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This study aims to investigate into the dynamic connection between ESG scores and the volatility term structure for Indian companies listed BSE. The study divides the BSE-100 listed companies into two panels based on their median ESG scores in 2022, creating high and low ESG scoring groups to capture volatility structure.

The study employs time-varying symmetric and asymmetric GARCH models and followed by continuous Wavelet to capture volatility structure and explore comparative resilience behavior.

The study found similar volatility patterns regardless of ESG scores, nudging doubt on the direct impact of ESG on volatility. Additionally, both high- and low-ESG-scored companies displayed high vulnerabilities during the pandemic, raising questions about the effectiveness of ESG frameworks in capturing risks. Finally, by examining the resilience behavior of ESG-scored companies during the pandemic, our study contributes to the evolving understanding of the intersection between ESG performance and crisis response.

The study carries vital implications for investors and policymakers. It highlights the urgent need to strengthen the ESG framework and scores to shield investors from short- and long-term volatilities and economic vulnerabilities.

To the best of the authors’ knowledge, this is the first study investigating the Indian market by examining the volatility structure and resilience behavior of high- and low-ESG-scored companies during the pandemic.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-02-2024-0113

Purpose: The article discusses the use of risk assessment models in the health insurance sector, aiming to enhance the quality of care provided to individuals by leveraging technologies such as cloud-based platforms and remote medical sensors.

Methodology: The article reviews various papers on the topic, examining studies ranging from the impact of co-insurance in Vietnam to the architecture of e-health systems. It also discusses different models for connecting body sensors to cloud-based systems, emphasizing the importance of algorithm and shared data models (SDMs) for the health and insurance industries.

Findings: Findings highlight the increasing trend of individuals, families, groups, corporate houses and governments leveraging health insurance policies to mitigate risks, even in areas lacking basic primary health facilities. The article underscores the significance of technologies like data mining and machine learning in adding value to the insurance sector.

Practical Implications: The article presents an architecture for health risk analysis monitoring, consisting of multiple layers to ensure effective risk management.

Originality: The interdisciplinary study of merging designs for healthcare and insurance is depicted as an ongoing process aimed at improving overall care quality. The article explores innovative approaches and platforms, showcasing the originality in addressing challenges within the health insurance sector.

The present study delves into the incorporation of the Metaverse in the insurance industry, with an emphasis on augmenting consumer experiences via virtual interaction.

We explored secondary data sources on metaverse in the insurance industry and went through a thorough analysis of the literature and case studies, showcasing best practices and inspirational tales from top insurers.

The study found that insurers are ready to capitalize on the convergence of the digital and physical realms embracing the “phygital” environment. By making investments in the Metaverse, insurance companies can reduce new risks, enhance customer satisfaction, and streamline operations. But it also brings up issues with user privacy and security. The efficient application of metaverse solutions may be hampered by problematic areas including malware, cyberbullying, identity theft, cyber hacking, and cyberattacks. User privacy and data security are complicated issues that need the cooperation and accountability of several stakeholders.

Insurers may revolutionize traditional insurance interactions by utilizing cutting-edge technology like virtual reality (VR) and augmented reality (AR) to create personalized, interactive, and instructive experiences for their consumers. For insurers, the Metaverse has ushered in a new era of digital transformation by giving them a powerful arsenal of technological resources to engage with customers and develop creative business plans.

The study on the Metaverse in insurance—a virtual customer experience is an original contribution based on literature and case studies on virtual experiences. The ultimate goal of this study is to offer insights into the optimization of virtual client experiences in the digital age by examining the possible advantages, difficulties, and ramifications of applying Metaverse technologies in the insurance industry.

Purpose: The book chapter seeks to investigate the numerous green finance measures already taken by various banks in the direction of Bangladesh’s sustainable development. The chapter also demonstrates the extent to which banks in the nation will use green finance between 2019 and 2022.

Need for the study: This study indicated that practically all banks participated in green finance initiatives to promote sustainable economic growth. Private commercial banks (PCBs) and foreign commercial banks (FCBs) have taken more measures than the other banks, although their efforts still fall short. Therefore, it will be better for the environment and sustainable growth if the financial industry adopts more green finance projects.

Methodology: The study mostly relied on secondary data and information gathered from Bangladesh Bank annual reports, numerous studies on green financing, and the websites of several institutions. Microsoft Excel has been used to examine the data. Data have been presented using tables, graphs, and charts.

Findings: Green financing is now the demand of the time because the whole world, especially Bangladesh, is facing adverse impacts from global warming and climate change. So environmental issues have become the main concern of the government, Bangladesh Bank, stakeholders, and society, and they wish to know about the initiatives towards green financing taken by the various banks to reduce the impact of global warming.

