Статті в журналах: "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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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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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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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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11

Nayak, Bishwajit, Som Sekhar Bhattacharyya, and Bala Krishnamoorthy. "Integrating wearable technology products and big data analytics in business strategy." Journal of Systems and Information Technology 21, no. 2 (May 13, 2019): 255–75. http://dx.doi.org/10.1108/jsit-08-2018-0109.

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Purpose This study aims to explore the impact of the adoption of wearable technology products for Indian health insurance firms. It identifies the key dynamic capabilities that health insurance firms should build to manage big data generated by wearable technology so as to attain a competitive advantage. Design/methodology/approach A qualitative exploratory study using in-depth personal interviews with 53 Indian health insurance experts was conducted with a semi-structured questionnaire. The data were coded using holistic and pattern codes and then analyzed using the content analysis technique. The findings were based on the thematic and relational intensity analysis of the codes. Findings An empirical model was established where all the propositions were strongly established except for the moderate relationship between wearable technology adoption and product innovation. The study established the nature of the interaction of variables on technology policy, organizational culture, strategic philosophy, product innovation, knowledge management and customer service quality with wearable technology adoption and also ascertained its influence on firm performance and competitive advantage. Research limitations/implications From a dynamic capabilities perspective, this study deliberates on wearable technology adoption in the health insurance context. It also explicates the relationship between the variables on technology policy, organizational culture, strategic philosophy, product innovation, knowledge management and customer service quality with wearable technology adoption on firm performance. Originality/value This study is one of the first studies to add the context of wearable technology and health insurance to the existing body of knowledge on dynamic capabilities and sustainable competitive advantage for the service sector. It would help existing and prospective players in adopting or setting up appropriate business models.

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12

Soyer, Βarış. "USE OF BIG DATA ANALYTICS AND SENSOR TECHNOLOGY IN CONSUMER INSURANCE CONTEXT: LEGAL AND PRACTICAL CHALLENGES." Cambridge Law Journal 81, no. 1 (March 2022): 165–94. http://dx.doi.org/10.1017/s0008197322000010.

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AbstractInsurers are increasingly using big data analytics and artificial intelligence in rating risks and customising insurance products particularly in the context of consumer insurance. The primary aim of this article is to elaborate the extent to which the legal rules in force could ensure that consumers are not treated unfairly as a result of the use of such disruptive technologies. Relevant insurance law principles and doctrines are also considered as part of this analysis. The article concludes that despite the protection provided to consumers by data and consumer protection legislation, unregulated and unlimited use of data analytics and algorithms in the risk assessment process could create significant difficulties for consumers. It is argued that further regulation, especially making regular audits essential for insurers employing such technologies in risk assessment process, is required. The article also finds that the use of artificial intelligence in customising insurance products does not present similar degree of difficulties for consumers.

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13

Asmuni, Nurin Haniah. "Evaluation of Risk Factor for Cancer Insurance using R Analytics." International Journal of Advanced Trends in Computer Science and Engineering 9, no. 1.4 (September 15, 2020): 92–99. http://dx.doi.org/10.30534/ijatcse/2020/1491.42020.

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14

الهادی, محمد, and Ehab Elhadad. "Insurance Business Enterprises' Intelligence in View of Big Data Analytics." مجلة الجمعیة المصریة لنظم المعلومات وتکنولوجیا الحاسبات 26, no. 26 (October 1, 2021): 13–21. http://dx.doi.org/10.21608/jstc.2021.202278.

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15

Zhang, Cheng, B. Vinodhini, and Bala Anand Muthu. "Deep Learning Assisted Medical Insurance Data Analytics With Multimedia System." International Journal of Interactive Multimedia and Artificial Intelligence In Press, In Press (2023): 1. http://dx.doi.org/10.9781/ijimai.2023.01.009.

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16

Fras, Mariusz, and Monika Szaraniec. "New technologies in insurance distribution. Selected legal problem sconcerning introducing innovations in the insurance market." Prawo Asekuracyjne 2, no. 99 (June 15, 2019): 90–109. http://dx.doi.org/10.5604/01.3001.0013.5675.

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Introducing New technologies in insurance distribution seems to be inevitable and should be based on applicable legal regulations.Thisprocess, however, isassociated with many legal problems in the regulatory and supervisory as wellas civil law. Robo-advice, artificial intelligence, digital cross-sellers insurance, Big Data Analytics and customer data generation, Internet of Things, as well as enti tiesapplying blockchain technology to manager insurance contracts, their liquidation and clients' claims constitute competition for traditional (classic) insurance distributor activity. Considerations, due to the limitem volume of the article, signal on selected legal problems (in national and EU law) regarding the introduction of innovations in the insurance market.

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Bhatt, Prerna Vishwakarma, and Shah Singh Khan. "Can Adoption of Disruptive Technologies Improve Performance of Insurance Firms in Developing Countries?" American Journal of Business and Strategic Management 1, no. 1 (June 21, 2022): 51–61. http://dx.doi.org/10.58425/ajbsm.v1i1.15.

