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The economic crisis of 2007–2009 had a major negative impact on financial institutions in general. Health and life insurance industry continues to face growth challenges even six years after the economic crisis. Due to the challenges faced by health and life insurance industry, several companies in this industry have merged and some decided to get out of this business altogether. This study benchmarks 10 life and health insurance companies on the basis of return on equity, investment yield, and loss ratio for the year 2009 and 2014.

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.

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.

Insurance frauds deeply affect insurance companies, policyholders, and the insurance industry as a whole. The cost of fraudulent damage affects the profitability of companies, and has negative effects on the society in terms of moral values. Increases in insurance costs can lead to increases in the premiums paid by policyholders, each family, and, ultimately, all of the insured. Recently, new legal regulations related to this issue have been performed in Turkey and higher institutions have been created. A regulation issued by the Under-secretariat of the Treasury, on June 1, 2011, defines insurance fraud as aggravated fraud. Insurance fraud in Turkey usually takes the form of intentional misrepresentations of facts to the insurance company to get the company to pay for something not actually covered by the policy. Studies examined the insurance industry in terms of the concept of financial crime, and inclusion of the concept of financial crime in insurance regulations was proposed since financial crimes have an important place in the current problems of the industry. In addition, it is seen that insurance frauds have changed over time as a result of studies.

The study explores the impact of the general insurance industry’s financial soundness on Sri Lanka’s financial performance by using the CARAMELS approach for seven years (2011–2019) and using secondary data. The study utilised panel data regression analysis. Return on Asset was used as the proxy of financial performance while the 10 dimensions were employed. The best-fitted model is the fixed effect model (FEM), which indicates capital adequacy ratio (CAR) and profitability ratio has a positive impact and that the retention ratio (RR), claims ratio, and expenses ratio harm financial performance in the general insurance sector. The study concluded that capital adequacy, earnings and profitability, reinsurance, and actuaries are important predictors of financial performance for general insurers. The findings help the regulator and general insurers set better performance targets and enable insurance company managers to allocate capital more efficiently.

Introduction: There is a variety of wearables and health applications available in the market which allow the tracking of various health and lifestyle measures like blood sugar, calorie counter, number of steps, sleep patterns, etc. After the Covid-19 pandemic, people have become more aware of their health and use these wearables to maintain a healthy lifestyle. Insurance companies in India are also eyeing the potential usage of these wearables in life and health insurance.

Purpose: This research aims to look at the emergence of wearables and health apps and their usage in India’s life and health insurance industry. This study also focuses on how these devices might benefit insurers’ business models and some of the pitfalls to consider.

Methodology: The study used both primary and secondary data. A survey was conducted to understand the customer perception towards usage of wearables. The secondary research included the analysis of the integration of wearables by insurance companies.

Findings: The research would be helpful to the insurance companies as it would help them to understand the customer’s viewpoint for the usage of wearables in the insurance industry. This study would also allow insurers to understand new dimensions, such as where the wearables improve customer satisfaction and engagement. The study results would be helpful for the customers for the appropriate usage of wearables and the internet of things (IoT). Insurance companies can provide better pricing and make personalised insurance plans that ultimately help customers.

Introduction: Big data in the insurance industry can be defined as structured or unstructured data that can affect the rating, marketing, pricing, or underwriting. The five Vs of big data provide insurers with a valuable framework for converting their raw data into actionable information. These five Vs are specifically: (1) Volume: The need to look at the type of data and the internal systems; (2) Velocity: The speed at which big data is generated, collected, and refreshed; (3) Variety: Refers to both the structured and unstructured data; (4) Veracity: Refers to trustworthiness and confidence in data; and (5) Value: Refers to whether the data collected are good or bad.

Purpose: Insurance companies face many data challenges. However, the administration of big data has allowed insurers to acknowledge the demand of their customers and develop more personalised products. In addition, it can be used to make correct decisions about insurance operations such as risk selection and pricing.

Methodology: We do this by conducting a systematic literature review on big data. Our emphasis is on gathering information on the five Vs of the big data and the insurance market. Specifically, how big data can help in data-driven decisions.

Findings: Big data technology has created an endless series of opportunities, which have ensured a surge in its usage. It has helped businesses make the process more systematic, cost-effective, and helped in the reduction in fraud and risk prediction.

Introduction: New ideas and concepts of big data have emerged in recent years in response to the astounding growth of data in many industries. Furthermore, the phenomenal increase in the use of the internet and social media has added enormous amounts of data to conventional data processing systems. Still, it has also created challenges for traditional data processing.

Purpose: A significant characteristic of the insurance sector is critically dependent on information. This sector generates a great deal of structured and unstructured data, which traditional data processing techniques cannot handle. As compared to conventional insurance data processing and decision-making requirements, this lesson shows an analysis of data technology’s value additions.

Research methodology: The author assesses the primary use of cases for data in the insurance industry via a case study analysis. From the perspective of the insurance sector, this chapter examines the concepts, technologies, and tools of big data. A few analytical reviews by the insurance company are also provided, which justified several gains gained either through inefficient processing of massive, diverse data sets or by supporting better decisions.

Findings: This chapter demonstrates the importance of adopting new business models that allow insurers to move beyond understand and protect and become more predictive and preventative by using the tools and technologies of big data technology.

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.