Big Data: A Game Changer for Insurance Industry

Introduction: There is a variety of wearables and health applications available in the market which allow the tracking of various health and lifestyle measures like blood sugar, calorie counter, number of steps, sleep patterns, etc. After the Covid-19 pandemic, people have become more aware of their health and use these wearables to maintain a healthy lifestyle. Insurance companies in India are also eyeing the potential usage of these wearables in life and health insurance.

Purpose: This research aims to look at the emergence of wearables and health apps and their usage in India’s life and health insurance industry. This study also focuses on how these devices might benefit insurers’ business models and some of the pitfalls to consider.

Methodology: The study used both primary and secondary data. A survey was conducted to understand the customer perception towards usage of wearables. The secondary research included the analysis of the integration of wearables by insurance companies.

Findings: The research would be helpful to the insurance companies as it would help them to understand the customer’s viewpoint for the usage of wearables in the insurance industry. This study would also allow insurers to understand new dimensions, such as where the wearables improve customer satisfaction and engagement. The study results would be helpful for the customers for the appropriate usage of wearables and the internet of things (IoT). Insurance companies can provide better pricing and make personalised insurance plans that ultimately help customers.

Introduction: Big data is that disruptive force that affects businesses, industries, and the economy. In 2021, insurance analytics will include more than simply analysing statistics. According to current trends, new insurance big data analytics (BDA) methods will enable firms to do more with their data. The insurance business has traditionally been conservative, but adopting new technology is no longer only a current trend; it must be competitive. Big data technologies aid in processing a huge amount of data, improve workflow efficiency, and lower operating costs.

Purpose: Some of the most recent developments in big data for insurance and how insurers may use the information to stay ahead of their competitors are discussed in this chapter. This chapter’s prime purpose is to analyse how artificial intelligence (AI), blockchain, and mobile technology change the outlook and working of the insurance sector.

Methodology: To achieve our research purpose, we analyse case studies and literature that emphasise how BDA revolutionises the insurance market. For this purpose, various articles and studies on BDA in the insurance market will be selected and studied.

Findings: From the analysis, we find that the use of big data in the insurance business is growing. The development of BDA has proven to be a game-changing technology in insurance, with a slew of benefits. The insurance sector is now grappling with the risks and opportunities that modern technology presents. Big data offers opportunities that every company must avail of. We can safely argue that big data has transformed the insurance sector for the better. The BDA’s consequences have enabled insurers to target clients more accurately. This chapter highlights that new tools and technologies of big data in the insurance market are increasing. AI is emerging as a powerful technology that can alter the entire insurance value stream. The transmission of any type of digital proof for underwriting, including the use of digital health data, might be a blockchain use case (electronic health record (EHR)). As digital forensics becomes easier to include in underwriting, it must expect price and product design changes in the future. In the future, the internet of things (IoT) and AI will combine to automate insurance processes, causing our sector to transform dramatically. We highlight that these technologies transformed insurance practices and revolutionalised the insurance market.

Introduction: The internet of things (IoT) is the emerging technology of interconnected objects that can be termed as ‘things’ used to exchange data, connecting with different devices on the internet. It is the future where connected devices are controlled remotely. The insurance sector is one of the leading industries providing financial protection services to their customers to recover losses. Like others, the insurance industry uses the services very efficiently to solve their customer-centric problems and provide the best services to them. IoT in insurance is enhancing customer services.

Purpose: To determine how the insurance industry utilises the different IoT technologies to provide the best services and solutions to their users. The insurance sector is working on other areas of expertise to offer outstanding facilities to their clientele.

Methodology: We reviewed published material covering five years on IoT and insurance and customer services in the media, newspapers, journal publications, and the web. We determined how the insurance sector adapted the new terminology to contribute its best services to the users.

Findings: We observed that IoT services and technologies benefit the insurance industry and the clientele. This shows excellent results in the growth of the sector and heightened facilities for the consumers.

Introduction: The world is passing through a technology explosion phase where one technology is being replaced by another very quickly. Emerging technologies play more important roles in the insurance sector directly or indirectly. These technologies have a high potential to change the insurance paradigm.

Purpose: In this chapter, we discuss emerging technologies such as artificial intelligence (AI), big data, blockchain, the internet of things (IoT), mobile technology, predictive analytics, social media, telematics, chatbots, low codes, and drones in the context of the insurance industry.

Methodology: To carry out our analysis, we searched for data using the keywords for each technology from the Web of Science (WoS) coral database. Certain inclusion and exclusion criteria were followed to select the articles for further analysis. R-studio was used for the data analysis and visualisation.