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Purpose: This study aimed at assessing whether adoption of disruptive technologies can improve performance of insurance firms in developing countries. Methods: The study adopted a desktop research design. Relevant books references and journal articles for the study were identified using Google Scholar. The inclusion criteria entailed materials that were related to technology and the performance of insurance firms. Findings: The study found that various aspects of disruptive technologies have a significant impact on performance of insurance firms in developing countries. The findings also revealed that mobile phone technology has contributed to increased customer base and real-time customer service delivery. The study also established that disruptive technologies can enable real-time business evaluation through big data analytics thus boosting overall performance and profitability of insurance firms. Conclusion: There exist numerous disruptive technologies such as big data analytics, artificial intelligence systems, cloud computing, digital currency technologies, etc that can be adopted to boost performance of insurance firms in developing countries. Adoption of disruptive technologies can increase operational efficiency, increase customer base and increase competitive advantage. Recommendations: Insurance firms in developing countries should adopt disruptive technologies to improve on both financial and non-financial performance. The study recommend insurance firms in developing countries should redefine their operations to pave way for adoption of disruptive technologies. Also, developing countries should mandate their respective insurance regulatory bodies to create a favorable environment for the adoption of disruptive technologies.

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Schaefer, Eric, Evangelos Messaris, Christopher Hollenbeak, and Audrey Kulaylat. "Truven Health Analytics MarketScan Databases for Clinical Research in Colon and Rectal Surgery." Clinics in Colon and Rectal Surgery 32, no. 01 (January 2019): 054–60. http://dx.doi.org/10.1055/s-0038-1673354.

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AbstractThe MarketScan databases are a family of administrative claims databases that contain data on inpatient and outpatient claims, outpatient prescription claims, clinical utilization records, and healthcare expenditures. The three main databases available for use are each composed of a convenience sample for one of the following patient populations: (1) patients with employer-based health insurance from contributing employers, (2) Medicare beneficiaries who possess supplemental insurance paid by their employers, and (3) patients with Medicaid in one of eleven participating states. Eleven supplemental databases are available, which are utilized to overcome the limited clinical data available in the core MarketScan databases. There are several limitations to this database, primarily related to the fact that individuals or their family members within two of the core databases mandatorily possess some form of employer-based health insurance, which prevents the dataset from being nationally representative. Nonetheless, this database provides detailed and rigorously maintained claims data to identify healthcare utilization patterns among this cohort of patients.

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Hassani, Hossein, Stephan Unger, and Christina Beneki. "Big Data and Actuarial Science." Big Data and Cognitive Computing 4, no. 4 (December 19, 2020): 40. http://dx.doi.org/10.3390/bdcc4040040.

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This article investigates the impact of big data on the actuarial sector. The growing fields of applications of data analytics and data mining raise the ability for insurance companies to conduct more accurate policy pricing by incorporating a broader variety of data due to increased data availability. The analyzed areas of this paper span from automobile insurance policy pricing, mortality and healthcare modeling to estimation of harvest-, climate- and cyber risk as well as assessment of catastrophe risk such as storms, hurricanes, tornadoes, geomagnetic events, earthquakes, floods, and fires. We evaluate the current use of big data in these contexts and how the utilization of data analytics and data mining contribute to the prediction capabilities and accuracy of policy premium pricing of insurance companies. We find a high penetration of insurance policy pricing in almost all actuarial fields except in the modeling and pricing of cyber security risk due to lack of data in this area and prevailing data asymmetries, for which we identify the application of artificial intelligence, in particular machine learning techniques, as a possible solution to improve policy pricing accuracy and results.

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Moturi, Christopher A., Vincent O. Okemwa, and Daniel O. Orwa. "Big data analytics capability for digital transformation in the insurance sector." International Journal of Big Data Management 2, no. 1 (2022): 42. http://dx.doi.org/10.1504/ijbdm.2022.119435.

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Daniel, Ochieng, CHRISTOPHER MOTURI, and Vincent O. Okemwa. "Big Data Analytics Capability for Digital Transformation in the Insurance Sector." International Journal of Big Data Management 1, no. 1 (2020): 1. http://dx.doi.org/10.1504/ijbdm.2020.10034709.

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22

Longhi, Leonardo, and Mirco Nanni. "Car telematics big data analytics for insurance and innovative mobility services." Journal of Ambient Intelligence and Humanized Computing 11, no. 10 (December 14, 2019): 3989–99. http://dx.doi.org/10.1007/s12652-019-01632-4.

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23

McFall, Liz, Gert Meyers, and Ine Van Hoyweghen. "Editorial: The personalisation of insurance: Data, behaviour and innovation." Big Data & Society 7, no. 2 (July 2020): 205395172097370. http://dx.doi.org/10.1177/2053951720973707.

