Big Data: A Disruptive Innovation in the Insurance Sector

Introduction: The Internet has tremendously transformed the computer and networking world. Information reaches our fingertips and adds data to our repository within a second. Big data was initially defined as three Vs, where data come with greater variety, increasing volumes and extra velocity. Big data is a collection of structured, unstructured and semi-structured data gathered from different sources and applications. It has become the most powerful buzzword in almost all the business sectors. The real success of any industry can be counted based on how the big data is analysed, potential knowledge is discovered and productive business decisions are made. New technologies such as artificial intelligence and machine learning have added more efficiency to storing and analysing data. This big data analytics (BDA) becomes more valuable to those companies, focusing on getting insight into customer behaviour, trends and patterns. This popularity of big data has inspired insurance companies to utilise big data at their core systems and advance the financial operations, improve customer service, construct a personalised environment and take all possible measures to increase revenue and profits.Purpose: This study aims to recognise what big data stands for in the insurance sector and how the application of BDA has opened the door for new and innovative changes in the insurance industry.Methodology: This study describes the field of BDA in the insurance sector, discusses the benefits, outlines tools, architectural framework, the method, describes applications in general and specific and briefly discusses the opportunities and challenges.Findings: The study concludes that BDA in insurance is evolving into a promising field for providing insight from very large data sets and improving outcomes while reducing costs. Its potential is great; however, there remain challenges to overcome.

The banking, financial services, and insurance (BFSI) sector is one of the earliest and most prominent adopters of artificial intelligence (AI). However, academic research substantially lags behind the adoption of AI in practice. At the beginning of this century, AI research has been centered on the sector's credit risk. In the 2010s decade, expert systems were increasingly replaced by data‐driven, “algorithmic” AI. Big data enjoyed much hype in that decade, which diminished later mostly due to unsuccessful implementations. Much published research on big data actually relates to machine and deep learning but not to big data per se. These terms are often found to be conflated in research and practice. The insurance sector is substantially underrepresented in published AI research, and current research is dominated by banking and investments. Governance frameworks for “responsible AI” (RAI) are yet to be incorporated into practice by fintech companies as well as incumbent organizations. RAI is a particular issue for decentralized finance (DeFi). The most successful implementations of AI in BFSI practice, as well as dominant academic research areas, are in investments, securities, market making, customer relationships, lending, risk management, and compliance.

FigureThis month’s continuing education activity, “Pressure Ulcer-Related Pain in Nursing Home Residents with Cognitive Impairment,” on page 375, is an example of the use of information collected for regulatory and clinical data sets to inform our clinical decision-making processes in the field. The data tackle and try to make clinical sense of 3 problems in which there is a wide variability and subjectivity in the practice patterns and clinical decision making: (1) pain, (2) cognitive impairment, and (3) pressure ulcer management. This work by Ahn et al demonstrates how “big data” are currently utilized and their potential for the future. Big Data Big data are simply the use of ginormous bits or sets of data in the terabyte-and-beyond range,1 which are exponentially increasing by the minute, owing to the capacity of data collection, storage, and analysis. The use of big data is made possible by the recent explosion in computing capacity in business, manufacturing, and now healthcare. Some examples of early adopters in data management in wound care and skin-related diseases were in the United Kingdom, where they use the General Practice Research Database, which is a large database that was established in the United Kingdom in 1987 to allow researchers to conduct large epidemiologic studies.2 Disruptive Innovation The use of big data is an example of the concept of “disruptive innovation.” Disruptive innovation3 is a concept whereby the healthcare industry can transform to provide increasingly affordable and conveniently accessible healthcare by expanding the use of data, teaching workers at the point of service, and transferring skills from “highly trained but also expensive personnel to more affordable providers, including the use of technology-based care.”3 This concept was persuasively modeled by Sharkey et al.4 Not only did they make use of big data by incorporating the data from 14 nursing homes, but they also tested the concept of disruptive innovation in healthcare by measuring the utility of “training up” nursing assistants to improve quality, cost, and outcomes in wound care. This month’s continuing education article is an example of big data and the opportunity for disruptive innovation, which is inherently more complicated given the complexities of the confluence of pressure ulcers, pain, and dementia. It is unlikely that a person trained in pain medicine will be practicing in the nursing home environment, especially with expertise in both the management of dementia and wounds. The opportunity for disruptive innovation is to specifically train the staff in recognizing and documenting the patient’s pain and treatment outcomes. We now have big data on our side. Perhaps by incorporating more national data, we can learn more about this complicated issue proposed by the Ahn et al article in this issue. “The roots of great innovation are never just in the technology itself. They are always in the wider historical context. They require new ways of seeing. As Einstein put it, ‘The significant problems we face cannot be solved at the same level of thinking we were at when we created them.’” —David Brooks.5FigureRichard “Sal” Salcido, MD