Findings: It was found that the highest number of research articles published are related to big data, followed by AI and social media. The first article on AI in insurance appeared in 1975. Social media is the highest cited new technology, whereas the low codes are the undiscovered paradigm for the insurance sector with no published research. Research on the impact of chatbots, drones, and mobile technology in the insurance industry is still at a nascent stage. We also noticed that the United States is leading the research on emerging technologies in the insurance sector.

Implications: This chapter audits the emerging technologies in the insurance sector and identifies technological areas with the highest, least, or no research, dominant journals, authors, and countries. This holistic overview empowers managers and academicians to decide the future course of action.

Introduction: The insurance industry is one of the lucrative sectors of the economy. However, it is volatile because of the large chunk of data generated by the transactions taking place daily. However, every bit of it is responsible for creating market trends for stock investors to predict the returns. The specialised data mining techniques act as a solution for decision-making, reducing uncertainty in decision-making.

Purpose: There are limited studies that have examined the efficiency and effectiveness of data mining techniques across the companies in the insurance industry to date. To enable the companies to take exact benefit of data mining techniques in insurance, the present study will focus on investigating the efficiency of artificial neural network (ANN) and support vector machine SVM across insurance companies of CNX 500.

Method: For predictive models, various technical indicators were considered independent variables, and change in return, i.e. increase and decrease, was deemed a dependent variable. The indicators were transformed from daily raw data of insurance company’s stock values spanning four years. We formed 90 data sets of varied periods for building the model – specifically six months, one year, two years, and four years for selected six insurance companies.

Findings: The study’s findings revealed that ANN performed best for the ICICIPRULI data model in terms of hit ratio. Whereas the performance of SVM was observed to be the best for the ICICIGI data model. In the case of pairwise comparison among the six selected Indian insurance companies from CNX 500, the extracted data evaluated and concluded that there were eight significantly different pairs based on hit ratio in the case of ANN models and nine significantly different pairs based on hit ratio for SVM models.

Introduction: Blockchain is gaining attention in various industries and sectors. It is described as an emergent technology with immense possibilities similar to how the internet has revolutionised how businesses are currently carried out. Still, various sectors have either not adopted or are in a very nascent stage to adopt blockchain technology in their operations. The current research examines how blockchain can be used in the insurance sector. This industry was chosen as it is extremely relevant in today’s world and directly bears its economy.

Purpose: To determine the current and future path in which the insurance industry is moving about blockchain technology adoption and find synergy between blockchain technology and the insurance business.

Need for study: The insurance industry is highly relevant in today’s world and directly bears the country’s economy. Additionally, blockchain is an emergent technology with immense possibilities similar to how the internet has revolutionised how businesses are done. The current research looks at how blockchain can be used in the insurance business.

Methodology: A systematic literature review was conducted in this study by reviewing literature related to blockchain technology and the insurance sector. Science direct was used as a source of information. For this study, the literature review approach was chosen since it allows us to trace the growth of the subject matter and identify the patterns that have formed through time.

Findings: The study found that the insurance sector has recognised the latent benefits of blockchain technology and has begun to develop its usage in selected cases such as fraud prevention and risk assessment.

Practical implications: The current study can be referred to by academicians, marketers, industry people, and policymakers. The study encourages companies and academicians to further investigate the usage of blockchain in insurance.

Purpose: The insurance business is confronted with coordination difficulties that necessitate a high level of mobility, flexibility, and the capacity to analyse heterogeneous, location-dependent data from different sources and qualities. Recent innovations in emerging technologies have given the insurance industry new organisational options. When coupled with data analytics, crowdsourcing in the insurance industry facilitates solving complex issues with the wisdom of crowds. The notion of incorporating crowdsourcing and big data into the mainstream activities of insurance management is developed in this article, as are the ramifications and gains of collective intelligence achieved by Crowdsourcing and the added value of crowdsourcing insurance activities.

Design/methodology/approach: This chapter is a conceptual work that builds on relevant literature.

Findings: This chapter analyses what insurance industry managers should consider when coordinating crowdsourced activities and how they may benefit from collective intelligence combined with data analytics in terms of efficient and real-time response management for the insurance industry. Furthermore, it is demonstrated how they may use crowdsourcing to exploit information and benefit from invoking additional resources and eliminating the institutional voids present in the industry.

Practical implications: Exemplary applications that take advantage of crowdsourcing and data analytics would help the insurance sector respond flexibly, efficiently, and effectively in real time.