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The adoption of Big Data analytics (BDA) in insurance has proved controversial but there has been little analysis specifying how insurance practices are changing. Is insurance passively subject to the forces of disruptive innovation, moving away from the pooling of risk towards its personalisation or individualisation, and what might that mean in practice? This special theme situates disruptive innovations, particularly the experimental practices of behaviour-based personalisation, in the context of the practice and regulation of contemporary insurance. Our contributors argue that behaviour-based personalisation in insurance has different and broader implications than have yet been appreciated. BDAs are changing how insurance governs risk; how it knows, classifies, manages, prices and sells it, in ways that are more opaque and more extensive than the black boxes of in-car telematics.

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Kajwang, Ben. "IMPLICATIONS FOR BIG DATA AND ANALYTICS ON UNDERWRITING PRACTICES IN INSURANCE SECTOR." American Journal of Data, Information and Knowledge Management 3, no. 1 (July 29, 2022): 1–11. http://dx.doi.org/10.47672/ajdikm.1135.

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Purpose: Many businesses' approaches to data management have been revolutionized as a result of the advent of big data analytics. These days, companies are harnessing the power of the insights offered by big data in order to instantly establish more information about their customers and the ways in which they conduct business. The goal of this research is to analyze the implications for Big Data and analytics on underwriting practices 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. Papers that were published no more than ten years prior were required to meet the inclusion criteria. Findings: According to the findings of the study, top insurers' underwriting was significantly impacted by the digitization of their claims processes, which made use of big data and analytics. In this paper, the beneficial role of adopting technologies and tools of big data has been justified. These technologies and tools make it possible to develop powerful new business models, which, in turn, make it possible for the role of insurance to transition from "understand and protect" to "predict and prevent." Unique contribution to theory, practice and policy: According to the findings of the study, various aspects of building up digital insurance control mechanisms that assist in maintaining the data integrity of underwriting processes should be implemented. When such advanced security frameworks are implemented, data integrity can be assured, and instances of fraud losses can be significantly reduced. In addition to this, there is a requirement for need-based training to be provided to underwriters regarding the implementation of digital management systems. Moreover, insurance companies should take advantage of the opportunities presented by technology in order to provide underwriters with an early warning of any potentially fraudulent activities

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Karulkar, Yashodhan, and Simran Jain. "Forecasting Business Persistency at HDFC Life: Smart Insights Powered by Data Analytics." South Asian Journal of Business and Management Cases 9, no. 3 (October 26, 2020): 343–58. http://dx.doi.org/10.1177/2277977920958573.

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The life insurance industry is inherently a data-driven industry with various applications for analytical decision-making. Data science has influenced all business functions in an insurance organization to provide a distinct competitive advantage and push the industry towards the vision of ‘Insure Tech’. This case revolves around one such application of analytics in HDFC analytics dealing with championing the initiative for forecasting and analysis of Business Persistency. The persistency ratio is actually a fairly simple, yet very important metric that provides a snapshot of the health of the insurance industry. Considering the importance of this parameter, it became extremely important for HDFC Life to understand the factors behind persistency numbers and what lies ahead for the organization. The existing forecasting techniques were biased by the nature of work and did not give a significantly accurate and realistic number. The top management found this to be challenging for decision-making and decided that this required the intervention of the Business Insights department. Mr. Francis Rodrigues, SVP—Data Labs, Business Insights and Innovation was given the task to take over the pilot project and increase usage of analytical tools for Persistency Analysis. While Quarter 1 results have been significant, Mr. Francis Rodrigues still wonders whether he captured all the internal and external measures to obtain effective results. Has he done enough and how many more areas can analytics be applied for in the insurance domain? Research Question/Purpose: The current methods of persistency calculation are biased by the nature of work and deflect by a huge margin from the numbers actually achieved. There are no realistic forecasts for the coming year making the management uncertain with the decisions they take for strategic purposes. Theory: Time-indexed collection data help in predicting persistency numbers which are also influenced by some important variables behind collection follow-up and categories of customers. Type of the Case: Applied problem solving. Protagonist: Not needed. Options: Due to the availability of time-indexed data, time series was considered to be the best approach to forecast values into the future. However, that would only solve one half of the problem. It was also important to understand the parameters affecting persistency numbers, important variables behind persistency collections and classify the customers based on such variables. With this knowledge, classification models were also taken into consideration. Discussions and Case Questions: Besides time-indexed collection data which other variables amongst policy and demographic parameters influence persistency parameters? How can insights be drawn about the factors that are actually affecting the persistency numbers? Will calculating and sharing monthly persistency numbers for the coming year improve the percentage of customers remitting payment on time? By classifying customers into various categories with regards to the frequency of follow-up required for premium collection can a favourable segment be carved out?

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Zhou, Jiehui, Rongchen Zhu, Wei Zhang, Junhua Lu, Haochao Ying, Jian Wu, and Wei Chen. "MedicareVis: a Joint Visual Analytics Approach for Anti-Fraud in Medical Insurance." Journal of Computer-Aided Design & Computer Graphics 33, no. 9 (September 1, 2021): 1311–17. http://dx.doi.org/10.3724/sp.j.1089.2021.18981.