The broad diffusion of so-called disruptive technologies in the public sector is expected to heavily impact and give a strong digital boost to public service provisioning. To ensure acceptance and sustainability, the benefits and challenges of using disruptive technologies in public service provisioning need to be well researched. This chapter applies scenario-based science and technology roadmapping to outline potential future uses of disruptive technologies. It develops a roadmap of research for Government 3.0. Based on a literature review of disruptive technologies in Government 3.0, thirteen scenarios sketch possible use of internet of things, artificial intelligence, machine learning, virtual and augmented reality, big data and other disruptive technologies in public service provisioning. Subsequently, gap analysis is applied to derive a roadmap of research, which outlines nineteen research actions to boost innovation in public service with the use of disruptive technologies, thereby building on engagement of and interaction with expert stakeholders from different fields. We conclude with recommendations for a broader and more informed discussion about how such new (disruptive) technologies can be successfully deployed in the public sector—leveraging the expected benefits of these technologies while at the same time mitigating the drawbacks affiliated with them.KeywordsGovernment 3.0Disruptive technologiesRoadmappingScenariosScenario-based science and technology roadmapping

While the public sector traditionally lags behind business in innovation, significant changes are anticipated with the broad diffusion of so-called disruptive technologies. The use of such technologies in public service, along with possible benefits, need to be well researched, and challenges be carefully discussed, analysed and evaluated. This paper applies scenario-based science and technology roadmapping to identify research and training needs in the implementation of disruptive technologies in public service. 70 experts reviewed 13 future scenarios and derived a number of research and training needs regarding internet of things, artificial intelligence, virtual and augmented reality, big data and other disruptive technologies. The identified needs serve as a starting point for a broader and more informed discussion about how such new (disruptive) technologies can be successfully deployed in the public sector - leveraging the benefits of these technologies while at the same time constraining the drawbacks affiliated with them.

Insurance plays a fundamental role in the economies of nations worldwide. Protecting the future earnings of individuals, companies from uncertainties, and risks fastens a nation’s gross domestic product (GDP) [1]. The second decade (2D) of the current century (2D/twenty-first century) has witnessed a revolutionary disruptive technological advancement, innovative strategies, and financially engineered models in the marketing of insurance products across the insurance sector worldwide. Some of these technologies and advancements are named as “FinTech, InsurTech, Blockchain Technology, Cryptocurrency, Robotics, Cloud Technologies, Data Science, Data Analytics, Big Data, Financial Engineering, ICTs (Information and Communication Technology), IoT (Internet of Things), AI (Artificial Intelligence), Machine Learning, Mobile Phones, Social Media sites, and Drown Technologies” [2]. To list and to explain these major disruptive technologies of the sector is the main purpose of this research work. The main focus is to study the specially designed financial engineering models in the process of insurance marketing. To conclude, the detailed role of financial engineering applications in the design, development, and execution of marketing models in the insurance sector is discussed. Descriptive-cum-exploratory research methodology is used.

The insurance sector has entered a profound transformation process driven by technological advancements. Particularly in products such as individual retirement and life insurance, the ability to respond quickly and accurately to customer needs has become one of the key determinants of competitive advantage in the industry. In this context, Big Data analytics stands out among the innovations offered by Insurtech technologies. Big Data methods have had a significant impact on predicting customer behaviors, risk modeling, and optimizing processes within the insurance sector. This study focuses on the modernization of a mobile application aimed at digitizing individual retirement and life insurance processes for a leading insurance company with a high market share in Turkey. The core innovative aspect of the project is the integration of algorithms supported by Big Data technologies into the sales and post-sales support processes of individual retirement and life insurance products. Through this approach, in-depth analysis of user behaviors, personalized recommendations, and risk management solutions are provided. This initiative introduces an innovative digitalization model that optimizes operational processes and enhances user experience in the insurance sector.

This chapter describes the impact of Big Data Analytics on the insurance sector. Big Data should be understood as large data sets that are characterized by large volume, diversity, and complexity. The use of large data sets in the business activities of insurance companies makes it possible to improve every link in the insurance value chain and consequently allows for a competitive advantage. Big data allows for a better adjustment of the offer of insurance companies to clients, supports risk management, and the process of determining the amount of premium for the services provided. However, Big Data also raises numerous concerns, especially in the area of privacy. This chapter discusses both the current state of the application of Big Data by insurers and the challenges related to Big Data that the insurance sector is facing.

