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Background: Insurance was discovered many centuries before Christ (BC). In the second and third millennia BC, Chinese and Babylonian traders traded risks. Insurance is now the backbone of the economy, but penetration is low in developing countries. Big data, internet of things (IoT), and InsurTech have recently ushered in the fourth industrial revolution in insurance.

Objective: This study examines the Indian challenges and solutions of using Big Data Analytics (BDA).

methodology: A SLR was used to extract themes/variables related to challenges and solutions in adopting BDA in the Indian insurance sector. Google Scholar was searched for relevant literature using keywords. Inclusion and exclusion criteria were used to filter the studies.

Findings: This study identified several barriers to BDA adoption in the Indian insurance industry. Policymakers could use the suggestions to improve insurance service delivery.

Practical implication: Insurers can understand the challenges, and accordingly, they can adopt the proposed solution in this study to enhance the insurance penetration in India.

Background: Human resource management (HRM) is the tactical method for a business enterprise’s optimistic and systemic administration. This study aims to identify the common and major triggering attributes and the knowledge gap between HRM and an organisation’s employee attrition rate.

Method: The employee Attrition Case Study Dataset used is an anecdotal data set that tries to figure out relevant variables that determine employee behavioural aspects towards attrition. This study investigates why attrition occurs, the major triggering attributes for employee turnover, and how it might be anticipated to employ artificial intelligence (AI) to avert corporate losses.

Results: Employees’ monthly income, age, average monthly hours, distance from home, total working years, years at the company, per cent of salary hike, number of companies worked, stock options level, job role and other factors are taken into consideration. A feature importance extraction framework was devised to investigate the various dormant factors. The findings also show feasible hypotheses that help enhance employee engagement, reinvent the worker dynamic, and higher levels of risk decrease attrition rate.

Implications: Employees’ monthly income, age, average monthly hours, distance from home, etc., are all major variables in employee attrition in the Indian IT business. This research adds to the theory development of behavioural elements in people analytics based on AI.

Purpose: Can we predict employee attrition through employee behavioural patterns advancement using AI tools.

The healthcare sector in India is witnessing phenomenal growth, such that by the year 2022, it will be a market worth trillions of INR. Increase in income levels, awareness regarding personal health, the occurrence of lifestyle diseases, better insurance policies, low-cost healthcare services, and the emergence of newer technologies like telemedicine are driving this sector to new heights. Abundant quantities of healthcare data are being accumulated each day, which is difficult to analyze using traditional statistical and analytical tools, calling for the application of Big Data Analytics in the healthcare sector. Through provision of evidence-based decision-making and actions across healthcare networks, Big Data Analytics equips the sector with the ability to analyze a wide variety of data. Big Data Analytics includes both predictive and descriptive analytics. At present, about half of the healthcare organizations have adopted an analytical approach to decision-making, while a quarter of these firms are experienced in its application. This implies the lack of understanding prevalent in healthcare sector toward the value and the managerial, economic, and strategic impact of Big Data Analytics. In this context, this chapter on “Predictive Analytics in Healthcare” discusses sources, areas of application, possible future areas, advantages and limitations of the application of predictive Big Data Analytics in healthcare.

The awareness of probability was observed in ancient cultures through the discovery of primitive dice games made with animal bones. The history of analytics in the workplace, as it is currently known (defined as predictive analytics), probably started in ancient Roman times, when the concept of insurance was first created. While the previous example showed that analytics for business had been around for some time, it is only relatively recently that there is an increased emphasis on the use of analytics in the modern firm. Credit card firms and retail catalog companies relied on analytics to drive their business models, for most of the latter half of the twentieth century. The use of advanced analytics for business also grew around the Millennium since the widespread use of data warehousing and relational databases on client servers. Moreover, Machine Learning and Artificial Intelligence Techniques, which have been around for many decades, have had very few breakthrough successful applications up until recently when cloud computing and being able to take advantage of the infrastructure of companies, such as Amazon and Google, with their Cloud Services enabled these algorithms to be used to their full extent in firms. This powerful infrastructure availability coupled with BIG DATA is creating breakthrough applications across many business models on a consistent basis. This chapter explores the use of advanced analytics across different business functional areas. It also introduces some breakthrough models, which include Netflix, Pandora, eHarmony, Zillow, and Amazon, and explores how these are not only changing the lives of consumers but also changing the nature of the workplace and creating new issues for firms such as data protection and liabilities for the actions of automated algorithms.

The objective of this chapter is to identify the key characteristics of Global Services businesses that will thrive and achieve success in the future. These factors are integrated into three main pillars, which we refer to as the Triple-Win. The first and most obvious pillar is technology as a tool. The second pillar is the design and sustainability of the business model, without which the previous factor would be merely a cost and not an investment. And last but not the least, there is the purpose which gives meaning to the proposal, focusing on the human being and their environment. The DIDPAGA business model sits at the intersection of these three elements.

Micro, small, and medium enterprises (MSMEs) can play a significant role in achieving sustainable development goals (SDGs) as they have the ability to reduce unemployment. Digitalization helps MSMEs in a number of ways, including lowering transaction costs, quickening access to information, and bettering communication with extended supply chain members. This chapter aims to understand the level of digitalization in MSMEs in an emerging economy such as Bangladesh. MSMEs in Bangladesh account for 25% of the gross domestic product and employ 87% of civilians. This chapter builds on qualitative data from 60 MSMEs from various manufacturing and service sectors such as textile, retail, food delivery, IT companies, etc. The interviews were semi-structured and followed an interview protocol. The length of interviews varied between 40 and 50 minutes. Content analysis was used to analyze the data. Findings suggest that counterintuitively the level of digitization in MSMEs is not low in Bangladesh. Many micro and small enterprises use MS Excel to help them manage customer and product data. Medium Enterprises use Enterprise Resource Planning (ERP) software for planning enterprise-wide resources. Some medium enterprises also use powerful data analytics software such as Oracle, Power BI, Google Analytics, Python, and SPSS. Results also reveal barriers to digitization in MSMEs, which include a lack of employee awareness, training, and motivation of top management. This chapter maps the digitalization levels in MSMEs in Bangladesh and provides implications for SGDs. The chapter also presents policy recommendations for improving the digitalization level in emerging economies.