Big Data Analytics in the Insurance Market

Nowadays insurance industry has huge innovation potential. Several key vectors for developing the concept of insurance tech include machine learning, business analytics, consumer protection rules, Big Data, artificial intelligence, neural networks, blockchain, and telematics. Technological innovations become widespread only when a community that supports them emerges, and COVID-19 has rapidly accelerated the changes that were already in full swing to a greater extent than any other factor. COVID-19 has helped reinforce the story and illustrate the results that technologies achieve on a large scale. Modern marketing and management approaches in insurance are viewed as an activity to optimize and control the insurance company's innovation and marketing activities. It would allow taking a strategically advantageous position in the insurance market. There are two kinds of insurance marketing: structural and commodity. Structural marketing could help to solve the problem of the economic efficiency of the activity of insurance companies. Commodity marketing helps to improve financial activity and, as a result, to increase profitability. This article summarizes the arguments and counterarguments within the scientific discussion on the place and prospects marketing and management in insurance (strategies, functions, principles) in the context of key innovation metrics. The study's primary purpose is to confirm the hypothesis about the functional link between the level of innovative development of the country and key insurance determinants as drivers for transformation in marketing strategies of insurance companies. In this regard, the array of input data is presented in the form of seven independent variables (regressors), six of which denote innovation measures, one is control variable, and five dependent variables (regressands), which identify the insurance sector. The study of the impact of innovation metrics on the insurance sector of the country in the article is carried out in the following logical sequence: 1) the formation of an array of input data; selection of relevant indicators using Principal Component Analysis; 2) formalization of functional relationships between variables by constructing five-panel Multifactor regression models with Random Effects; and 3) interpretation of the obtained results. Seventeen countries of Central and Eastern Europe were selected as the object of the study for the period from 2004 till 2019. The study empirically confirms the above hypothesis, which is evidenced by the following identified dependences. Key insurance determinants depend on innovation fluctuations. The most significant positive influence on the dependent variables is exercised by the Innovations index, Research and development expenditure, and Patent applications by residents. The study results could be helpful for insurance companies that provide new insurance technologies and seek to optimize activities to support innovative development. The main directions of marketing and management in insurance should be considered from two positions applying new technologies in insurance marketing and introducing new insurance products or services.

The confluence of science, technology, and medicine in our dynamic digital era has spawned new data applications to develop prescriptive analytics, to improve healthcare personalization and precision medicine, and to automate the reporting of health data for clinical decisions.1 Data science in health care has seen recent and rapid progress along 3 paths: (1) through big data via the aggregation of large and complex data sets including electronic medical records, social media, genomic databases, and digitized physiological data from wireless mobile health devices2; (2) through new open-access initiatives that seek to leverage the availability of clinical trial, research, and citizen science data sources for data sharing3; and (3) in analytic techniques particularly for big data, including machine learning and artificial intelligence that may enhance the analyses of both structured and unstructured data.4 As new data sets are created, analyzed, and become increasingly available, several key questions emerge including the following: What is the quality of unstructured data generation? Will the use of nonstandardized methods in data processing with traditional software and hardware lead to data fragmentation and analyses that are nonreproducible? Will healthcare systems incorporate and use big data especially from new publically and patient-generated sources? How will physicians and researchers learn from new open-sourced data and big-data analytics? And ultimately, How can they acquire the skills to create a knowledge translation in data sciences?5 Practicing in an era of continuous payment reform and decline in research funding, early career investigators are challenged to keep up with the accelerating pace of change in medicine, all while being expected to provide meaningful contributions through productive clinical, educational, and research experiences.6 In this perspective, we aim to highlight how data science can catalyze professional advancement and discuss the implications of big data, open access, …

