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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 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.

The Government of India’s National Policy of Education 2020 stipulates that in the following five years all stand-alone teacher education colleges will be required to convert to multidisciplinary higher education institutions. This calls for a complete overhaul of the country’s vast, diverse, and age-old system of teacher preparation. Evidence-based policy implementation is thus the need of the hour. This chapter attempts to aid the process by presenting insights from a comparative education research on pre-service teacher education (PSTE) of secondary school teachers at stand-alone teacher education institutions (TEIs) in the Indian city of Mumbai and university-based teacher education in the Chinese city of Hong Kong. Documentary sources, field visits, and 57 interviews form the basis of the findings. The dimensions for comparison include academic freedom and autonomy; pathways to PSTE; linkages of teacher education providers; and role and working conditions of teacher educators. The chapter deduces the core differences in teacher education at stand-alone TEIs vis-à-vis that at a university and draws out implications of shutting down the former. It concludes by laying down a road map for the effective universitization of teacher education in India that will result in genuinely improving teacher quality.