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Purpose: In addition to the liberalisation policy, big data has revolutionised the level of awareness among customers about the quality and prices of insurance products. The rationale behind this study is to underline the issues in managing product portfolios in a disruptive environment, where a sudden and unexpected situation like COVID-19 pandemic is going to challenge the traditional models and insurance covers of organisations as well as individuals.

Methodology: The study is based on secondary data. The scope of the study will only be confined to the top two general insurance companies in India based on year of registration and market share to compare their product portfolios during pre- and post-liberalisation periods ranging from 1985–1986 to 2000–2001 and 2001–2002 to 2018–2019, respectively.

Findings: There is a lack of a balanced product portfolio for fulfilling the varying needs of customers. The insurance companies needed to set up different portfolios and should provide separate covers for natural catastrophes such as floods, earthquakes, landslides, tsunami, and the occurrence of new pandemics like COVID-19.

Significance: The study highlights that the outbreak of COVID-19 and similar pandemics or global emergencies need special preparation from the insurance sector.

Purpose – The aim of this chapter is to examine the constant proportion portfolio insurance (CPPI) method when the multiple is allowed to vary over.

Methodology/approach – A quantile approach is introduced under the dependent return hypothesis. We use for example ARCH-type models.

Findings – In this framework, we provide explicit values of the multiple as function of the past asset returns and other state variables. We show how the multiple can be chosen to satisfy the guarantee condition, at a given level of probability and for particular market conditions.

Originality/value of paper – We show in this chapter that it is possible to choose variable multiples for the CPPI method if quantile hedging is used and in the case of dependent log returns. Upper bounds can be calculated for each level of probability and according to state variables. This new multiple can be determined according to the distributions of the risky asset log return and volatility.

Insurance is a contract whereby one party (the policyholder) promises and makes a payment or series of payments in exchange for the second party’s (the insurance company’s) promise to indemnify the policyholder for losses covered under the terms of the policy. Perhaps it is easier to just think of insurance as a transaction where the policyholder trades small regular losses (the premium paid) for large and irregular gains (claims proceeds).

While it may seem somewhat disproportionate to devote an entire chapter to more detailed treatment of a single risk financing tool, insurance has a very large impact, not only in terms of its intrinsic value, but also in terms of the many ways in which insurance influences risk management thinking and practice. As will be shown, some of this influence is waning and in other cases it could be argued that insurance ‘thinking’ has hindered efforts to respond to facts on the ground and the ability to adapt the role of risk management in organisations.

To provide a useful discussion, this chapter will cover both the products that the insurance industry offers and the structure of the industry itself, along with addressing legal and regulatory matters that were touched upon in Chapter Nine. The chapter concludes with an overview of public sector insurance issues that provides a basis for understanding alternatives to insurance that have emerged in dramatic fashion in recent decades – which in turn provides a basis for considering some of the constraints that insurance imposes on risk management practice.

Saving is essential to the health of economies and households, yet relatively little scholarship investigates saving behaviors among the urban working class in the nineteenth century. This chapter uses five surveys of industrial workers in 1880s New Jersey, an analysis of which reveals sophisticated saving behaviors consistent with life-cycle and precautionary theories. The mean saving rate was between 8% and 12% of annual income. Younger households saved less than older households. Householders with longer expected careers, on average, saved less. Life insurance and fraternal societies were the most popular saving vehicles, but workers also used savings banks and building and loan associations, alone and in combination.

This chapter presents the survey of selected linear and mixed integer programming multi-objective portfolio optimization. The definitions of selected percentile risk measures are presented. Some contrasts and similarities of the different types of portfolio formulations are drawn out. The survey of multi-criteria methods devoted to portfolio optimization such as weighting approach, lexicographic approach, and reference point method is also presented. This survey presents the nature of the multi-objective portfolio problems focuses on a compromise between the construction of objectives, constraints, and decision variables in a portfolio and the problem complexity of the implemented mathematical models. There is always a trade-off between computational time and the size of an input data, as well as the type of mathematical programming formulation with linear and/or mixed integer variables.

Introduction: All countries are interested in attracting foreign direct investment (FDI) as it provides for productivity gains and modernisation for attaining sustainable development goals. Multinational corporations (MNCs) collect a vast volume of structured and unstructured big data when seeking international expansion by the FDI route in the insurance sector, but concluding these data may not be practically feasible. So nowadays, for finalising their FDI ventures, MNCs depend on machine-based algorithms for quick analysis of big data sets.

Purpose: This chapter explores how emerging big data analytics and predictive modelling fields can scale and speed up FDI decisions in the insurance sector.

Methodology: The author used a descriptive study based on secondary data from sources like World Bank, The Organisation for Economic Co-operation and Development (OECD), World Trade Organisation (WTO), and International Finance Corporation (IFC) data repositories to identify variables such as risks, costs, trade agreements, regulatory policies, and gross domestic product (GDP) that affect FDI movements. This chapter highlights the process flow that can be beneficial to convert big data sets using statistical tools and computer software such as Statistical Analytics Software (SAS), IBM SPSS Statistics.

Findings: The application of artificial intelligence-based statistical tools on FDI variables can help derive time-series graphs and forecast revenues. The authors found that foreign investors can narrow their prospect search for industry or product to manageable from varied investment opportunities in host countries. Advancements in big data analysis offer cost-effective methods to improve decision-making and resource management for enterprises.