Littérature scientifique sur le sujet « Insurance analytics »
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Articles de revues sur le sujet "Insurance analytics"
Embrechts, P. « Insurance Analytics ». British Actuarial Journal 8, no 4 (1 octobre 2002) : 639–41. http://dx.doi.org/10.1017/s1357321700003858.
Texte intégralFrees, Edward W. « Analytics of Insurance Markets ». Annual Review of Financial Economics 7, no 1 (7 décembre 2015) : 253–77. http://dx.doi.org/10.1146/annurev-financial-111914-041815.
Texte intégralHuizinga, Tylor, Anteneh Ayanso, Miranda Smoor et Ted Wronski. « Exploring Insurance and Natural Disaster Tweets Using Text Analytics ». International Journal of Business Analytics 4, no 1 (janvier 2017) : 1–17. http://dx.doi.org/10.4018/ijban.2017010101.
Texte intégralMizgier, Kamil J., Otto Kocsis et Stephan M. Wagner. « Zurich Insurance Uses Data Analytics to Leverage the BI Insurance Proposition ». Interfaces 48, no 2 (avril 2018) : 94–107. http://dx.doi.org/10.1287/inte.2017.0928.
Texte intégralInfantino, Marta. « Big Data Analytics, Insurtech and Consumer Contracts : A European Appraisal ». European Review of Private Law 30, Issue 4 (1 septembre 2022) : 613–34. http://dx.doi.org/10.54648/erpl2022030.
Texte intégralKajwang, Ben. « IMPLICATIONS FOR BIG DATA ANALYTICS ON CLAIMS FRAUD MANAGEMENT IN INSURANCE SECTOR ». International Journal of Technology and Systems 7, no 1 (29 juillet 2022) : 60–71. http://dx.doi.org/10.47604/ijts.1592.
Texte intégralMoradi, Mohsen, et Seyed Mohammad Fateminejad. « Sharing and Analyzing Data to Reduce Insurance Fraud ». Journal of Management and Accounting Studies 5, no 03 (10 août 2019) : 96–100. http://dx.doi.org/10.24200/jmas.vol5iss03pp96-100.
Texte intégralLee, Gee Y., Scott Manski et Tapabrata Maiti. « ACTUARIAL APPLICATIONS OF WORD EMBEDDING MODELS ». ASTIN Bulletin 50, no 1 (22 octobre 2019) : 1–24. http://dx.doi.org/10.1017/asb.2019.28.
Texte intégralSenousy, Youssef, Abdulaziz Shehab, Wael K. Hanna, Alaa M. Riad, Hazem A. El-bakry et Nashaat Elkhamisy. « A Smart Social Insurance Big Data Analytics Framework Based on Machine Learning Algorithms ». Cybernetics and Information Technologies 20, no 1 (1 mars 2020) : 95–111. http://dx.doi.org/10.2478/cait-2020-0007.
Texte intégralQuan, Zhiyu, et Emiliano A. Valdez. « Predictive analytics of insurance claims using multivariate decision trees ». Dependence Modeling 6, no 1 (1 décembre 2018) : 377–407. http://dx.doi.org/10.1515/demo-2018-0022.
Texte intégralThèses sur le sujet "Insurance analytics"
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.
Texte intégralLyxzén, 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.
Texte intégralCOREA, FRANCESCO. « Essays on machine learning for economics and finance ». Doctoral thesis, Luiss Guido Carli, 2017. http://hdl.handle.net/11385/201135.
Texte intégralVelenyi, 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.
Texte intégralHájek, 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.
Texte intégralBrandes, Alina Christa Annemarie Verfasser], et 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.
Texte intégralMotužienė-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.
Texte intégralThe 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]
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.
Texte intégralMaggi, Piero. « Enhanced web analytics for health insurance ». Master's thesis, 2020. http://hdl.handle.net/10362/101010.
Texte intégralNowadays 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.
Chen, Jen-Ling, et 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.
Texte intégral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.