Originality/value: This chapter offers new collaborative ways to enhance the decision-making of insurance industry managers. The relevance of overcoming institutional voids is expanded, and repercussions from the given framework are suggested using data analytics.

Purpose: To analyse the insurance market breakthroughs through ‘Big Data’ and the possibility of new techniques of services provided, creating access for information gathering and fraud detection. This can contribute to improved risk management processes and mitigation strategies referred to as ‘InsurTech’.

Methodology: We catalogue the technique which is especially useful and being evaluated as having the ability to bring innovations to the insurance business. In doing this, we reveal which marketplaces actively participate in start-ups and how insurers engage in them and present them, highlighting the impact of blockchain technology, ride services, robo-advice, and data analysis on the insurance industry.

Findings: Findings show that because emerging economies have fewer organisation needs to ensure the distribution model, technology and research may significantly influence such areas. Nonetheless, whether industrialised or emergent, relevant legislative inspections should be carried out to protect subscribers’ welfare.

Practical implication: Since ‘Big Data’ impacts insurers’ constant monitoring of business risks and corporate governance, an overview of how information is harnessed should be carefully studied. Moreover, it is essential to study the handling of algorithms to guarantee that the expectations are reasonable and that unforeseen effects are avoided to the greatest extent feasible, and regulators have a mechanism for engaging in this review.

Introduction: All countries are interested in attracting foreign direct investment (FDI) as it provides for productivity gains and modernisation for attaining sustainable development goals. Multinational corporations (MNCs) collect a vast volume of structured and unstructured big data when seeking international expansion by the FDI route in the insurance sector, but concluding these data may not be practically feasible. So nowadays, for finalising their FDI ventures, MNCs depend on machine-based algorithms for quick analysis of big data sets.

Purpose: This chapter explores how emerging big data analytics and predictive modelling fields can scale and speed up FDI decisions in the insurance sector.

Methodology: The author used a descriptive study based on secondary data from sources like World Bank, The Organisation for Economic Co-operation and Development (OECD), World Trade Organisation (WTO), and International Finance Corporation (IFC) data repositories to identify variables such as risks, costs, trade agreements, regulatory policies, and gross domestic product (GDP) that affect FDI movements. This chapter highlights the process flow that can be beneficial to convert big data sets using statistical tools and computer software such as Statistical Analytics Software (SAS), IBM SPSS Statistics.

Findings: The application of artificial intelligence-based statistical tools on FDI variables can help derive time-series graphs and forecast revenues. The authors found that foreign investors can narrow their prospect search for industry or product to manageable from varied investment opportunities in host countries. Advancements in big data analysis offer cost-effective methods to improve decision-making and resource management for enterprises.

Introduction: China released an action plan in 2015 to foster the growth of big data, demonstrating that big data development has become an essential and inescapable choice for stabilising growth, advancing reform, adapting structures, helping people’s livelihoods, and supporting government modernisation. The rudimentary ecology of the big data sector, combined with a favourable policy climate, creates ideal conditions for developing big data in China. The use of big data, on the other hand, has steadily shifted from a theory to reality, thanks to the explosive increase of data resources and the rise of specialised big data firms. Globally, the insurance industry is undergoing a technological revolution. The internet, mobile networks, social networks, cloud computing, and big data are all examples of digital technologies that progressively influence daily business operations and will usher in a golden era for the insurance industry.

Purpose: This chapter aims to understand the use of big data in the insurance industry innovation and the challenges insurance companies face. This chapter will also offer insight into the big data strategies of insurance companies.

Methodology: This chapter attempts to study literature reviews related to big data and examines the use of big data in the insurance industry. Also, different techniques associated with collecting big data and an assortment of big data sources are analysed in the context of the developing insurance industry.

Findings: This chapter helps us understand how big data innovations are useful for the insurance industry. The present chapter helps us in understanding the challenges faced by insurance companies. This chapter will also offer insight into the big data strategies of insurance companies.

Purpose: This paper aims to reveal the impact of the pandemic Covid-19 on the banking and financial sector. Covid-19 is a pandemic disease that’s impacting all nations. However, its amount varies from one country to another depending on the country’s social and economic infrastructure progress. The whole world is passing through great improbability. Indian economy is also facing equivalent issues from contraction in growth to rising inflation, unemployment and low demand. Covid-19 has impacted all industries worldwide, and the financial service sector is not any exception. Covid-19, which began as a health crisis, has now been appropriated as a financial one.

