New technologies in insurance distribution. Selected legal problem sconcerning introducing innovations in the insurance market

2019 ◽  
Vol 2 (99) ◽  
pp. 90-109
Author(s):  
Mariusz Fras ◽  
Monika Szaraniec
Keyword(s):  
Data Analytics ◽  
New Technologies ◽  
Big Data Analytics ◽  
Insurance Market ◽  
Data Generation ◽  
Legal Problem ◽  
Customer Data ◽  
Insurance Contracts ◽  
Blockchain Technology ◽  
Legal Problems

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.

An Effective Model for Consumer Need Prediction Using Big Data Analytics

2021 ◽  
Author(s):  
Yihao Tian
Keyword(s):  
Big Data ◽  
Consumer Behavior ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Customer Behavior ◽  
Data Set ◽  
Customer Data ◽  
Demand Prediction ◽  
Management Ratio ◽  
The Veil

Big data is an unstructured data set with a considerable volume, coming from various sources such as the internet, business organizations, etc., in various formats. Predicting consumer behavior is a core responsibility for most dealers. Market research can show consumer intentions; it can be a big order for a best-designed research project to penetrate the veil, protecting real customer motivations from closer scrutiny. Customer behavior usually focuses on customer data mining, and each model is structured at one stage to answer one query. Customer behavior prediction is a complex and unpredictable challenge. In this paper, advanced mathematical and big data analytical (BDA) methods to predict customer behavior. Predictive behavior analytics can provide modern marketers with multiple insights to optimize efforts in their strategies. This model goes beyond analyzing historical evidence and making the most knowledgeable assumptions about what will happen in the future using mathematical. Because the method is complex, it is quite straightforward for most customers. As a result, most consumer behavior models, so many variables that produce predictions that are usually quite accurate using big data. This paper attempts to develop a model of association rule mining to predict customers’ behavior, improve accuracy, and derive major consumer data patterns. The finding recommended BDA method improves Big data analytics usability in the organization (98.2%), risk management ratio (96.2%), operational cost (97.1%), customer feedback ratio (98.5%), and demand prediction ratio (95.2%).

Big Data and Big Data Analytics for Improved Healthcare Service and Management

2020 ◽  
Vol 8 (1) ◽  
pp. 13-51
Author(s):  
Pijush Kanti Dutta Pramanik ◽  
Saurabh Pal ◽  
Moutan Mukhopadhyay
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Healthcare Services ◽  
Healthcare Sector ◽  
Future Market ◽  
Data Generation ◽  
Related Data ◽  
Healthcare Data ◽  
Different Types

Like other fields, the healthcare sector has also been greatly impacted by big data. A huge volume of healthcare data and other related data are being continually generated from diverse sources. Tapping and analysing these data, suitably, would open up new avenues and opportunities for healthcare services. In view of that, this paper aims to present a systematic overview of big data and big data analytics, applicable to modern-day healthcare. Acknowledging the massive upsurge in healthcare data generation, various ‘V's, specific to healthcare big data, are identified. Different types of data analytics, applicable to healthcare, are discussed. Along with presenting the technological backbone of healthcare big data and analytics, the advantages and challenges of healthcare big data are meticulously explained. A brief report on the present and future market of healthcare big data and analytics is also presented. Besides, several applications and use cases are discussed with sufficient details.

The Compute Infrastructures for Big Data Analytics

2014 ◽  
pp. 74-109 ◽  
Author(s):  
Pethuru Raj
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Data Sources ◽  
Steady Increase ◽  
Data Generation ◽  
Abnormal Growth ◽  
The World ◽  
Tremendous Amount ◽  
And Storage

The implications of the digitization process among a bevy of trends are definitely many and memorable. One is the abnormal growth in data generation, gathering, and storage due to a steady increase in the number of data sources, structures, scopes, sizes, and speeds. In this chapter, the author shows some of the impactful developments brewing in the IT space, how the tremendous amount of data getting produced and processed all over the world impacts the IT and business domains, how next-generation IT infrastructures are accordingly getting refactored, remedied, and readied for the impending big data-induced challenges, how likely the move of the big data analytics discipline towards fulfilling the digital universe requirements of extracting and extrapolating actionable insights for the knowledge-parched is, and finally, the establishment and sustenance of the dreamt smarter planet.

Big Data Analytics in Bioinformatics

Biotechnology ◽  
2019 ◽  
pp. 1967-1984
Author(s):  
Dharmendra Trikamlal Patel
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Annual Growth ◽  
Predictive Analysis ◽  
The Internet ◽  
Data Generation ◽  
Intelligent Devices ◽  
The Internet Of Things

Voluminous data are being generated by various means. The Internet of Things (IoT) has emerged recently to group all manmade artificial things around us. Due to intelligent devices, the annual growth of data generation has increased rapidly, and it is expected that by 2020, it will reach more than 40 trillion GB. Data generated through devices are in unstructured form. Traditional techniques of descriptive and predictive analysis are not enough for that. Big Data Analytics have emerged to perform descriptive and predictive analysis on such voluminous data. This chapter first deals with the introduction to Big Data Analytics. Big Data Analytics is very essential in Bioinformatics field as the size of human genome sometimes reaches 200 GB. The chapter next deals with different types of big data in Bioinformatics. The chapter describes several problems and challenges based on big data in Bioinformatics. Finally, the chapter deals with techniques of Big Data Analytics in the Bioinformatics field.