Livres sur le sujet "Insurance analytics"
Boobier, Tony. Analytics for Insurance. Chichester, UK : John Wiley & Sons, Ltd, 2016. http://dx.doi.org/10.1002/9781119316244.
Texte intégralGravelle, H. S. E. An exposition of some basic analytics of observability in insurance contracts. London : Queen Mary and Westfield College. Department of Economics, 1989.
Trouver le texte intégralservice), SpringerLink (Online, dir. Business Analytics for Managers. New York, NY : Springer Science+Business Media, LLC, 2011.
Trouver le texte intégralservice), SpringerLink (Online, dir. R for Business Analytics. New York, NY : Springer New York, 2013.
Trouver le texte intégralDeventer, Donald R. van. Financial risk analytics : A term structure model approach for banking, insurance and investment management. Chicago, Ill : Irwin Professional Publ., 1997.
Trouver le texte intégralWickramanayake, 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.
Trouver le texte intégralMpuku, Herrick Chota. Theory and policy in export credit insurance and finance : An analytical and empirical study. Birmingham : University of Birmingham, 1992.
Trouver le texte intégralFinancial journals and serials : An analytical guide to accounting, banking, finance, insurance, and investment periodicals. New York : Greenwood Press, 1986.
Trouver le texte intégralFisher, William. Financial journals and serials : An analytical guide to accounting, banking, finance, insurance, and investment periodicals. New York : Greenwood Press, 1986.
Trouver le texte intégralZhuan 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.
Trouver le texte intégralChapitres de livres sur le sujet "Insurance analytics"
Huang, Wayne. « Transforming Insurance Business with Data Science ». Dans Financial Data Analytics, 345–67. Cham : Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-83799-0_12.
Texte intégralHuang, Wayne. « Transforming Insurance Business with Data Science ». Dans Financial Data Analytics, 345–67. Cham : Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-83799-0_12.
Texte intégralPrabhu, C. S. R., Aneesh Sreevallabh Chivukula, Aditya Mogadala, Rohit Ghosh et L. M. Jenila Livingston. « Big Data Analytics for Insurance ». Dans Big Data Analytics : Systems, Algorithms, Applications, 267–70. Singapore : Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0094-7_11.
Texte intégralGray, Thomas R. « Medical Liability Insurance Data Analytics ». Dans Health Informatics, 407–15. New York : Productivity Press, 2022. http://dx.doi.org/10.4324/9780429423109-26.
Texte intégralSzaniewski, Daniel. « Big data analytics in insurance ». Dans The Digital Revolution in Banking, Insurance and Capital Markets, 190–203. London : Routledge, 2023. http://dx.doi.org/10.4324/9781003310082-16.
Texte intégralPiesio, Michał, Maria Ganzha et Marcin Paprzycki. « Applying Machine Learning to Anomaly Detection in Car Insurance Sales ». Dans Big Data Analytics, 257–77. Cham : Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-66665-1_17.
Texte intégralScriney, Michael, Dongyun Nie et Mark Roantree. « Predicting Customer Churn for Insurance Data ». Dans 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.
Texte intégralSai Bhavana, A., et P. L. Srinivasa Murthy. « Automated Member Enrollment : Health Insurance Agency ». Dans Learning and Analytics in Intelligent Systems, 97–106. Singapore : Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9293-5_8.
Texte intégralSaporta, Gilbert. « From Conventional Data Analysis Methods to Big Data Analytics ». Dans Big Data for Insurance Companies, 27–41. Hoboken, NJ, USA : John Wiley & Sons, Inc., 2018. http://dx.doi.org/10.1002/9781119489368.ch2.
Texte intégralGibson, Teresa B., Zeynal Karaca, Gary Pickens, Michael Dworsky, Eli Cutler, Brian J. Moore, Richele Benevent et Herbert Wong. « Young Adults, Health Insurance Expansions and Hospital Services Utilization ». Dans Advanced Data Analytics in Health, 135–49. Cham : Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77911-9_8.
