IACE 2021). AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0092721.

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Uuganbayar, Ganbayar, Artsiom Yautsiukhin, and Fabio Martinelli. "Cyber Insurance and Security Interdependence: Friends or Foes?" In 2018 International Conference On Cyber Situational Awareness, Data Analytics And Assessment (Cyber SA). IEEE, 2018. http://dx.doi.org/10.1109/cybersa.2018.8551447.

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Meland, Per Hakon, and Fredrik Seehusen. "When to Treat Security Risks with Cyber Insurance." In 2018 International Conference On Cyber Situational Awareness, Data Analytics And Assessment (Cyber SA). IEEE, 2018. http://dx.doi.org/10.1109/cybersa.2018.8551456.

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Звіти організацій з теми "Insurance analytics"

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Volkova, Nataliia P., Nina O. Rizun, and Maryna V. Nehrey. Data science: opportunities to transform education. [б. в.], September 2019. http://dx.doi.org/10.31812/123456789/3241.

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Анотація:

The article concerns the issue of data science tools implementation, including the text mining and natural language processing algorithms for increasing the value of high education for development modern and technologically flexible society. Data science is the field of study that involves tools, algorithms, and knowledge of math and statistics to discover knowledge from the raw data. Data science is developing fast and penetrating all spheres of life. More people understand the importance of the science of data and the need for implementation in everyday life. Data science is used in business for business analytics and production, in sales for offerings and, for sales forecasting, in marketing for customizing customers, and recommendations on purchasing, digital marketing, in banking and insurance for risk assessment, fraud detection, scoring, and in medicine for disease forecasting, process automation and patient health monitoring, in tourism in the field of price analysis, flight safety, opinion mining etc. However, data science applications in education have been relatively limited, and many opportunities for advancing the fields still unexplored.

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Heathcote, Jonathan, Kjetil Storesletten, and Giovanni Violante. Consumption and Labor Supply with Partial Insurance: An Analytical Framework. Cambridge, MA: National Bureau of Economic Research, August 2009. http://dx.doi.org/10.3386/w15257.

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Svynarenko, Radion, Guoping Huang, Theresa L. Profant, and Lisa C. Lindley. Effectiveness of End-of-Life Strategies to Improve Health Outcomes and Reduce Disparities in Rural Appalachia: An Analytic Codebook. Pediatric End-of-Life (PedEOL) Care Research Group, College of Nursing, University of Tennessee, Knoxville, 2023. http://dx.doi.org/10.7290/n89xhm.

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Appalachia is one of the most medically underserved areas in the nation. The region has provider shortages and limited healthcare infrastructure. Children and adolescents in this area are in poor health and do not receive the needed quality care. Implementation of section 2302 of the 2010 Patient Protection and Affordable Care Act (ACA) enabled children enrolled in Medicaid/Children's Health Insurance Program with a terminal illness to use hospice care while continuing treatment for their terminal illness. In addition to being more comprehensive than standard hospice care, this relatively new type of care is more culturally congruent with the end-of-life values of rural Appalachian families, who often view standard hospice as hastening death. The overall goal of this project was to investigate access to pediatric concurrent hospice care in Appalachia. Our central hypothesis was that concurrent care reduces rural/urban disparities in access to hospice care. Data from the Centers for Medicare and Medicaid Services (CMS) used in this project was used and included 1,788 children who resided in the Appalachian region– from January 1, 2011, to December 31, 2013. Observations with missing birth dates, death dates, and participants older than 21 years were removed from the final sample. Geographic Information Systems (GIS) databases were created to map the boundaries of the Appalachian region, hospice locations, and driving times to them.

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Svynarenko, Radion, Theresa L. Profant, and Lisa C. Lindley. Effectiveness of concurrent care to improve pediatric and family outcomes at the end of life: An analytic codebook. Pediatric End-of-Life (PedEOL) Care Research Group, College of Nursing, University of Tennessee, Knoxville, 2022. http://dx.doi.org/10.7290/m5fbbq.

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Implementation of the section 2302 of the 2010 Patient Protection and Affordable Care Act (ACA) enabled children enrolled in Medicaid/Children's Health Insurance Program with a prognosis of 6 months to live to use hospice care while continuing treatment for their terminal illness. Although concurrent hospice care became available more than a decade ago, little is known about the socio-demographic and health characteristics of children who received concurrent care; health care services they received while enrolled in concurrent care, their continuity, management, intensity, fragmentation; and the costs of care. The purpose of this study was to answer these questions using national data from the Centers of Medicare and Medicaid Services (CMS), which covered the first three years of ACA – from January 1, 2011, to December 31, 2013.The database included records of 18,152 children younger than the age of 20, who were enrolled in Medicaid hospice care in the sampling time frame. Children in the database also had a total number of 42,764 hospice episodes. Observations were excluded if the date of birth or death was missing or participants were older than 21 years. To create this database CMS data were merged with three other complementary databases: the National Death Index (NDI) that provided information on death certificates of children; the U.S. Census Bureau American Community Survey that provided information on characteristics of communities where children resided; CMS Hospice Provider of Services files and CMS Hospice Utilization and Payment files were used for data on hospice providers, and with a database of rural areas created by the Health Resources and Services Administration (HRSA). In total, 130 variables were created, measuring demographics and health characteristics of children, characteristics of health providers, community characteristics, clinical characteristics, costs of care, and other variables.

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