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Cather, David A. "Reconsidering insurance discrimination and adverse selection in an era of data analytics." Geneva Papers on Risk and Insurance - Issues and Practice 45, no. 3 (April 27, 2020): 426–56. http://dx.doi.org/10.1057/s41288-020-00166-7.

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Łańcucki, Jerzy. "The Impact of Innovative Technologies on the Insurance Market Operation." Prawo Asekuracyjne 2, no. 99 (June 15, 2019): 6–22. http://dx.doi.org/10.5604/01.3001.0013.5659.

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Innovative technologies are being increasingly used on the insurance market, as well. However, while analyzing and assessing their impact on the market what must be taken into account are not only distributors of insurance services, but also institutions performing regulatory and supervisory tasks, and perhaps above all, customers and consumers. The present article seeks to analyze the conditions, benefits and barriers associated with the application of innovative technologies with special regard to big data analytics, artificial intelligence and the possibility of absorption of innovative products and services by their customers.It has been emphasized that while evaluating the suitability of innovative technologies for the insurance market it is vital to confront product offers provided by insurance undertakings and intermediaries with the expectations, needs and skills of the purchasers of these products.

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Fytros, Charalampos. "The aporetic financialisation of insurance liabilities: Reserving under Solvency II." Finance and Society 7, no. 1 (May 4, 2021): 20–39. http://dx.doi.org/10.2218/finsoc.v7i1.5589.

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The valuation of insurance liabilities has traditionally been dealt with by actuaries, who closely monitored underlying illiquid features, assumed a long-term perspective, and exercised their own subjective, expert judgment. However, the new EU regulatory regime of Solvency II (S2) has come to require market-consistent valuation supplemented by a risk-sensitive capital. This is considered an unwanted shift towards short-termism that is misaligned with the industry’s long term and countercyclical character. The new principles place the ‘technicalising’ logic of financial economics over ‘contextualising’ actuarial know-how. Following existing analytics of valuation from the ethnography of reinsurance markets and the social studies of finance, such requirements appear either as an alarming attack against the actuarial component of traditional valuation practice, or else as a preserver of it, through a process of enfolding at the heart of the financialisation project. This article holds that the case of S2 challenges both these analytics of valuation. S2’s financialisation project, precisely by attempting to construct itself, deconstructs itself into an actuarial project, in a recurring, aporetic process. In this respect, fair (or otherwise) valuation remains always undecidable, inconclusive, and thus responsible.

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Muthu, Sathish, Madhan Jeyaraman, and Girinivasan Chellamuthu. "Research data: basis of future research analytics." MOJ Public Health 10, no. 2 (April 8, 2021): 35–38. http://dx.doi.org/10.15406/mojph.2021.10.00356.

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Living in an era of data explosion, there is an urgent need for making high-quality scholarly research data available for technologies like Artificial Intelligence(AI) to make something more out of it, other than publications. AI being portrayed as the sentience of machines, with more computational power and advances analytical methods has the potential to deliver personalised health care to individuals with greater probability. Although various data points such as electronic medical records, insurance company records, financial records, etc. can be used for such purposes research data remains the key building block based on which the deep learning algorithms can generate meaningful results. Hence, we discuss the significance, need and various methods for making high-quality scholarly research data available for the future to identify intricate and potentially unknown patterns hidden in them by harnessing the potential of AI. We also recommend research data deposition to be made a necessary pre-requisite before the publication of the results derived out of them.

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Hipps, Robert, Tushar Chopra, Peng Zhao, Edward Kwartler, and Sylvain Jaume. "Geospatial Analytics to Improve the Safety of Autonomous Vehicles." International Journal of Knowledge-Based Organizations 7, no. 3 (July 2017): 40–51. http://dx.doi.org/10.4018/ijkbo.2017070104.

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Finding the costs and risks associated with highway traffic routes would allow companies and people alike to find routes that offer a comfortable amount of risk. With the amount of traffic data being collected at a more granular level the ability to find costs and risks associated with traffic routes given real time circumstances is plausible. Weighing these data and finding the areas that are most accident-prone allows for an assessment of the probability that an accident happens and what the cost of that accident would be for any given route. This information is very valuable for both safety and cost saving for drivers and insurance companies.

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Owuor, Ezekiel. "IMPACT OF DISRUPTIVE TECHNOLOGY ON THE PERFORMANCE OF INSURANCE FIRMS IN KENYA." Journal of Strategic Management 3, no. 1 (November 30, 2018): 72. http://dx.doi.org/10.47672/jsm.367.