Methodology: This study intends to showcase various new developments in the banking sector. In the present scenario, banks are focusing on utilising new technological innovations to reinforce their risk management competence. Since the aim is to analyse various latest developments in the banking sector and its impact during Covid-19, the focus is to collect the relevant and supporting material from every possible secondary source. To attain the main aim of this paper, the data are collected using secondary sources, i.e. data from the annual reports of the Reserve Bank of India (RBI), Security Exchange Board of India, Federation of Indian Chambers of Commerce & Industry, Organisation for Economic Co-operation and Development (OECD), the International Monetary Fund (IMF), and the World Bank and various others sources. This is taken care of on the primary basis that the reliable and authentic sources are incorporated in this study. Since the study scope is limited to analysing the new developments in the banking sector due to Covid-19, the maximum literature available to attain the paper’s objective is from 2020 to 2021.

Findings: The banking sector is among the most crucial sectors of the Indian economy, which is accountable for almost every financial activity possibly happening within the country. It acts as a holding hand to the industry involved in credit, transactions, collection, etc. With the disruption of supply chains across the globe, numerous physical business places are closed. Banks are the backbone of the economy. Their stability is critical to continue the system up and to run.

Practical implications: The banking sector aims to supply funding to anyone, say corporate or individuals. The decelerate pace can guide prospective job losses, ground stress in banks’ retail loan books. The banks should design a plan to shield employees and their customers from its spread. It has hit the scope to individuals, small- and medium-sized enterprises, and large corporate. The only obvious thing is that every group has faced an income crunch that threatens economic and financial market permanence.

Significance: The relevance of this study stands on the fact that Covid-19 has begun as a health crisis, quickly extended into a business crisis. This is often not only a health crisis but also depression. The outbreak of Covid-19 has created a huge impact on nations. The nationwide lockdowns have almost faded social and economic life. The global economy was hit hard by the continued coronavirus. The whole world is passing through great uncertainty. As a result, various services sectors, banking sectors, and financial services have suffered through various ups and downs, resulting in economic stress. The uncertain and risky environment has had a severe impact on banks’ asset quality. The coronavirus outburst influenced financial markets and consumer emotions as well.

Introduction: The Indian insurance sector has a large number of insurance companies. More than 20 companies are in the life insurance business, and nearly 35 are non-life insurers – only one public sector company among the life insurers (Life Insurance Corporation (LIC)). However, there are six public sector insurers in the property insurance division. The government policies have recently increased the foreign direct investment (FDI) share from 49% to 74%.

Purpose: The purpose of the study is based on the latest decision by the Government of India (GOI) to increase the FDI in the insurance sector, which was earlier 49% and now increased to 74%. The study will have objectives that impact change in FDI and its effect on customers’ decisions.

Methodology: This chapter is based on secondary information collected from the Insurance Regulatory and Development Authority and Articles from various journals for objectives 1 and 2. Qualitative analysis is done with the use of NVIVO software. There are primary two objectives taken under consideration in this chapter: objective 1: regulatory framework of insurance sector post-FDI change in limits by GOI and objective 2: customer awareness regarding changed limits of FDI in the insurance sector and its various factors. Fifty-four interviews were conducted, out of which a total of 40 responses have been considered for final analysis. An incomplete and unclear answer has been excluded from the study.

Findings: In the study’s findings, it was found that in accordance with the first objective, GOI changes policies according to time to time. Foreign Direct Investment (FDI) in the insurance sector recently increased by GOI Earlier, it increased in the year 2015 and recently this year, it increased by 49% again to 74%. In the second objective findings, the awareness about changes in FDI in the insurance sector respondent’s sentiments is positive and constructive. A maximum of respondents has said that they are aware of the insurance sector and the participation of various foreign international players in the insurance industry.

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.

Purpose: Blockchain is the most significant technological innovation of the generation following the internet. However, most individuals are unaware of how it will affect the insurance business.

Design/methodology/approach: The present study utilises a systematic review methodology to assess the existing literature on blockchain technology in the insurance industry.

Findings: Currently, few insurance companies are researching and using blockchain technology for automated claims, fraud detection, and cash flow tracking. The use of blockchain technology in the insurance business is still in its early stages, and many significant issues remain unsolved. This chapter lays out the discussions regarding the current state of blockchain technology in the insurance business.

Practical implications: Using distributed ledger technology (DLT), all the stakeholders can easily exchange the relevant information on a real-time basis. In particular, blockchain technology will help all insurance companies minimise discrepancies related to fraudulent claims by keeping track of the customer’s history of the customer reducing administrative costs.

