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Introduction: Foreign direct investment (FDI) is a deciding factor in the insurance industry’s growth in any nation. Besides, similar socioeconomic conditions, some countries tend to attract more FDI inflows. This chapter focuses on exploring the FDI in the insurance industry in Brazil, Russia, India, China, and South Africa (BRICS).

Purpose: The chapter aims to explore the current situation of FDI in the insurance industry in BRICS member nations and uncover the factors that have led to higher foreign investments in some countries.

Methodology: Using descriptive and comparative approaches, this chapter explains the FDI scenario in the insurance sector of BRICS nations.

Findings: Based on a comparative analysis, the authors observed that deregulation, increased foreign engagement, and adoption of innovative technology and distribution methods are some avenues that could be worked upon to improve FDIs in the Indian insurance sector.

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.

This chapter explores the influence of the banking and insurance sectors on the economic growth of Czechia, a nation with unique financial dynamics ideal for this study. Our aim is to ascertain the contribution of these financial institutions to economic growth, addressing the divergence in empirical findings that have marked this research area for decades. We scrutinise the impact of various factors, including sectoral development and the efficiency and stability of these institutions, all within the Czech context. Utilising the Granger causality test, we assess the role of several indicators related to the development of the banking and insurance sectors. Our findings reveal that in Czechia, the evolution and operational efficiency of these financial institutions significantly drive economic growth. This study provides an in-depth understanding of the role these sectors play in the Czech economic landscape, affirming their crucial contribution to the nation's economic prosperity.

Purpose: In addition to the liberalisation policy, big data has revolutionised the level of awareness among customers about the quality and prices of insurance products. The rationale behind this study is to underline the issues in managing product portfolios in a disruptive environment, where a sudden and unexpected situation like COVID-19 pandemic is going to challenge the traditional models and insurance covers of organisations as well as individuals.

Methodology: The study is based on secondary data. The scope of the study will only be confined to the top two general insurance companies in India based on year of registration and market share to compare their product portfolios during pre- and post-liberalisation periods ranging from 1985–1986 to 2000–2001 and 2001–2002 to 2018–2019, respectively.

Findings: There is a lack of a balanced product portfolio for fulfilling the varying needs of customers. The insurance companies needed to set up different portfolios and should provide separate covers for natural catastrophes such as floods, earthquakes, landslides, tsunami, and the occurrence of new pandemics like COVID-19.

Significance: The study highlights that the outbreak of COVID-19 and similar pandemics or global emergencies need special preparation from the insurance sector.

Introduction: The insurance sector is playing a crucial role in the sustainable growth of the Indian economy. But in India, this sector loses crores of rupees every year due to the increasing fraud cases. With the increase in insurance customers, insurance companies need to efficiently equip themselves with a robust system to handle claims fraud. Detection of insurance fraud is a pretty challenging problem. Nowadays, machine learning (ML) and artificial intelligence (AI) are the strategic choices of many leading organisations that want to proceed in a new digital arena.

Purpose: This chapter’s main objective is to highlight the fundamental market forces driving the adoption of AI and ML and showcase the traditional and modern methods to predict insurance claims fraud intelligently.

Methodology: Various research papers have been reviewed, and ML methods have been discussed, which are all being used to predict insurance fraud claims. This chapter also highlights various driving forces influencing the adoption of ML.

Findings: This study highlights the introduction of blockchain technology in fraud detection and in combatting insurance fraud. Literature indicates that the quantity and quality of data significantly impact predictive accuracy. ML models are beneficial to identify the majority of fraudulent cases with reasonable precision. Insurance companies should explore the benefits of experienced resource persons from the same domain and develop unique business ideas/rules.

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: This chapter is based on risk management of the insurance sector with reinsurance as its linchpin. Such is the importance of the insurance sector that its risk management must be considered.

Need for the study: Risk management of various sectors is gaining much attention. The insurance sector, known to manage the risk of multiple sectors, also requires its own chance to be controlled with the same or even more intensity. Considering the importance of reinsurance coupled with the dependency of primary insurers on reinsurers and the absence of research on reinsurers, the need to conduct a comprehensive study on the topic is felt.

Methodology: It will be a conceptual chapter based on the rigorous literature on the topic integrated with the researcher’s insights to bring forth the framework of reinsurers for the readers.

Findings: It is found that insurers can themselves become the victims of the financial crisis in case they insure risks that surpass their economic boundaries. Not only this, the failure of insurance companies can have a ripple effect on the country’s economy. Therefore, insurers must possess financial resilience; to remain so, they need to have prudent management of the risk they are undertaking.

Practical implications: The study covers a relatively less researched area of reinsurance and hence has a vast scope of research in the future. The study would be helpful to stakeholders like regulators and primary insurers. It will unveil the paradigm of reinsurance and enlighten the stakeholders on how to use it effectively.

Introduction: The insurance sector provides security to society by pooling resources to manage risks. Insurers’ improved ability to analyse risks by examining vast amounts of granular data has considerably refined this technique. Compiling and analysing the fine data sets is now transformed into the ‘Big Data’ technique. The introduction of big data analytics (BDA) is transforming the insurance industry and the role data plays in insurance.

Purpose: This chapter will attempt to examine the applications and role of big data in the insurance sector and how big data affects the different insurance segments like health insurance, property and casualty, and travel insurance. This chapter will also describe the disruptive impact of big data on the insurance market.

Methodology: Systematic research is carried out by analysing case studies and literature studies, emphasising how BDA is revolutionary for the insurance market. For this purpose, various articles and studies on BDA in the insurance market are selected and studied.

Findings: The execution of big data is continuously increasing in the insurance sector. The performance of big data in the insurance market results in cost reduction, better access to insurance services, and more fraud detection that benefits the customers and stakeholders. Therefore, big data has revolutionised the insurance market and assisted insurers in targeting customers more precisely.

Purpose: This chapter aims to review the research literature on the insurance industry and map the emerging research trends in this field through a bibliometric analysis and network visualisation exercise.

Design/methodology/approach: The research literature gathered from the Web of Science (WoS) databases was applied to bibliometric analysis in this article. With the help of Biblioshiny, this research was utilised to identify documents, most prolific institutions, countries, resource titles, and WoS categories in the insurance industry. In addition, bibliometric mapping was used to identify national and institutional collaboration networks.

Findings: The author discovered that the literature had increased drastically in the academic discourse during the last two decades. According to the bibliometric data, developed countries such as the United States and the United Kingdom reign research in this sector. The research highlights the most prominent studies and writers in the insurance field and the evolution of the domain from its inception to the contemporary. It also highlights theoretical disagreements and contradictions between theoretical conceptualisation and empirical measures by presenting the significant concerns in the literature.

Originality/value: This chapter delivers the first comprehensive bibliometric analysis of the insurance sector’s literature production in connection to emerging technology, which will aid researchers and practitioners in better understanding the relationships between themes and outsiders to understand the domain area better. The author makes recommendations for future perspectives study directions and highlights the critical conceptual framework that can build future research. Overall, this research contributes to a better understanding of the insurance industry and offers new perspectives.