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Purpose: The purpose of this paper was to explore the impact of disruptive technology on the performance of insurance firms in Kenya.Methods: The study utilized desktop literature review and focused on previously published journals in PDF format that address technology and the performance of insurance firms. A total of 13 journals was found relating to technology and the performance of insurance firms. The study utilized a sample of 12 journals which were randomly selected from a list of published journals in PDF format relating to disruptive technology and performance of insurance firms. The theories underpinning of the study entailed Christensen's Theory of Disruptive Technology, the Diffusion of Innovation Theory and Schumpeterian Theory of Creative Destruction.Results: The review of literature revealed that various aspects of disruptive technology have a significant impact on organizational performance. The review showed that mobile phone technology has a significant influence and explains to a large extent the growth of micro insurance in Kenya. It was also found that the increase in industrial convergence, technological innovation and social digital trends increases the financial performance of financial institutions including insurance firms. The study also established that there is a strong and positive relationship between insurance innovation strategies and a firm’s performance. In addition, it was found out that real-time business evaluation through big data analytics boosts overall performance and profitability, thus thrusting the organization further into the growth cycle.Unique Contribution to theory, practice and policy: The leadership and management of insurance companies should put greater emphasis on the adoption of disruptive technologies to improve on both financial and non-financial performance as well as their competitiveness within the industry. These include Big Data, Analytics, Artificial Intelligence Systems, Cloud Computing and Digital Currency Technologies. Processes in the organizations should be refined to ensure that they are efficient and effective as this serves to increase market share and to reduce on operational costs. Moreover, explorations in disruptive technology should continue in the insurance industry as these would play a significant role in ensuring that efficiencies and effectiveness of business processes are achieved. The Insurance Regulatory Authority (IRA) should also develop policies that encourage innovation and the adoption of technology. The authority whilst exercising due diligence in its mandate to protect consumers should ensure policies do not stifle the growth and creativity of insurers. The regulatory body should also strive to create a favourable environment for the adoption of disruptive technologies.

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Alhabshi, Syed Musa. "Takāful Risk Fund and Surplus Management: Analytics for Social Equity?" Turkish Journal of Islamic Economics 8, Special Issue (June 15, 2021): 401–21. http://dx.doi.org/10.26414/a2375.

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An important and significant feature of Takāful Model is the proper administration and effective management of risk fund and the takāful surplus distribution. Unlike conventional insurance where all premiums paid are exchanged in consideration for underwriting risk pool assumed by the insurance company in the form of risk transfer, Takāful contribution is based on the principle of mutual trust, mutual risk pooling, and mutual benefit based on (ta’aun) cooperation and the social contract of tabarru’. The tabarru’ contribution to the risk fund exemplifies the social nature of the risk fund. Technically the nature of this mutual risk fund is based on social contribution intended to indemnify the participants as policyholders against peril arising from hazard. However, the issue of both moral and morale hazard may also arise due to the lack of policy and strategy in terms of governance and transparency of the risk fund. This article attempts to critically review the current takāful surplus management of selected takāful operator’s model based on Shariah principles, rulings, and requirements; and the regulatory guidelines and policy documents. It will highlight the issue(s) of social equity among the shareholders, takāful operators, and policyholders and present the analytics of takāful surplus based on the different takāful operating models as specified by the Takāful Operating Framework. It will also examine the consequences of takāful surplus management reporting practices and the implications of the adoption of relevant international financial reporting standards. Finally, this article will attempt to articulate the analytics for social equity in Takāful.

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Wang, Lidong, and Cheryl Ann Alexander. "Big Data Analytics for Medication Management in Diabetes Mellitus." International Journal of Studies in Nursing 1, no. 1 (October 28, 2016): 42. http://dx.doi.org/10.20849/ijsn.v1i1.99.

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Medication development plays a prominent role in the fight against chronic illness such as hypertension, diabetes mellitus, asthma, etc. Without proper testing and methods for management of drug data, the disease management would fail. Providers rely on pharmaceutical companies to provide research data in widespread formats and pharmaceutical companies rely on hospitals for electronic medical record data (EHR) and for pharmacy refill records from insurance companies. Big Data Analytics (BDA) provides an excellent basis to examine and manage terabytes of data that comprises drug data and can manage all aspects of drug development. This survey paper examines the current literature to determine what is current practice in the area of Big Data analytics and medication management.

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Xiong, Lu, Tingting Sun, and Randall Green. "Predictive analytics for 30-day hospital readmissions." Mathematical Foundations of Computing 5, no. 2 (2022): 93. http://dx.doi.org/10.3934/mfc.2021035.

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<p style='text-indent:20px;'>The 30-day hospital readmission rate is the percentage of patients who are readmitted within 30 days after the last hospital discharge. Hospitals with high readmission rates would have to pay penalties to the Centers for Medicare & Medicaid Services (CMS). Predicting the readmissions can help the hospital better allocate its resources to reduce the readmission rate. In this research, we use a data set from a hospital in North Carolina during the years from 2011 to 2016, including 71724 hospital admissions. We aim to provide a predictive model that can be helpful for related entities including hospitals, health insurance actuaries, and Medicare to reduce the cost and improve the clinical outcome of the healthcare system. We used R to process data and applied clustering, generalized linear model (GLM) and LASSO regressions to predict the 30-day readmissions. It turns out that the patient's age is the most important factor impacting hospital readmission. This research can help hospitals and CMS reduce costly readmissions.</p>

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Kostyunina, Tatyana. "Classification of operational risks in construction companies on the basis of big data." MATEC Web of Conferences 193 (2018): 05072. http://dx.doi.org/10.1051/matecconf/201819305072.