Big data applications and analytics are vital in proposing ultimate strategic decisions. The existing literature emphasizes that big data applications and analytics can empower those who apply Big Data Analytics during the COVID-19 pandemic. This paper reviews the existing literature specializing in big data applications pre and peri-COVID-19. A comparison between Pre and Peri of the pandemic for using Big Data applications is presented. The comparison is expanded to four highly recognized industry fields: Healthcare, Education, Transportation, and Banking. A discussion on the effectiveness of the four major types of data analytics across the mentioned industries is highlighted. Hence, this paper provides an illustrative description of the importance of big data applications in the era of COVID-19, as well as aligning the applications to their relevant big data analytics models. This review paper concludes that applying the ultimate big data applications and their associated data analytics models can harness the significant limitations faced by organizations during one of the most fateful pandemics worldwide. Future work will conduct a systematic literature review and a comparative analysis of the existing Big Data Systems and models. Moreover, future work will investigate the critical challenges of Big Data Analytics and applications during the COVID-19 pandemic.

Big data application has found drastic growth in every field since it estimates an appropriate result, and it can handle any volume of data. Data analytics models predict the target through which rise or fall of each data can be identified. Big data, when combined with data analytics, overcomes all the traditional technology and provides the best solution. COVID-19, a disease that came into the picture as it emerged from Wuhan city, China, made a complete change throughout the world. Curing this disease became a significant challenge yet. Big Data and data analytics through the COVID-19 data have predicted and found the recovery and mortality rate in many hospitals of many countries. The aim of this paper is discussed in Section IV by comparing three different data analytic models—logistic regression, Kaplan–Meier analysis and SIR model, used for prediction of COVID-19 using myocardial injury dataset. This paper also has a literature study on big data analytics. It concludes with a favourable result on the SIR model. The challenges so far faced by big data and data analytics add a recommendation for other countries to get involved with big data and data analytics on COVID-19.

Big data and data analytics are currently the buzzwords in both academia and industry to become data driven. Big data has been the trending topic in the accounting industry also. Big data and data analytics will have an important impact on accounting and accountants. Big data will improve the quality of accounting information and the accounting profession will continue to provide real-time and dynamic information to assist in decision-making. The purpose of this research is to investigate the impact of big data and big data analytics in accounting. Data analytics is one of the most recent developments in the accounting context. This study is qualitative in nature and adopted a literature review methodology to gain a better understanding of the study area. This literature review seeks to provide a description and evaluation of the impact of big data analytics on accounting. This research found that big data presents great opportunities for decision making in accounting and risks analysis, which indicated that companies could improve their performance, measure performance, manage risks and allow effective real-time decision-making with data analytics. This research revealed that accountants can create more value in a world of big data analytics and encourages accountants to get started with big data to find answers to risks in business operations as well as understand financial performance. It shows that relying on big data analytics will open new possibilities for accountants. This study contributes to the research literature in the area of big data analytics on accounting. The limitations of this study are that it utilizes few recent peer reviewed articles in the general accounting practice, therefore not exhaustive in describing how big data and big data analytics impacts accounting.

In this paper, we study the features of big data and data analytics. We see how Big Data contributes to mobile networks. We give a term in which big data generally refers to a large amount of digital data. Also, we estimate that the amount processed by "Big Data" systems will double every two years. Hence, Big Data on mobile networks need to be analyzed in-depth according to retrieve exciting and useful information. Big Data provides unprecedented opportunities for internet service providers to understand the behavior and requirements of their users, which in turn enables real-time decision making across a wide range of applications. After that, we mention the dimensions often describe the 4Vs of Big Data. We continue with the study about the use of big data analytics in mobile networks. As we see, new technologies for managing big data in a highly scalable, cost-effective, and damage-resistant manner are required. So, beyond 2020 the system capacity and data rates in mobile networks must support thousands of times more traffic than 2010 levels. Furthermore, we mention the end-to-end latency, the massive number of connections, the cost, the Quality of Experience, the Issues, and finally, the big data management. We continue with the study about the big data analytics in 5G. The 5G networks standardizing and the 5G mobile optimization are crucial areas. There are new research areas were exploring new analytics techniques in big data according to discover new patterns and extract knowledge from the data are collected. Big data analytics can provide organizations with the ability to profile and segment customers based on distinct socioeconomic characteristics and increase customer satisfaction and retention levels. Also, Big Data analytics techniques can provide telecom providers with in-depth knowledge of networks before making informed decisions. Also, as we see, these analytics techniques can help Telecommunication providers to monitor and analyze various types of data as well as event messages on networks. Important information, like business intelligence, can be extracted from momentary and stored data. Hence, the mass adoption of smartphones, mobile broadband modems, tablets, and mobile data applications has been overwhelmingly wireless. Operators bend under the pressure and cost of continuously adding capacity and improving coverage while maximizing the use of the existing components of their range. Advanced radio access technologies, and all Internet Protocols, open internet network architectures must evolve smoothly from 4G systems. So those needs are leading us to make a study about the heterogeneous network or else HetNet for 5G networks. We are continuing with the challenges, and we mention about the curse of modularity, dimensions procedure, feature engineering, non-linearity, Bonferonni's principle, category report, variance and bias, data locality, data heterogeneity, noisy data, data availability, real-time processing, and streaming, data provenance, and data security.