Research Challenges in Big Data Analytics

2017 ◽  
pp. 83-99
Author(s):  
Sivamathi Chokkalingam ◽  
Vijayarani S.
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Large Scale ◽  
New Technologies ◽  
Big Data Analytics ◽  
Large Data ◽  
Data Sets ◽  
Data Types ◽  
Customer Preferences ◽  
Research Challenges

The term Big Data refers to large-scale information management and analysis technologies that exceed the capability of traditional data processing technologies. Big Data is differentiated from traditional technologies in three ways: volume, velocity and variety of data. Big data analytics is the process of analyzing large data sets which contains a variety of data types to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information. Since Big Data is new emerging field, there is a need for development of new technologies and algorithms for handling big data. The main objective of this paper is to provide knowledge about various research challenges of Big Data analytics. A brief overview of various types of Big Data analytics is discussed in this paper. For each analytics, the paper describes process steps and tools. A banking application is given for each analytics. Some of research challenges and possible solutions for those challenges of big data analytics are also discussed.

Big Data and Big Data Analytics

2021 ◽  
pp. 1-19
Author(s):  
Sam Goundar ◽  
Akashdeep Bhardwaj ◽  
Shavindar Singh ◽  
Mandeep Singh ◽  
Gururaj H. L.
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Business Processes ◽  
New Technologies ◽  
Big Data Analytics ◽  
Small Island ◽  
Island State ◽  
Diverse Data ◽  
Analytical Tools ◽  
Small Island State

Big data is emerging, and the latest developments in technology have spawned enormous amounts of data. The traditional databases lack the capabilities to handle this diverse data and thus has led to the employment of new technologies, methods, and tools. This research discusses big data, the available big data analytical tools, the need to use big data analytics with its benefits and challenges. Through a research drawing on survey questionnaires, observation of the business processes, interviews and secondary research methods, the organizations, and companies in a small island state are identified to survey which of them use analytical tools to handle big data and the benefits it proposes to these businesses. Organizations and companies that do not use these tools were also surveyed and reasons were outlined as to why these organizations hesitate to utilize such tools.

The Impact of Innovative Technologies on the Insurance Market Operation

2019 ◽  
Vol 2 (99) ◽  
pp. 6-22 ◽  
Author(s):  
Jerzy Łańcucki
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Present Article ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Insurance Market ◽  
Innovative Technologies ◽  
Market Operation ◽  
Innovative Products ◽  
The Impact

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.

scholarly journals SURVEY ON ACCURACY OF PREDICTIVE BIG DATA ANALYTICS IN HEALTHCARE

2019 ◽  
Vol 01 (02) ◽  
pp. 77-86
Author(s):  
Smys S
Keyword(s):  
Health Care ◽  
Big Data ◽  
Data Analytics ◽  
New Technologies ◽  
Big Data Analytics ◽  
Health Care Industry ◽  
Research Area ◽  
Care Industry ◽  
The Core ◽  
The Cost

The failures in the most of research area, identified that the lack of details about the actionable and the valuable data that conceived actual solutions were the core of the crisis, this was very true in case of the health care industry where even the early diagnoses of a chronic disease could not save a person’s life. This because of the impossibility in the prediction of the individual’s outcomes in the entire population. The evolving new technologies have changed this scenario leveraging the mobile devices and the internet services such as the sensor network and the smart monitors, enhancing the practical healthcare using the predictive modeling acquiring a deeper individual measures. This affords the researches to go through the huge set of data and identify the patterns along with the trends and delivering solutions improvising the medical care, minimizing the cost and he regulating the health admittance, ensuring the safety of human lives. The paper provides the survey on the predictive big data analysis and accuracy it provides in the health care system.

scholarly journals Big Data and IoT Opportunities for Small and Medium-Sized Enterprises (SMEs)

2019 ◽  
pp. 77-88
Author(s):  
Siti Aishah Mohd Selamat ◽  
Simant Prakoonwit ◽  
Reza Sahandi ◽  
Wajid Khan
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
21St Century ◽  
Data Analytics ◽  
New Technologies ◽  
Big Data Analytics ◽  
Technological Advancement ◽  
New Wave ◽  
Advanced Technologies ◽  
Data Explosion

The advancement of technology and emergence of internet of things (IoT) has exponentially caused a data explosion in the 21st century era. As such, the arrival of IoT is set to revolutionize the development of the small and medium-sized enterprise (SME) organizations by shaping it into a more universal and integrated ecosystem. Despite evidential studies of the potential of advanced technologies for businesses, the SMEs are apprehensive towards new technologies adoption such as big data analytics and IoT. Therefore, the aim of this chapter is to provide a holistic study of big data and IoT opportunities, challenges, and applications within the SMEs context. The authors hope that the outcome of this study would provide foundational information on how the SMEs can partake with the new wave technological advancement and in turn, spurring more SMEs for adoption.

Sign in / Sign up

Export Citation Format

Share Document