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Nowadays, Big Data is commonly used in many business sectors. Its use is also relevant for the construction industry. One of the most promising areas of Big Data technologies application is their use for risk analysis and assessment. Big Data represents an efficient way to manage modern risks by analyzing the unlimited amount of structured and unstructured information. The study examines principles of operational risks classification in construction companies on the basis of Big Data technologies. The final goal of such classification is the creation of a solution pattern for subsequent use of Big Data. As an example, a solution pattern for such business problem as "Construction: Detection of Insurance Fraud" is created. Application of the Big Data analytics for fraud detection has a series of advantages as compared to traditional approaches. Insurance companies can build systems that include all relevant data sources. An analysis of operational risks by means of self-organizing Kohonen maps on the basis of the Deductor analytical platform is performed.

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Sriram, Varun, Zijie Fan, and Ni Liu. "ECLIPSE: Holistic AI System for Preparing Insurer Policy Data." Risks 11, no. 1 (December 21, 2022): 4. http://dx.doi.org/10.3390/risks11010004.

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Reinsurers possess high volumes of policy listings data from insurers, which they use to provide insurers with analytical insights and modeling that guide reinsurance treaties. These insurers often act on the same data for their own internal modeling and analytics needs. The problem is this data is messy and needs significant preparation in order to extract meaningful insights. Traditionally, this has required intensive manual labor from actuaries. However, a host of modern AI techniques and ML system architectures introduced in the past decade can be applied to the problem of insurance data preparation. In this paper, we explore a novel application of AI/ML on policy listings data that poses its own unique challenges, by outlining the holistic AI-based platform we developed, ECLIPSE (Elegant Cleaning and Labeling of Insurance Policies while Standardizing Entities). With ECLIPSE, actuaries not only save time on data preparation but can build more effective loss models and provide crisper insights.

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Ho, Calvin W. L., Joseph Ali, and Karel Caals. "Ensuring trustworthy use of artificial intelligence and big data analytics in health insurance." Bulletin of the World Health Organization 98, no. 4 (February 25, 2020): 263–69. http://dx.doi.org/10.2471/blt.19.234732.

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Wang, Yibo, and Wei Xu. "Leveraging deep learning with LDA-based text analytics to detect automobile insurance fraud." Decision Support Systems 105 (January 2018): 87–95. http://dx.doi.org/10.1016/j.dss.2017.11.001.

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40

Denuit, Michel. "Investing in your own and peers’ risks: the simple analytics of P2P insurance." European Actuarial Journal 10, no. 2 (June 5, 2020): 335–59. http://dx.doi.org/10.1007/s13385-020-00238-x.

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Barry, Laurence, and Arthur Charpentier. "Personalization as a promise: Can Big Data change the practice of insurance?" Big Data & Society 7, no. 1 (January 2020): 205395172093514. http://dx.doi.org/10.1177/2053951720935143.

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The aim of this article is to assess the impact of Big Data technologies for insurance ratemaking, with a special focus on motor products.The first part shows how statistics and insurance mechanisms adopted the same aggregate viewpoint. It made visible regularities that were invisible at the individual level, further supporting the classificatory approach of insurance and the assumption that all members of a class are identical risks. The second part focuses on the reversal of perspective currently occurring in data analysis with predictive analytics, and how this conceptually contradicts the collective basis of insurance. The tremendous volume of data and the personalization promise through accurate individual prediction indeed deeply shakes the homogeneity hypothesis behind pooling. The third part attempts to assess the extent of this shift in motor insurance. Onboard devices that collect continuous driving behavioural data could import this new paradigm into these products. An examination of the current state of research on models with telematics data shows however that the epistemological leap, for now, has not happened.

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Kalotay, Andrew, and Leslie Abreo. "Municipal bond insurance: identifying the best payment plan." Journal of Risk Finance 18, no. 1 (January 16, 2017): 48–54. http://dx.doi.org/10.1108/jrf-06-2016-0082.

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Purpose The volume of municipal bond insurance declined dramatically following the financial crisis of 2008. Insurance now is making a gradual comeback. Two related considerations complicate identification of the best insurance plan. One is the current practice in the municipal market of issuing callable bonds with an above-market coupon; such bonds are very likely to be refunded. The other is that the cost of insurance may depend on when the bonds are refunded. This paper shows how contemporary fixed income analytics can be applied to identifying the best payment plan. Design/methodology/approach When the structure of the bond issue is fixed, the benefit from insurance is simply in the increase in proceeds from the better pricing. The debt service is adjusted to incorporate the cashflows associated with insurance. The optimum time of refunding depends on the adjusted cashflows. The effective insurance cost is the difference between the present value of the debt service with and without the adjustment for insurance payments. Findings The timing of refunding is a critical determinant of which premium payment plan is the best deal. For a given bond structure, the likelihood of refunding favors plans that are contingent on that event. Originality/value The paper proposes an analytically rigorous approach to identifying the most cost-effective bond insurance plan. The findings are relevant to participants in municipal finance, including issuers and their advisors, underwriters and bond insurance companies.

