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Purpose: Cryptocurrency technology has improved fast in the social economy and growth. Because cryptocurrency has many good qualities, it is initially employed for Bitcoin transactions.

Methodology: With the advent of Bitcoin, the link between distributed ledger technology (DLT) and the banking market has become stronger and more integrated. As more banking institutions understood the relevance of DLT, they began experimenting with using it in financial activities, such as R3CEV, Hyperledger, and Qiwi.

Findings: Many commercial organisations are beginning to experiment with DLT to reduce transactional costs and boost operational effectiveness, particularly in financial notes, cross-border payments, and asset-backed financing.

Practical Implications: DLT has many potential applications in banking domains in the upcoming years.

This research focuses on the challenges of establishing a better medical system that can detect and diagnose diseases earlier. Using such cutting-edge health systems, healthcare practitioners may quickly and effectively manage patients’ medical issues by providing the appropriate data at the right time about the right people. The advancement of technology has increased the usefulness of devices that routinely analyse health measurements or monitoring time-sensitive health-related data. Medical professionals and patients alike are downloading health-related mobile apps to better track and manage their health. The research evidences how Internet of Things (IoT) technology may be used to support health care.

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.

It is difficult to argue against the fact that research has focussed on artificial intelligence (AI) and robotisation over the past few decades. Additionally, during the past several years, it has taken off and is now extensively used in numerous businesses across various industries. Most of the time, AI has been associated with some industrial sector process automation. Still, recently, the authors have noticed more positive technology uses, especially in the financial services industry. Due to several factors, the financial sector needs to adopt AI and recognise its potential. The industry has historically been concerned about unpredictability, legislation, stronger cybersecurity, technological limitations and disruption of established lucrative operations.

Never before has there been more discussion about AI due to the advantages it provides to businesses that are providing financial services. That may explain why this change is referred to as the fourth industrial revolution. Both positively and negatively, it is quite disruptive. The effectiveness, accuracy and cost-effectiveness of solutions greatly increase. However, immense power also entails great responsibility.

Precautions and security are more crucial than ever for businesses since the financial sector is changing significantly and quickly. The various benefits and drawbacks of this technology are yet unknown to humans. Although AI was first shown to us in the 1950s, it has recently gained new prominence as processing power, and the available quantity of data has increased dramatically.

Purpose: The primary objective of this investigation is to determine the importance of big data, machine learning, and systems integration in the creation, production, and promotion of the corporation’s life insurance products marketed in India overall designated insurance carriers. It is also necessary to investigate the function of these instruments in the sectors financial designed and operated managing approaches.

Methodology: The approach used for this analysis is mainly connected to evolutionary and exploratory research. Secondary information is used to obtain the necessary data for the study topic. Secondary data included scientific papers and videos supplied by specialists in diverse domains.

Findings: In this chapter, the authors explain the financial function of large data sets, computer sciences, and content marketing modelling and simulation in the designing, developing, and deploying financial products. The researcher investigated the sale of life insurance plans in India. Insurance Governing Planning Commission is a controlling organisation from the Government of India that oversees all registered insurance businesses in India. Insurance Regulatory and Development Authority (IRDAI) regulates a total of 60 businesses. Thirty-four are in the commercial banking industry, 24 are in the life insurance industry, and 2 are more significant than the average total cost.

Practical implication: Data analytics approaches in financial technological processes and private insurers are helping them increase their business turnovers, collections, and revenue. Similarly, big analysis of data is becoming increasingly important in corporate finance in the life insurance industry, particularly in improving operations, as well as attempting to address numerous problems such as how to optimise marketing strategies and how to enhance customer experience, which has resulted in the most significant goal of improving operational efficiency in the financial industry.

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.