This study demonstrates how Machine Learning techniques and Big Data Analytics can be used in the insurance sector. Due to various web technologies, mobile devices, and sensor devices, the amount of data in the insurance sector is currently growing daily. Insurance companies deal with large amounts of data from different sources. The quality and quantity of this data may vary, making it difficult for machine learning algorithms to accurately analyze and predict risk. Data preparation, cleaning, and processing can be a time-consuming and expensive task. Machine Learning plays a significant role in converting data into information. Because Machine Learning has the ability to learn from the input data and is a fundamental part of data analytics tools, it learns from data to provide new insights, predictions, and decisions from vast amounts of data. In the insurance sector, machine learning has a wide range of uses, such as customer segmentation, fraud detection, customer retention, claim processing, and claim review. As a result of this study, machine learning creates various prediction models for the insurance industry such as AdaBoost, Naïve Bay, K-Nearest Neighbor, and Decision Tree. As a result, Machine Learning is currently seen as a fundamental game changer for insurance businesses. The potential use of machine learning in insurance businesses will be further investigated by integrating big data tools.

Ant Financial Services, by taking cloud computing and big data as underlying facilities and credit system as basis, opens up low-end and and individual customer market which is not satisfied in traditional financial market by disruptive service modes and concepts. with unique digital credit means, aiming at accurately measuring personal credit score, is one of the key links for Ant Financial Services to carry out disruptive innovation. Sesame Credit is in open beta period, in spite of wide attention and remarkable achievement, it is still at the experimental stage with certain limitations. Thus, this paper first discloses the disruptive innovation characteristics that Ant Financial Services has, then analyses and discusses the scoring of Sesame Credit, and finally on this basis proposes an improved personal credit scoring model. Index Terms - Ant financial services; Disruptive innovation; Sesame credit scoring; Improved personal credit scoring model. 1. Introduction Internet finance market is positioned at small and micro level, characterized by amount, fastness, and convenience, and it thus has outstanding advantages in the sector of and finance (1). Internet finance constantly innovates credit means by using big data mining technology to greatly reduce credit and financing costs of and enterprises (2) as well as information asymmetry. The key of the healthy development of Internet finance is to deepen the credit system. The improved and perfected credit system enhances the financial risk management level and the efficiency of resource allocation, thus it better provides convenient services to low-end long- tailed users in the financial market and achieves disruptive innovation in the financial sector. Ant Financial Services subordinate to Alibaba Group, as a typical representative of Internet financial companies, establishes Sesame Credit, the China's first personal digital credit throughout history, in the support of Internet platform, cloud computing, and big data technologies. It can serve low- end and and individual customer markets where cannot be satisfied in the traditional financial market. Sesame Credit is still in open beta period but has the room for improvement in the objective acquisition of data (3) as well as for improvement in the accurate scoring (4). Therefore, this paper first discloses the disruptive innovation characteristics of Ant Financial Services, then analyses and discusses Sesame Credit scoring, and finally on this basis proposes the improved personal credit scoring model to provide reference for personal credit scoring.

Purpose: Vision 2030 is a unique transformative social, economical and political blueprint being adopted by nations globally to improve their stature and the quality of life for their citizens. There exists a total of seventeen global goals agreed upon by world leaders to strive for better nations by the year 2030, widely known as sustainable development goals. These were launched in the year 2015 at a world summit of the United Nations. Countries all over the world had started making progress towards their individual targets by the year 2030 before the 2015 meeting, and the global summit not only streamlined the process but made it a common goal to make better societies for their occupants. The insurance sector bears many responsibilities. The sector caters to risk taking, risk bearing and investments. This study’s main aim is to investigate the role of the insurance sector in supporting the achievement of vision 2030.
 Methodology: In this study, a systematic search was carried using search engines such as Google Scholar, Google Books, Semantic Scholar, Science.gov and ResearchGate, using the keywords; “insurance”, “development”, and “vision 2030”. In total, 35 papers, published until between November 2012 and January 2022, were extracted from these databases. After removing duplicates, the titles and abstracts of these articles were reviewed based on the inclusion and exclusion criteria. The inclusion criteria relate to all books and researches that scrutinize the role insurance plays in attaining vision 2030 goals. The exclusion criteria included non-English articles and publications whose full text could not be accessed due to varying reasons. Subsequent to careful screening of these articles, books and publications a handful of them were picked to assist in this study.
 Findings: This study found that the insurance sector, in many of the nations discussed is tasked with provision of offering sound policies to citizens, as one of major goals of the vision 2030 is to improve the quality of life. In most of the third world countries, , the insurance sector is tasked with providing better governance solutions as well as insuring innovations that advocate for a better environment and climate change. The study also established that for the insurance sector to thrive while going hand in hand with development objectives, its prosperity heavily relies in its ability to adapt to technological changes and adapt new innovation such as artificial intelligence to operate smoothly in the future.
 Recommendations: This paper recommends that Insurance companies, to effectively support achievement of the vision 2030 should consider some of the propose approaches by this study. First and foremost, technology is rapidly evolving, and insurers need to invest in artificial intelligence, because as we know it, the world will be much further advanced in 2030, such as intelligent credit rating systems using big data and analytics. This will enhance efficiency in service delivery to individuals as well as corporate clients.

