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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.

This paper aims to compare the emergence and evolution of organizational fields through an analysis of the life insurance industries in two large emerging markets. Using institutional theory as a conceptual framework, we compare the regulatory, cognitive and normative dimensions of the life insurance industry in China and India.

The authors introduce a qualitative variation of the country institutional profile (CIP) that has been traditionally implemented as a quantitative analytical tool used to describe differences in national environments. This newly proposed methodology captures the socially embedded aspects of the phenomenon more completely than commonly employed survey-based methodology.

This analysis leads to a three-dimensional typology of constructs and themes within each national environment. These themes include the importance of regulation and protectionism, the domestic savings culture, family support structures and human capital development within the industry. The authors conclude by comparing these typologies to consider the implications for studying change in organizational fields across contexts.

As the authors reflect on the evolution of organizational fields, they demonstrate how the interplay of historical factors and new global norms results in a negotiated stance between compliance with new norms and allegiance to local interests. In terms of methodological contribution, we show how the socially embedded aspects of the examined phenomenon are explored more completely by the proposed qualitative CIP than through its quantitative variation. This approach and the analysis illustrate a complex interplay of local and global norms within a selected industry that may be missed by other research methods.

The purpose of this paper is to analyse the performance of the Indian non-life (general) insurance sector in terms of efficiency, productivity and returns-to-scale economies. In addition to this, it identifies the determinants of efficiency.

This study employs a two-stage data envelopment analysis (DEA) bootstrap approach to estimate the level and determinants of efficiency. In the first stage, the DEA bootstrap approach is employed to estimate bias-corrected efficiency scores. In the second stage, the truncated bootstrapped regression is used to identify the effect of firm-level characteristics on the efficiency of insurers. Moreover, the bootstrapped Malmquist index is used to examine the productivity growth over the observation period 2005–2016.

The bootstrapped DEA results show that the Indian non-life insurance sector is moderately technical, scale, cost and allocative efficient, and there is a large opportunity for improvement. Moreover, the results reveal that the public insurers are more cost efficient than the private insurers. It is also evident that all the insurers irrespective of size and ownership type are operating under increasing returns to scale. Malmquist index results divulge an improvement in productivity of insurers, which is attributable to the employment of the best available technology. Bootstrapped DEA and bootstrapped Malmquist index results also show that the global financial crisis of 2008 has not severely affected the efficiency and productivity of the Indian non-life insurance sector. The truncated regression results spell that size and reinsurance have a statistically significant negative relationship with efficiency. It also shows a statistically significant positive age–efficiency relationship.

The results hold practical implications for the regulators, policy makers, practitioners and decision makers of the Indian non-life insurance companies.

This study is the first of its kind that comprehensively investigates different types of robust efficiency measures, determinants of efficiency, productivity growth and returns-to-scale economies in the Indian non-life insurance market for an extended time period.

Introduction: The internet of things (IoT) is the emerging technology of interconnected objects that can be termed as ‘things’ used to exchange data, connecting with different devices on the internet. It is the future where connected devices are controlled remotely. The insurance sector is one of the leading industries providing financial protection services to their customers to recover losses. Like others, the insurance industry uses the services very efficiently to solve their customer-centric problems and provide the best services to them. IoT in insurance is enhancing customer services.

Purpose: To determine how the insurance industry utilises the different IoT technologies to provide the best services and solutions to their users. The insurance sector is working on other areas of expertise to offer outstanding facilities to their clientele.

Methodology: We reviewed published material covering five years on IoT and insurance and customer services in the media, newspapers, journal publications, and the web. We determined how the insurance sector adapted the new terminology to contribute its best services to the users.

Findings: We observed that IoT services and technologies benefit the insurance industry and the clientele. This shows excellent results in the growth of the sector and heightened facilities for the consumers.

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.

The purpose of this paper is to benchmark the solvency status of Indian general insurance firms.

The paper collects, compiles and analyses the key financial, operational and business data of eight Indian insurance firms. The authors first decide on initial firm‐specific economic variables and use data of last five years from IRDA Reports and Company Annual Reports. The NAIC IRIS ratios method was used to obtain an initial risk classification. This was used as a proxy of insolvency risk. Linear regression and logit techniques were thereafter applied to estimate the significant factors (direction‐wise and magnitude‐wise) which influence insurer solvency.

The results suggest that the factors that most significantly influence Indian non‐life insurers are lines of business, the firm's market share, the premium growth rate, the underwriting performance and the claims incurred. Further, the factors which have the strongest effect are market share, change in inflation rate, firm size, lines of business and claims incurred.

The sample of Indian general insurers used is limited with regard to the time span. No holdout sample was used and the entire data set was subjected to statistical analysis. These somewhat limit the findings and implications.

The paper provides insurers with easy‐to‐use operational and marketing indicators to benchmark their solvency risk. It will lead to competitive goal setting for continuous improvement. Estimation of appropriate market/economic parameters can be a useful input for regulators. A few suggested indicators are new.

Previous studies of insurance companies have focused on developed economies (USA, Europe) or the Asian Markets (China and Japan). This paper determines a set of marketing, financial and operational variables to predict benchmark financial strength of general insurance firms in India. It incorporates qualitative inputs from practising managers and industry experts before carrying out quantitative modeling and analysis.