Introduction: Big data in the insurance industry can be defined as structured or unstructured data that can affect the rating, marketing, pricing, or underwriting. The five Vs of big data provide insurers with a valuable framework for converting their raw data into actionable information. These five Vs are specifically: (1) Volume: The need to look at the type of data and the internal systems; (2) Velocity: The speed at which big data is generated, collected, and refreshed; (3) Variety: Refers to both the structured and unstructured data; (4) Veracity: Refers to trustworthiness and confidence in data; and (5) Value: Refers to whether the data collected are good or bad.Purpose: Insurance companies face many data challenges. However, the administration of big data has allowed insurers to acknowledge the demand of their customers and develop more personalised products. In addition, it can be used to make correct decisions about insurance operations such as risk selection and pricing.Methodology: We do this by conducting a systematic literature review on big data. Our emphasis is on gathering information on the five Vs of the big data and the insurance market. Specifically, how big data can help in data-driven decisions.Findings: Big data technology has created an endless series of opportunities, which have ensured a surge in its usage. It has helped businesses make the process more systematic, cost-effective, and helped in the reduction in fraud and risk prediction.

Insurtech is closely associated with digital transformation by new entrants that seek to disrupt insurance markets. However, the insurtech concept also includes its use by incumbent insurance companies, which are actively deploying a wide variety of insurtech applications to protect their market positions through innovation of their existing business models, e.g. through improved business processes or new insurance services. A theoretical insurtech business innovation model is developed that captures the effects of digital technology in insurance markets by considering innovation as a multi-dimensional concept that encompasses business processes, novel insurance products and changes to the insurance value chain. This framework is applied to an empirical sample of digital leaders: three incumbents and four new entrants. The results illustrate a variety of insurtech applications that include the transformation of business processes, products and new types of value chain configuration, as well as relatively minor enhancements to existing systems and business practices. It is shown that all the new entrants exploit artificial intelligence, big data and digital technology to build brand-new insurance services that emphasise innovative product features, high customer value and a delightful customer experience. In contrast, the legacy insurance firms tend to use digital technology in a defensive manner, e.g. the enhancement of existing insurance services, distribution channels and market positions. The exception is the launch of a telematics insurance service by an incumbent firm, where the telematics insurance effectively operates as a standalone business within a legacy insurance firm. The theory model is effective at analysing and evaluating both the type and magnitude of innovation. The case studies make an empirical contribution by illustrating state-of-the-art innovation by insurance disruptors and contrasts this with the defensive and sometimes novel digital strategies of incumbent firms. Future trends and research opportunities are outlined.