Texte intégralActes de conférences sur le sujet "Insurance analytics"
Franke, Ulrik, et Per Hakon Meland. « Demand side expectations of cyber insurance ». Dans 2019 International Conference on Cyber Situational Awareness, Data Analytics And Assessment (Cyber SA). IEEE, 2019. http://dx.doi.org/10.1109/cybersa.2019.8899685.
Texte intégralGunadi, Farhan, Muhammad Fauzi, Bagas Firdaus et Afrida Helen. « Preprocessing Application for Car Insurance Claim Classification Model ». Dans 2021 International Conference on Artificial Intelligence and Big Data Analytics (ICAIBDA). IEEE, 2021. http://dx.doi.org/10.1109/icaibda53487.2021.9689717.
Texte intégralLi, Zengxiang, Zhe Xiao, Quanqing Xu, Ekanut Sotthiwat, Rick Siow Mong Goh et Xueping Liang. « Blockchain and IoT Data Analytics for Fine-Grained Transportation Insurance ». Dans 2018 IEEE 24th International Conference on Parallel and Distributed Systems (ICPADS). IEEE, 2018. http://dx.doi.org/10.1109/padsw.2018.8644599.
Texte intégralVo, Hoang Tam, Lenin Mehedy, Mukesh Mohania et Ermyas Abebe. « Blockchain-based Data Management and Analytics for Micro-insurance Applications ». Dans CIKM '17 : ACM Conference on Information and Knowledge Management. New York, NY, USA : ACM, 2017. http://dx.doi.org/10.1145/3132847.3133172.
Texte intégralLu, Jiaqi, Benjamin C. M. Fung et William K. Cheung. « Embedding for Anomaly Detection on Health Insurance Claims ». Dans 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 2020. http://dx.doi.org/10.1109/dsaa49011.2020.00060.
Texte intégralSubudhi, Sharmila, et Suvasini Panigrahi. « Effect of Class Imbalanceness in Detecting Automobile Insurance Fraud ». Dans 2018 2nd International Conference on Data Science and Business Analytics (ICDSBA). IEEE, 2018. http://dx.doi.org/10.1109/icdsba.2018.00104.
Texte intégralAhmad, Shakil, et Charu Saxena. « Internet of Things and Blockchain Technologies in the Insurance Sector ». Dans 2022 3rd International Conference on Computing, Analytics and Networks (ICAN). IEEE, 2022. http://dx.doi.org/10.1109/ican56228.2022.10007267.
Texte intégralShamsuddin, Siti Nurasyikin, Noriszura Ismail et Nur Firyal Roslan. « A bibliometric analysis of insurance literacy using bibliometrix an R package ». Dans The 5th Innovation and Analytics Conference & Exhibition (IACE 2021). AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0092721.
Texte intégralUuganbayar, Ganbayar, Artsiom Yautsiukhin et Fabio Martinelli. « Cyber Insurance and Security Interdependence : Friends or Foes ? » Dans 2018 International Conference On Cyber Situational Awareness, Data Analytics And Assessment (Cyber SA). IEEE, 2018. http://dx.doi.org/10.1109/cybersa.2018.8551447.
Texte intégralMeland, Per Hakon, et Fredrik Seehusen. « When to Treat Security Risks with Cyber Insurance ». Dans 2018 International Conference On Cyber Situational Awareness, Data Analytics And Assessment (Cyber SA). IEEE, 2018. http://dx.doi.org/10.1109/cybersa.2018.8551456.
Texte intégralRapports d'organisations sur le sujet "Insurance analytics"
Volkova, Nataliia P., Nina O. Rizun et Maryna V. Nehrey. Data science : opportunities to transform education. [б. в.], septembre 2019. http://dx.doi.org/10.31812/123456789/3241.
Texte intégralHeathcote, Jonathan, Kjetil Storesletten et Giovanni Violante. Consumption and Labor Supply with Partial Insurance : An Analytical Framework. Cambridge, MA : National Bureau of Economic Research, août 2009. http://dx.doi.org/10.3386/w15257.
Texte intégralSvynarenko, Radion, Guoping Huang, Theresa L. Profant et 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.
Texte intégralSvynarenko, Radion, Theresa L. Profant et 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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