Implications: Policymakers, governments, private and public organisations, banking industries, and their stakeholders will benefit from the book chapter.

Introduction: Data play a very significant role in solving the problem faced at micro and macro levels. Financial inclusion and insurance penetration have been a major problem of developing economies. These two economic indicators can be strengthened with the emergence of data alchemy.

Purpose: The present research study is conducted with the objective of measuring the impact of technological infrastructure, data alchemist techniques, and regulatory environment on insurance penetration and financial inclusion.

Methodology: To meet the research objectives, data were collected through a random sampling technique from the insurance agents in Mumbai, which can be considered the heart of insurance in India. On the data collected, the partial least squares (PLS) algorithm was applied using smart PLS software. PLS is a statistical method used for predictive modeling and analysis of complex data with multiple variables.

Findings: The final results revealed a significant relationship between data alchemy techniques and financial inclusion. Also, a significant impact on the financial inclusion level of the regulatory environment is also recorded. However, in a developing country like India, currently data alchemy techniques are not significantly impacting insurance penetration.

In the era of data-driven decision-making, ethical considerations are central. This chapter examines the complex ethical landscape of data analytics, addressing challenges such as privacy and bias. It outlines guiding principles and best practices, aiming to foster responsible data stewardship and uphold integrity in an increasingly interconnected world.

Purpose: The main aim of this study is to explore the ethical dimensions of data analytics, addressing key challenges, principles, and best practices in various sectors of society.

Methodology: The study explores the ethical dimensions of data analytics using multi-faceted approaches. Since, new dimensions have to be explored, the study chosen is exploratory.

Findings: This study explores the ethical dimensions of data analytics. Addressing these ethical dimensions requires a multi-faceted approach involving technical, organizational, and regulatory measures. By proactively identifying and addressing ethical challenges, organizations can foster trust, accountability, and responsible innovation in data analytics. Additionally, ongoing dialog and collaboration among stakeholders are essential to navigating the complex ethical landscape of data analytics effectively.

Implications: This study on ethical considerations in data analytics serves as a valuable resource for organizations, policymakers, educators, and society at large. By integrating ethical principles into data analytics practices, stakeholders can harness the transformative potential of data while upholding ethical standards and safeguarding individual rights and societal well-being.

Employee performance and job satisfaction are crucial factors that influence organizational success, particularly in the insurance industry. The advent of data-driven approaches has led to the emergence of Employee-Performance Data Management (EPDM) practices, which play a pivotal role in shaping employee outcomes. This study, with its clear focus on the impact of EPDM on job satisfaction within the insurance sector, aims to provide an understanding of this relationship, employing a positivist perspective grounded in existing theories.

The primary objective of this research is to investigate the influence of EPDM variables, such as data integration, technology integration, and ethical considerations, on job satisfaction among employees in the insurance industry.

We adopted a causal-comparative research design. This design allowed us to discern the cause-and-effect relationships among the variables under study. We collected data through structured questionnaires, ensuring a diverse sample of 415 employees across various job roles within the insurance sector. Our analytical framework encompassed multiple regression analysis, f-tests, t-tests, and calculations of means and standard deviations, all of which were used to rigorously assess the data.

Our study's findings have significant implications for the insurance industry. We found that aspects of EPDM variables, including data integration, technology integration, and ethical consideration, have a profound impact on job satisfaction. These results underscore the critical role of effective data management in enhancing employee outcomes. They also highlight the need for insurance companies to invest in robust data management strategies, potentially leading to improved job satisfaction and enhanced organizational performance.

The study delves into the role played by cutting-edge data analytics, machine learning, and innovative technologies in reshaping traditional insurance practices. The primary goal of this review is to juxtapose findings from the literature sources, enabling a comprehensive analysis of the current state of implementation.

Systematic narrative review methodology has been applied to the present study. Scopus database has been used for the manuscripts ranging from year 2020 to 2024 considering the 5-year rule. 74 manuscripts were reviewed to navigate the landscape of data-driven revolution, unlocking the potential to elevate insurance operations to new heights. Two research questions about the impact of data alchemy on operational efficiency and insights and its contribution to reshaping the future landscape of insurance practices have been answered.

This approach captured the interplay between the theoretical potential for insurance and the practical realities of implementation of advanced practices, drawing upon the collective expertise within the field. By doing so, the article discerned the trajectory of the insurance sector concerning the advanced data alchemy observed in the industry.

The current research contributes to the broader area of data alchemy in the insurance industry. The transformative power of big data analytics lies in its capacity to turn vast and diverse datasets into valuable insights, driving innovation, informed decision-making, and improved business outcomes across various sectors. Notably, the research extends the body of literature exploring the impact of data alchemy on operational efficiency and insights, area where limited studies have been conducted.