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p A, manoj Kumar, and Arun Aggarwal. "Determinants of Technology Adaption Within the Framework of TOE: An Insurance Sector Perspective." ECS Transactions 107, no. 1 (April 24, 2022): 3417–28. http://dx.doi.org/10.1149/10701.3417ecst.

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Over the last four years, several interesting technology drivers have hit the market. Each of the technology has affected the insurance industry significantly. The cost of acquiring the customer through the digital medium is the least compared to the traditional methods. These changes in the industry could be analyzed based on the social, technological, economic, environmental, and political aspects. The social changes include non-interaction with customary channels including agents and using social media and online channels where they can make the decisions. Robo advisors, Internet of Things, digital platforms, telematics, telemetry, data analytics, and big data are the main disruptors in the insurance industry. Big data has enabled new pricing models with greater risk segmentation. Artificial Intelligence (AI) and Machine Learning (ML) algorithms are used for predicting customer churn in the insurance industry.

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Drymalovska, K. V., and R. O. Kyryliuk. "The Main Features of the Modern World Market of Insurance Services." Business Inform 4, no. 519 (2021): 36–41. http://dx.doi.org/10.32983/2222-4459-2021-4-36-41.

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As competition in international and national markets intensifies, it is important to create a system to protect economic actors from potential threats and adverse factors. To solve these issues, it is necessary to ensure the effective functioning of the insurance market, which is one of the important components of financial security. Without the developed insurance market, it will be impossible to ensure the social and economic progress of the country, its corporate security, welfare etc. The current state of the world insurance market has certain features, which makes it possible to adapt to the change of the modern world and improve the work of the insurance industry. To create a clear insurance market, it is necessary to develop an effective policy as to the insurance activities of both the insurer and the reinsurers, as well as to establish solvency insurance systems. The development of the insurance market is accompanied by many economic, regulatory, organizational, methodological and personnel issues. The publication is aimed at studying and distinguishing the peculiarities of development of the world market of insurance services. On the basis of studying the works of scholars, the main features of the modern insurance market are provided; key signs of the insurance industry are presented; statistical information on trends in the insurance market development during 2019 in the following regions: North and Latin America, Western Europe, Asia, European developing countries. The impact of COVID-19 on the state of the insurance industry in Europe has been characterized. As result of studying the key trends in the future development of the insurance market, the main components that are necessary for the formation of an effective policy of insurance companies in the context of COVID-19 have been formed as follows: digitalization, innovation, analytics, feedback.

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Olga Sosnovska, Olga Sosnovska. "COMPETITIVE ADVANTAGES OF INSURER IN THE CONTEXT OF DIGITAL TRANSFORMATIONS OF INSURANCE BUSINESS IN UKRAINE." Socio World-Social Research & Behavioral Sciences 05, no. 03 (June 17, 2021): 34–43. http://dx.doi.org/10.36962/swd05032021034.

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The paper substantiates the need to ensure the competitiveness of insurance companies as an imperative of their successful functioning and innovative development. It is noted that in the context of the development of digitalization and changing the conditions of market competition, the key task is the presence of competitive advantages in the insurer – its unique characteristics, skills, resources, results and opportunities that allow withstanding the competition that exists in the insurance market. Based on indicators of insurance companies assessment by potential consumers of insurance services, internal and external competitive advantages are systematized as the main competences of ensuring the competitiveness of the insurer. It has been established that the current stage of the development of the insurance business is accompanied by a large-scale digital transformation in all spheres of economic activity, and the progressive digitalization of insurance relations is a key competitive advantage of insurance companies. The necessity of changing traditional business models and introduction of technological innovations to ensure a new level of quality of insurance services and preservation of competitive positions in the insurance business environment is substantiated. The directions of providing competitive advantages of the insurer on the basis of digitalization are proposed, among them the development of online communications through the Internet and social networks, a flexible approach to the offer of insurance services, automation of the insurer's key business processes, underwriting on the basis of big data and advanced analytics, interaction with InsurTech companies. The advantages and disadvantages of the insurance business development on the basis of digitalization are determined. Keywords: digitalization, competitiveness, competitive advantages, competencies, insurance business, technological innovations.

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Ray, Rajarshi (Raj), Zack Agar, Pratik Dutta, Streetam Ganguly, Purushottam Sah, and Debarshi Roy. "MenGO: A NOVEL CLOUD-BASED DIGITAL HEALTHCARE PLATFORM FOR ANDROLOGY POWERED BY ARTIFICIAL INTELLIGENCE, DATA SCIENCE & ANALYTICS, BIO- INFORMATICS AND BLOCKCHAIN." Biomedical Sciences Instrumentation 57, no. 4 (October 15, 2021): 476–85. http://dx.doi.org/10.34107/kszv7781.10476.

