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Introduction: The insurance industry has unprecedented growth, and the demand for insurance has outgrown in the recent past due to the prevailing pandemic. The companies have a large base of the data set at their disposal, and companies must appropriately handle these data to come out with valuable solutions. Data mining enables insurance companies to gain an insightful approach to map strategies and gain competitive advantage, thus strengthening the profits that will allow them to identify the effectiveness of back-propagation neural network (BPNN) and support vector machines (SVMs) for the companies considered under study. Data mining techniques are the data-driven extraction techniques of information from large data repositories, thus discovering useful patterns from the voluminous data (Weiss & Indurkya, 1998).

Purpose: The present study is performed to investigate the comparative performance of BPNNs and SVMs for the selected Indian insurance companies.

Methodology: The study is conducted by extracting daily data of Indian insurance companies listed on the CNX 500. The data were then transformed into technical indicators for predictive model building using BPNN and SVMs. The daily data of the selected insurance companies for four years, that is, 1 April 2017 to 21 March 2021, were used for this. The data were further transformed into 90 data sets for different periods by categorising them into biannual, annual, and two-year collective data sets. Additionally, the comparison was made for the models generated with the help of BPNNs and SVMs for the six Indian insurance companies selected under this study.

Findings: The findings of the study exhibited that the predictive performance of the BPNN and SVM models are significantly different from each other for SBI data, General Insurance Corporation of India (GICRE) data, HDFC data, New India Assurance Company Ltd. (NIACL) data, and ICICIPRULI data at a 5% level of significance.

Conceptualizing development in terms of risk management has become a prominent feature of mainstream development discourse. This has led to a convergence between the rubrics of financial inclusion and risk management whereby improved access for poor households to private sector credit, insurance and savings products is represented as a necessary step toward building “resilience.” This convergence, however, is notable for a shallow understanding of the production and distribution of risks. By naturalizing risk as an inevitable product of complex systems, the approach fails to interrogate how risk is produced and displaced unevenly between social groups. Ignoring the structural and relational dimensions of risk production leads to an overly technical approach to risk management that is willfully blind to the intersection of risk and social power. A case study of the promotion of index-based livestock insurance in Mongolia – held as a model for innovative risk management via financial inclusion – is used to indicate the tensions and contradictions of this projected synthesis of development and risk management.

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.

Introduction: Blockchain is gaining attention in various industries and sectors. It is described as an emergent technology with immense possibilities similar to how the internet has revolutionised how businesses are currently carried out. Still, various sectors have either not adopted or are in a very nascent stage to adopt blockchain technology in their operations. The current research examines how blockchain can be used in the insurance sector. This industry was chosen as it is extremely relevant in today’s world and directly bears its economy.

Purpose: To determine the current and future path in which the insurance industry is moving about blockchain technology adoption and find synergy between blockchain technology and the insurance business.

Need for study: The insurance industry is highly relevant in today’s world and directly bears the country’s economy. Additionally, blockchain is an emergent technology with immense possibilities similar to how the internet has revolutionised how businesses are done. The current research looks at how blockchain can be used in the insurance business.

Methodology: A systematic literature review was conducted in this study by reviewing literature related to blockchain technology and the insurance sector. Science direct was used as a source of information. For this study, the literature review approach was chosen since it allows us to trace the growth of the subject matter and identify the patterns that have formed through time.

Findings: The study found that the insurance sector has recognised the latent benefits of blockchain technology and has begun to develop its usage in selected cases such as fraud prevention and risk assessment.

Practical implications: The current study can be referred to by academicians, marketers, industry people, and policymakers. The study encourages companies and academicians to further investigate the usage of blockchain in insurance.

Economic security is one of the crucial dimensions of the welfare state. High-income individuals are able to purchase private insurance, but a large portion of the individuals remains uninsured. The authors have tried to rationalize the problem of the study over the reason why people remain uninsured. Hence, the purpose of the study is to identify an insurance model that can cover the risk of the heterogeneous segments. The study is qualitative in nature and applies a fuzzy analytic hierarchy process (FAHP). Based on seven criteria, process is applied to arrive at an alternative model among basic models of insurance, namely, conventional private insurance, mutual, and social insurance. Since social insurance has emerged with the highest score of 41% in the study, it is implied that social insurance works best in a situation where the market is full of private information and moral hazard. The findings reaffirm that government intervention is required in an insurance market to provide coverage to both covariate and idiosyncratic risks. The findings are especially relevant in the context of emerging markets where a sizeable poor population goes uninsured. The study contributes to the literature by proposing alternative insurance to address the problem of insuring the voluntarily uninsured.

Introduction: The Internet has tremendously transformed the computer and networking world. Information reaches our fingertips and adds data to our repository within a second. Big data was initially defined as three Vs, where data come with greater variety, increasing volumes and extra velocity. Big data is a collection of structured, unstructured and semi-structured data gathered from different sources and applications. It has become the most powerful buzzword in almost all the business sectors. The real success of any industry can be counted based on how the big data is analysed, potential knowledge is discovered and productive business decisions are made. New technologies such as artificial intelligence and machine learning have added more efficiency to storing and analysing data. This big data analytics (BDA) becomes more valuable to those companies, focusing on getting insight into customer behaviour, trends and patterns. This popularity of big data has inspired insurance companies to utilise big data at their core systems and advance the financial operations, improve customer service, construct a personalised environment and take all possible measures to increase revenue and profits.

Purpose: This study aims to recognise what big data stands for in the insurance sector and how the application of BDA has opened the door for new and innovative changes in the insurance industry.

Methodology: This study describes the field of BDA in the insurance sector, discusses the benefits, outlines tools, architectural framework, the method, describes applications in general and specific and briefly discusses the opportunities and challenges.

Findings: The study concludes that BDA in insurance is evolving into a promising field for providing insight from very large data sets and improving outcomes while reducing costs. Its potential is great; however, there remain challenges to overcome.

This chapter opens the second part of the Volume, focusing on the small farms' role and dynamics within the evolving food system. Assessing small farmers' actual and potential contribution to the change towards a sustainable food and nutrition security requires a deep understanding of their strategic decision-making processes. These processes take place in a context highly conditioned by internal and external conditions, including the complex relations between farm and household, which are mapped and described. Building on an adaptation of Porter's model (Porter, 1990), the chapter investigates how farmers, given those conditions, define their strategies (in particular their innovation strategies) aimed at economic and financial sustainability through a multidisciplinary analysis of scientific literature. Internal conditions are identified in the light of the Agricultural Household Model (Singh & Subramanian, 1986) which emphasizes how family farming strategies aim at combining business-related objectives, and family welfare. Then, a comprehensive set of external conditions is identified and then grouped within eight categories: ‘Factors’, ‘Demand’, ‘Finance and Risk’, ‘Regulation and Policy’, ‘Technological’, ‘Ecological’, ‘Socio-institutional’ and ‘Socio-demographic’. Similarly, six types of strategies are identified: ‘Agro-industrial competitiveness’, ‘Blurring farm borders’, ‘Rural development’, ‘Risk management’, ‘Political support’ and ‘Coping with farming decline’.