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.

Our thrust in this study was to speak to disruptive innovations in the face of cyber fraudsters who are on the prowl. The goal of this study was therefore, to show how Zimbabwe's financial services sector is embracing disruptive technology and the preliminary benefits; one of the country's largest financial institutions. The study adopted the qualitative methodology with a desk content analysis. The findings of the study are that: banks, as part of the financial services industry, have the pivotal roles in the growth and economic development of a nation; hence the necessity to protect these institutions from the antics of fraudsters. The study further discovered that Zimbabwean government's approach to innovation should be focused on large market changes rather than on existing infrastructure. The study also found out that cyber-attacks are inevitable; however, there are various advantages to embracing disruptive technologies for the financial industry as identified. There is need therefore, to fully comprehend disruptive technology and make its use more consummate. Apart from protecting information systems, computers, devices, programs, data and networks from internal or external threats, harm, damage, attacks or unauthorized access, - disruptive technologies identified in the study such as Cloud computing, Artificial Intelligence, Block chain technology, Cyber security, and Big Data; have other tangible benefits, ranging from serving more customers, expanding market share, to increasing revenue at a lower cost. The study came up with a couple of recommendations; that the financial service sector in Zimbabwe should adapt to disruptive technologies and move from conservatism. That Regulators and the Government should soften banking laws and regulation to allow quick adoption to new disruptive technologies and that the financial service sector must invest in financial and human resources to enable swift uptake of technological innovations.

The paper analyses the economic and social challenges of disruptive technologies in conditions of Industry 4.0 and Industry 5.0. The paper overviews research progress on Industry 4.0 and 5.0 and their influence on sustainable development. The research explains disruptive technologies trends for sustainable development. The paper examines the development process of “disruptive technologies”, which are numerous: telephone (replaced the telegraph), steamboats (replaced the sailing vessels), semiconductors (replaced the vacuum equipment), e-mail (instead of traditional mail), etc. The paper analyzes basic disruptive technologies for creating the Internet of Things. The paper shows potential economic characteristics of disruptive technologies for the nearest five-year perspective. It investigates the EU experience on the realization of Industries 4.0 and 5.0. The paper highlights the trends that positively impact business growth up to 2022 according to the EU Future of Jobs Report: increasing adoption of new technology and big data; advances in mobile internet; advances in artificial intelligence and cloud technology; shifts in national economic growth; expansion of education; advances in new energy supplies and technologies. The research demonstrates how disruptive technologies will accelerate by 2025 and how both positive and negative impacts on business will grow up. The article tackles the issues of the potential economic and social impact of disruptive technologies in the nearest future. It distinguishes possible consequences of the implementation of key disruptive technologies of our time: for example excessive psychological impact; the risk of creative potential reduction; increasing information dependence; reduced the privacy of personal life; risks of uncontrolled reduction of information security (for example, due to hackers); increased information vulnerability of civilization; risk of loss of human control over cyber systems, etc. Key words: economic challenges, social challenges, Industry 4.0, Industry 5.0, disruptive technology.

The issue of the impact of emerging and disruptive technologies on security policy is a major concern of the North Atlantic Treaty Alliance. This is also demonstrated by the meeting between the Board members and the newly-established Advisory Group for Emerging and Disruptive Technologies, consisting of top experts in the fields of Cyber, Artificial Intelligence, Quantum Computing, Big Data, Space, Robotics and Autonomous or Biotechnological Systems, to find new synergies between NATO, the private, governmental and academic sectors and to maintain the technological supremacy of the Alliance. At the same time, the fact that the Romanian Army has mastered the defining elements of the impact of emerging and disrupted technologies on security policy and acts to make them operational is demonstrated by the meeting of July 12, 2021, of the Minister of National Defense, Nicolae-Ionel Ciuca with Heidi Grant, director of the US Defense and Security Cooperation Agency, on which occasion Romania received from the US the name of “Dependable Undertaking (DU)” under which contracts for the purchase of military equipment can be concluded without any payment in advance. Based on these elements, we would like to continue to talk about some aspects of innovation in dual military technologies, such as the influence of emerging and disruptive technologies on the organization and use of the armed forces. The research method undertaken consisted in identifying bibliographic resources, studying them, drawing relevant conclusions and formulating points of view on the impact of emerging and disruptive technologies on security policies.