Traditional statistical methods to study the financial performance of any industry have many barriers and limitations in terms of the statistical distribution of the financial ratios, and, in particular, it considers only its positive values of it. The purpose of this paper is to estimate the financial performance of 24 Indian life insurance companies for the period from 2013 to 2016 using Grey relational analysis (GRA) proposed by Deng (1982) that accommodates the negative values in the analysis.

Financial performance of 24 Indian life insurance companies for the years from 2013–2014 to 2015–2016 is examined using a total of 14 indicators from capital adequacy ratios, liquidity ratios, operating ratios and profitability ratios (PR). The methodology used is GRA to obtain the Grey grades to rank the performance indicators, where higher relational grade shows better financial performance, and a lower score depicts the scope for improving the performance.

The results rank the insurance companies according to their financial performance in which Shriram insurance stands first with higher relational grade score, followed by the companies like IDBI Insurance, Sahara Insurance and Life Insurance Corporation of India. The main finding is that PR which have negative values are playing a crucial role in determining the financial performance of Indian life insurance companies.

This study has far-reaching practical implications in twofold: first, for the Indian life insurance industry, they have to concentrate more on PR for better financial health and, second, for any financial performance analysis, ignoring negative value ratios produce biased inference and GRA can be used for better inference.

This study is the first attempt to evaluate the financial performance of Indian life insurance using the GRA methodology. The advantage of GRA is that there is no restrictions on the statistical distribution of the data and it also accommodates the negative values, whereas all the other traditional methods insist on the statistical distribution of data, and, more importantly, they cannot handle negative values in the performance analysis.

The purpose of this paper is to analyse the performance of the Indian non-life (general) insurance sector in terms of total factor productivity (TFP) over the period 2005–2016.

This study utilises Färe‒Primont index (FPI) to access the change in TFP and its components: technical change, technical efficiency and mix and scale efficiency over the observation period. Moreover, it employs the Mann–Whitney U-test to scrutinise the difference between the public and the private insurers in terms of growth in productivity.

The results reveal that the insurance sector possesses a very low level of TFP. Also, the results divulge an improvement of 11.98 per cent in TFP of the insurance sector at an annual average rate of 12.41 per cent over the observation period. The growth in productivity is mainly attributable to the improvement of 10.81 per cent in the scale‒mix efficiency. The progress in scale‒mix efficiency is mainly the result of improvements in residual scale and residual mix efficiency. The results also show that the privately owned insurers have experienced a high productivity growth rate than the state-owned insurers.

The results hold practical implications for the regulators, policymakers and decision makers of the Indian non-life insurance companies.

This study is the first of its kind to use FPI, which satisfies all economically relevant axioms and tests defined by the index number theory to comprehensively access the change in TFP of the Indian non-life insurance sector.

Introduction: Artificial intelligence (AI), the engineering of brilliant machinery, performs intelligent human intelligence tasks, such as learning and problem-solving. Insurance is a financial protection policy either for individuals or entities to reimburse losses from the insured company. The role of AI in insurance always helps enhance customer services and understand their behaviour.

Purpose: This chapter aims to determine the role of AI in the insurance industry in India. The insurance industry is expanding very fast, and to further increase its horizons, the part of the technology of AI is essential. However, this sector has initiated using AI technology and is expanding its scope to benefit the customers.

Methodology: The authors selected research papers of the last five years to review and determine how the technology changed during the period and how an increase in AI benefits the industry and facilitates delivering the best services, and understanding the customer’s needs and behaviour.

Findings: It has been found that the industry is moving very fast and adopting the AI technology methods to enhance customer services, betterment for growing India, and serve insurance services to the nation efficiently.

Students will have the opportunity to learn about differentiation strategy, business plans, strengths, weaknesses, opportunities and threats analysis. The primary objective is to allow students to evaluate Abhishek Singh’s decisions. After working through the case and assignment questions, students will be able to: understand the competitive landscapes in the Indian insurance broking market. Differentiate between selling and marketing. Understand the importance and application of differentiation strategy in the Indian insurance broking market. Emphasize the importance of value proposition in developing a competitive advantage for insurance broking organization. Critically analyze the internal strength and weaknesses of an insurance broking company.

RHIBPL, one of India’s largest insurance broking companies, operating for more than 12 years and has had a strong presence in eight major cities of India. RHIBPL had been known for life insurance, non-life insurance like cars, two-wheelers, Mediclaim and health. RHIBPL’s unique selling proposition was the best fit for each product and service after understanding the customer’s need. On October 16, 2020, Abhishek Singh, CDO at RHIBPL, was preparing for an upcoming management meeting on the company’s vision “to be the largest insurance provider in the country and to reach US$ 140 million by 2025,” which was communicated by the Ajay Bansal, founder and chief executive officer of RHIBPL. Singh had to prepare an action plan to achieve the RHIBPL’s vision. He had to develop and implement a differentiation strategy to achieve the RHIBPL’s vision from the available resources. How can he create a competitive advantage for RHIBPL in the highly competitive insurance broking market?

The case delineates the challenges facing an insurance broker company to achieve its new corporate vision. This case can be discussed in undergraduate and postgraduate courses (at the introductory level) in the business management discipline to understand the importance and application of differentiation strategy and competitive advantage. This case can also be significant for insurance-related courses for postgraduates and executives working in the insurance industry. The case can be useful for the courses on services marketing, strategic management, strategic marketing management and marketing management.

Teaching notes are available for educators only.

CSS 8: Marketing.