Security model for Big Healthcare Data Lifecycle

Data Analytics is at the cutting edge of technology in areas where data is evolving widely and rapidly. This involves handling large volume of data, called Big Data with a wide variety of properties. Handling big data involves organizing and analyzing data. Many tools with different environments have evolved in recent times in handling big data. Few of them can accommodate structured or semi structured data. At a glance, using various Hadoop components Big Data can be scaled and handled. In this concern Big Data must be modelled before performing analytics which is imperative. Models were proposed for organizing big data, but most of them are conceptual. These models never declared a unified model for implementing big data. Moreover very few could quantify that object oriented relational data model can be used for organizing big data. As such it is not feasible to organize semi-structured and unstructured data, as big data is not limited to structured data alone. Big data analytics plays an important role in Healthcare sector where huge amount of data needs to be analyzed and the conclusion drawn. As Health data is increased by leaps and bounds the role of big data has also received greater importance. Hence this paper proposes an approach for modeling big oncology drug related data in Healthcare sector which implements analytics in optimal way. This model can be useful to young oncologist which brings years of experience in hand.

Voluminous amounts of data have been produced, since the past decade as the miniaturization of Internet of things (IoT) devices increases. However, such data are not useful without analytic power. Numerous big data, IoT, and analytics solutions have enabled people to obtain valuable insight into large data generated by IoT devices. However, these solutions are still in their infancy, and the domain lacks a comprehensive survey. This paper investigates the state-of-the-art research efforts directed toward big IoT data analytics. The relationship between big data analytics and IoT is explained. Moreover, this paper adds value by proposing a new architecture for big IoT data analytics. Furthermore, big IoT data analytic types, methods, and technologies for big data mining are discussed. Numerous notable use cases are also presented. Several opportunities brought by data analytics in IoT paradigm are then discussed. Finally, open research challenges, such as privacy, big data mining, visualization, and integration, are presented as future research directions.

This paper reviews the impact of data science and artificial intelligence (AI) on future ‘data-driven’ Insurance Markets. The impact of insurance automation (driven by so-called Black Swan events such as Covid-19) mirrors the impact of algorithmic trading that changed radically the Capital Markets (Koshiyama, et al., 2020). The data science technologies driving change include: Big data, AI analytics, Internet of Things, and Blockchain technologies. These technologies are important since they underpin the automation of the Insurance Markets and risk analysis, and provide the context for the algorithms, such as AI machine learning and computational statistics, which provide powerful analytics capabilities.New AI algorithms are constantly emerging, with each ‘strain’ mimicking a new form of human learning, reasoning, knowledge, and decision-making. The current main disrupting forms of learning include Deep Learning, Adversarial Learning, Federated Learning, Transfer and Meta Learning. Albeit these modes of learning have been in the AI/ML field more than a decade, they are now more applicable due to the availability of data, computing power and infrastructure. These forms of learning have produced new models (e.g., Long Short-Term Memory, Generative Adversarial Networks) and leverage important applications (e.g., Natural Language Processing, Adversarial Examples, Deep Fakes, etc.). These new models and applications will drive changes in future Insurance Markets, so it is important to understand their computational strengths and weaknesses.The contribution of this paper is to review the data science technologies and specifically AI algorithms, their computational strengths and weaknesses, and discuss their future impact on the Insurance Markets.

The growing role of integrated and insightful big and real-time data analytics platforms

Big data analytics sentiment: US-China reaction to data collection by business and government

The article examines the current state and digital vectors of the development of the insurance market of Ukraine and identifies promising directions for the digitalization of insurance. The essence of the insurance category is revealed. It is noted that the insurance market in Ukraine lags far behind developed countries. An analysis of the dynamics of the number of participants in the insurance market, the amount of assets of insurance companies, the structure of the insurance portfolio by types of insurance was carried out, and the main trends of the insurance market in Ukraine were revealed. The keys to the stable development of insurance activity in Ukraine have been defined as: quick response to new conditions and customer requests; the ability to adapt, accumulate positive experience, analyze and correct negative moments; develop harmoniously, find a balance between the accumulation of new knowledge and their practical application. Factors influencing and functioning of the domestic insurance market are determined. The features of modern trends in digital insurance tools, as an important mechanism for the growth of the insurance market, are revealed. It was found InsurTech that modern technologies are important for insurance companies. These InsurTech nclude artificial intelligence technologies, new inventions in the field of cyber security, big data analysis («bigdata), blockchain technologies, the Internet of Things (LoT ) and smartphone applications.