Originality/value: It has been observed that very few studies have been conducted in this field. This is a holistic study that focuses on the applications of blockchain technology in various non-life insurance segments.

Purpose: This chapter revisits digital financial inclusion as an international development agenda and discusses everything you need to know about digital financial inclusion.

Methodology: This chapter uses conceptual discourse methodology to explain digital financial inclusion.

Findings: This chapter identifies the definitions of digital financial inclusion, the goal of digital financial inclusion, the components of digital financial inclusion, the types of providers of digital financial services, the instruments for digital financial inclusion, the benefits of digital financial inclusion, the risks of digital financial inclusion, and the regulatory issues associated with digital financial inclusion. It also proposes suggestions on how to make digital financial inclusion work for the good of all. This chapter concludes by offering some implications for policymaking and practice in the digital finance ecosystem.

Purpose: The scope of this research is to conduct a study on the perceived effectiveness of developments in InsurTech, by determining online use integration in the Maltese insurance market.

Methodology: To do this, the authors employed a self-administered questionnaire to which 471 participants responded on a 5-point Likert scale. We subjected the data collected from this questionnaire to statistical analysis, specifically, exploratory factor analysis (EFA) and multiple linear analysis using the Statistical Package for Social Science (SPSS) version 26.

Results: EFA loaded best on five factors of insurance customers’ perceived effectiveness, which make up the effectiveness model (EM), namely ‘Factor 1 – Internal Process Enhancement’, ‘Factor 2 – Cost-Efficiencies’; ‘Factor 3 – Time-Sensitive Conditions’, ‘Factor 4 – The Contemporary Use of Artificial Intelligence and Marketing in Relation to Customer Service’ and ‘Factor 5 – Customer Relations and Application of InsurTech in Communication’. Moreover, multiple linear regression results show that the perceived effectiveness dimension – EM is statistically significantly related to online use in Malta.

Practical implications: Therefore, it can be argued that the Maltese insurance sector is well prepared to meet the obligations and requirements of the European Green Deal. Findings shed light on the preparedness of the Maltese insurance market to accept innovative green proposals to go online with processes.

Purpose: This chapter sets out to lay out and analyse the effectiveness of the General Data Protection Regulation (GDPR), a recently established European Union (EU) regulation, in the local insurance industry.

Methodology: This was done through a systematic literature review to determine what has already been done and then a survey as a primary research tool to gather information. The survey was aimed at clients and employees of insurance entities.

Findings: The general results are that effectiveness can be segmented into different factors and vary regarding the respondents’ confidence. Other findings include that the GDPR has increased costs, and its expectations are unclear. These findings suggest that although the GDPR was influential in the insurance market, some issues about this regulation still exist.

Conclusions: GDPR fulfils its purposes; however, the implementation process of this regulation can be facilitated if better guidelines are issued for entities to follow to understand its expectations better and follow the law and fulfil its purposes most efficiently.

Practical implications: These conclusions imply that the GDPR can be improved in the future. Overall, as a regulation, it is suitable for the different member states of the EU, including small states like Malta.

Purpose: A cyber insurance policy’s purpose is to help in the recovering of a person or corporation following a cyber breach and to compensate for civil suit expenses stemming from first- and third-party responsibility claims.

Methodology: The usage of cybersecurity spending has forecast a variety of security categories using F&S projection methodology. Each of these is suited to the end-user organisations of in-scope security mechanisms, as well as the particular market circumstances. Critical national infrastructure (CNI), immigration control, big events, first responding, executive branch, infrastructure, and transportation security are among the worldwide forecast categories. This segmentation is further subdivided into 16 subsegments, each with its own security forecasting system. F&S protection marketplaces are anticipated using a bottom-up technique for each nation, which adds up to worldwide market penetration. This covers 177 nations spread throughout seven zones.

Findings: The cybersecurity insurer industry was valued at USD 7.36 billion in 2020 and is predicted to be worth USD 27.83 billion by 2026, growing at a compound annual growth rate (CAGR) of 24.30% during the forecast time frame (2021–2026). The expanding use of digitalisation innovations such as the cloud, big data, mobile computing, internet of things (IoT), and artificial intelligence (AI) across more lines of employment and society, as well as improved connectivity, have enhanced the burden of already overburdened information technology (IT) staff.

Practical implications: Accepted the innovative Insurance Data Security Model Law (#668), which necessitates insurance providers and other agencies registered by government insurance agencies to advance, integrate, and establish an information security management system; start investigating any cybersecurity events; and advise the private insurance superintendent of such happenings. Too far, the approach has been embraced by governorates.