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Digital innovation & transformation, a technology revolution triggered by the latest advancements in the IT sector, has redefined several socially significant domains including healthcare, agriculture, food, finance, and education since the turn of this millennium. Although the power of digital technology has played a key role in modernizing many areas of the healthcare arena, a critical sub-category like andrology i.e., sexual and reproductive health of men, is yet to reap the full benefit of digitalization. This paper describes and explains how MenGO, the world’s 1st data science and analytics powered digital healthcare solution for andrology, is ushering in a new era of men’s health, a traditionally neglected domain, with innovative applications of cutting-edge technologies such as artificial intelligence, machine & deep learning, natural language processing, bioinformatics, blockchain, and cloud computing. MenGO offers custom recommendations, contextual guidance, smart alerts, in-depth report analytics, and statistical guidance for physicians, health institutions, biomedical researchers, pharma houses, insurance companies, and common users. The data analytics engine of MenGO helps users with personalized analytics, physicians with predictive and prescriptive analytics, and caregiving institutes with demographic analytics. A one-stop solution for men suffering from chronic ailments like erectile dysfunction, infertility, ejaculation problems, prostate gland issues, etc. MenGO helps users access affordable physiological and psychological treatments through its cloud and big data analytics powered smart and interactive telehealth platform.

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Wu, Pei-Ju, Mu-Chen Chen, and Chih-Kai Tsau. "The data-driven analytics for investigating cargo loss in logistics systems." International Journal of Physical Distribution & Logistics Management 47, no. 1 (February 13, 2017): 68–83. http://dx.doi.org/10.1108/ijpdlm-02-2016-0061.

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Purpose Cargo loss has been a major issue in logistics management. However, few studies have tackled the issue of cargo loss severity via business analytics. Hence, the purpose of this paper is to provide guidance about how to retrieve valuable information from logistics data and to develop cargo loss mitigation strategies for logistics risk management. Design/methodology/approach This study proposes a research design of business analytics to scrutinize the causes of cargo loss severity. Findings The empirical results of the decision tree analytics reveal that transit types, product categories, and shipping destinations are key factors behind cargo loss severity. Furthermore, strategies for cargo loss prevention were developed. Research limitations/implications The proposed framework of cargo loss analytics provides a research foundation for logistics risk management. Practical implications Companies with logistics data can utilize the proposed business analytics to identify cargo loss factors, while companies without logistics data can employ the proposed cargo loss mitigation strategies in their logistics systems. Originality/value This pioneer empirical study scrutinizes the critical cargo loss issues of cargo damage, cargo theft, and cargo liability insurance through exploiting real cargo loss data.

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Volosovych, Svitlana, Iryna Zelenitsa, Diana Kondratenko, Wojciech Szymla, and Ruslana Mamchur. "Transformation of insurance technologies in the context of a pandemic." Insurance Markets and Companies 12, no. 1 (January 19, 2021): 1–13. http://dx.doi.org/10.21511/ins.12(1).2021.01.

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The COVID-19 pandemic has affected different sectors of the economy, including insurance, and has become a problem and a clear catalyst for innovation. The pandemic has highlighted some inefficiencies of the traditional model of interaction between insurers and their customers and focused on insurance companies’ efforts on innovations and investments in the digital future. That is why the article aims to generalize the transformations of the institutional environment in the InsurTech ecosystem in the context of the COVID-19 pandemic and identify prospects for its development in the post-pandemic period.The analysis of the functioning of InsurTech as an ecosystem necessitated the identification of challenges for the insurance market in the context of COVID-19. The peculiarities of the insurance market development have been identified: the blurring of boundaries between insurers, BigTech firms, and technological partners; expanding interaction with policyholders based on the principle of support and the use of social networks; changes in the structure of the implemented insurance services; an increase in insurance fraud cases; the growing demand for parametric insurance products; introduction of a digital approach to the interaction with customers and employees, modernization of technological infrastructure and expansion of data processing capabilities; remote risk identification; acceleration in the use of financial technologies by insurance market participants. There is a transformation of the insurance market under the influence of business processes digitalization because insurers are aware of the importance of InsurTech in the formation of competitive advantages. For many companies, the crisis has strengthened their innovative development strategies and accelerated the implementation of financial technology tools in their business processes against the background of modernization of technological infrastructure. Chatbots, telematics, the Internet of Things, machine learning, artificial intelligence, predictive analytics, etc., are widely used. In the future, InsurTech will also play an important role in introducing digital innovations in the insurance market.

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Bhatnagar, Vishal, and Jayanthi Ranjan. "Time to implement data mining in insurance firms for effective CRM and CRM analytics." International Journal of Networking and Virtual Organisations 9, no. 1 (2011): 1. http://dx.doi.org/10.1504/ijnvo.2011.040932.

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Fang, Kuangnan, Yefei Jiang, and Malin Song. "Customer profitability forecasting using Big Data analytics: A case study of the insurance industry." Computers & Industrial Engineering 101 (November 2016): 554–64. http://dx.doi.org/10.1016/j.cie.2016.09.011.

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