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The U.S. 2002 Farm Bill provides sizeable direct and indirect subsidies to U.S. farmers, which has created increased competition in markets where the United States and Canada compete. Target prices were reintroduced and the overall level of U.S. Government support was increased. Canadian farmers will find it more difficult to compete in grains, oilseeds, and pulses. Government support in Canada for these crops is significantly below U.S. support. Canada and the United States have a significant two-way trade in agricultural products, including beef and pork. The outbreak of Mad Cow Disease in Canada in 2003 clearly illustrates the need for cooperation between the two countries.

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.

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.

Climate change is projected to bring a range of changes in temperature, precipitation patterns, and sea level. As a result, widespread occurrence of floods, cyclones, droughts, cold and heat waves, etc. are projected with uneven distribution in time and spatial scales (Rosenzweig et al., 2007). These changes can manifest in the form of long-term slow changes in the mean state of the climate and sudden changes in the extremes of the climate (Carter et al., 2007). The sudden severe changes can have high impacts with widespread devastation, severely impacting years of developmental efforts in many vulnerable countries.

Introduction: The insurance sector is playing a crucial role in the sustainable growth of the Indian economy. But in India, this sector loses crores of rupees every year due to the increasing fraud cases. With the increase in insurance customers, insurance companies need to efficiently equip themselves with a robust system to handle claims fraud. Detection of insurance fraud is a pretty challenging problem. Nowadays, machine learning (ML) and artificial intelligence (AI) are the strategic choices of many leading organisations that want to proceed in a new digital arena.

Purpose: This chapter’s main objective is to highlight the fundamental market forces driving the adoption of AI and ML and showcase the traditional and modern methods to predict insurance claims fraud intelligently.

Methodology: Various research papers have been reviewed, and ML methods have been discussed, which are all being used to predict insurance fraud claims. This chapter also highlights various driving forces influencing the adoption of ML.

Findings: This study highlights the introduction of blockchain technology in fraud detection and in combatting insurance fraud. Literature indicates that the quantity and quality of data significantly impact predictive accuracy. ML models are beneficial to identify the majority of fraudulent cases with reasonable precision. Insurance companies should explore the benefits of experienced resource persons from the same domain and develop unique business ideas/rules.

Introduction: The insurance industry is one of the lucrative sectors of the economy. However, it is volatile because of the large chunk of data generated by the transactions taking place daily. However, every bit of it is responsible for creating market trends for stock investors to predict the returns. The specialised data mining techniques act as a solution for decision-making, reducing uncertainty in decision-making.

Purpose: There are limited studies that have examined the efficiency and effectiveness of data mining techniques across the companies in the insurance industry to date. To enable the companies to take exact benefit of data mining techniques in insurance, the present study will focus on investigating the efficiency of artificial neural network (ANN) and support vector machine SVM across insurance companies of CNX 500.

Method: For predictive models, various technical indicators were considered independent variables, and change in return, i.e. increase and decrease, was deemed a dependent variable. The indicators were transformed from daily raw data of insurance company’s stock values spanning four years. We formed 90 data sets of varied periods for building the model – specifically six months, one year, two years, and four years for selected six insurance companies.

Findings: The study’s findings revealed that ANN performed best for the ICICIPRULI data model in terms of hit ratio. Whereas the performance of SVM was observed to be the best for the ICICIGI data model. In the case of pairwise comparison among the six selected Indian insurance companies from CNX 500, the extracted data evaluated and concluded that there were eight significantly different pairs based on hit ratio in the case of ANN models and nine significantly different pairs based on hit ratio for SVM models.

Introduction: As the human population grows, consumer demand for digital services tailored to their specific needs also increases. To improve the financial performance of farms and meet the need for food of a growing population, farmers and agribusinesses have started incorporating distributed ledger technology into agricultural and farm management software. These developments in the agriculture sector may lead to realising sustainable development goals.

Purpose: Several researchers have done studies to explore the features and benefits of blockchain technology in the field of agriculture. There is a need to analyse the available literature to identify the use of this technology in agriculture and the scope of further research. This chapter will mainly focus on its publication trend, journal productivity and impact, prolific studies, and coherent themes.

Methodology: For a comprehensive review, bibliometric and content analysis of 71 open-access articles collected through a structured database of Mendeley is done. These articles were published during 2017–2021.

Findings: The execution of blockchain is continuously increasing in the agriculture sector, which has resulted in automation in supply chain management, land registrations, and crop insurance. The study revolves around supply chain management, digitisation of agriculture, and sustainable economic development. This study’s conclusions can help agriculturalists improve their understanding of blockchain implementation in agriculture. The study also gives directions for future research.

South Asia, home to one-fifth of humanity, perennially has been a disaster-prone region. In 2007, for instance, the Centre for Research on the Epidemiology of Disasters (CRED) reported that out of the top five countries in the world hit hardest by natural disasters, the first two were Bangladesh and India, while Pakistan occupied the fourth position (CRED Crunch, 2008). This was not an exceptional year but generally has been the trend, which highlights the comparative vulnerability of the region to disasters. Two-thirds of the disasters the region experiences are climate related and there have been phenomenal increases in their frequency, severity, and unpredictability in recent times. The severest impacts have been in terms of sea-level rise leading to submergence of low-lying coastal areas and depletion of Himalayan glaciers, threatening the perennial rivers that sustain the food, water, energy, and environmental security of the region. Climate change is surely creating grounds for newer and more severe risks of disasters in the region in the coming years.