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Financial assurance rules, also known as financial responsibility or bonding requirements, foster cost internalization by requiring potential polluters to demonstrate the financial resources necessary to compensate for environmental damage that may arise in the future. Accordingly, assurance is an important complement to liability rules, restoration obligations, and other regulatory compliance requirements. The paper reviews the need for assurance, given the prevalence of abandoned environmental obligations, and assesses the implementation of assurance rules in the United States. From the standpoint of both legal effectiveness and economic efficiency, assurance rules can be improved. On the whole, however, cost recovery, deterrence, and enforcement are significantly improved by the presence of existing assurance regulations.

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.

Purpose: The scope of this research is to conduct a study on the perceived effectiveness of developments in InsurTech, by determining online use integration in the Maltese insurance market.

Methodology: To do this, the authors employed a self-administered questionnaire to which 471 participants responded on a 5-point Likert scale. We subjected the data collected from this questionnaire to statistical analysis, specifically, exploratory factor analysis (EFA) and multiple linear analysis using the Statistical Package for Social Science (SPSS) version 26.

Results: EFA loaded best on five factors of insurance customers’ perceived effectiveness, which make up the effectiveness model (EM), namely ‘Factor 1 – Internal Process Enhancement’, ‘Factor 2 – Cost-Efficiencies’; ‘Factor 3 – Time-Sensitive Conditions’, ‘Factor 4 – The Contemporary Use of Artificial Intelligence and Marketing in Relation to Customer Service’ and ‘Factor 5 – Customer Relations and Application of InsurTech in Communication’. Moreover, multiple linear regression results show that the perceived effectiveness dimension – EM is statistically significantly related to online use in Malta.

Practical implications: Therefore, it can be argued that the Maltese insurance sector is well prepared to meet the obligations and requirements of the European Green Deal. Findings shed light on the preparedness of the Maltese insurance market to accept innovative green proposals to go online with processes.

Purpose: Blockchain is the most significant technological innovation of the generation following the internet. However, most individuals are unaware of how it will affect the insurance business.

Design/methodology/approach: The present study utilises a systematic review methodology to assess the existing literature on blockchain technology in the insurance industry.

Findings: Currently, few insurance companies are researching and using blockchain technology for automated claims, fraud detection, and cash flow tracking. The use of blockchain technology in the insurance business is still in its early stages, and many significant issues remain unsolved. This chapter lays out the discussions regarding the current state of blockchain technology in the insurance business.

Practical implications: Using distributed ledger technology (DLT), all the stakeholders can easily exchange the relevant information on a real-time basis. In particular, blockchain technology will help all insurance companies minimise discrepancies related to fraudulent claims by keeping track of the customer’s history of the customer reducing administrative costs.

Originality/value: It has been observed that very few studies have been conducted in this field. This is a holistic study that focuses on the applications of blockchain technology in various non-life insurance segments.

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.

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.

Purpose: The insurance business is confronted with coordination difficulties that necessitate a high level of mobility, flexibility, and the capacity to analyse heterogeneous, location-dependent data from different sources and qualities. Recent innovations in emerging technologies have given the insurance industry new organisational options. When coupled with data analytics, crowdsourcing in the insurance industry facilitates solving complex issues with the wisdom of crowds. The notion of incorporating crowdsourcing and big data into the mainstream activities of insurance management is developed in this article, as are the ramifications and gains of collective intelligence achieved by Crowdsourcing and the added value of crowdsourcing insurance activities.

Design/methodology/approach: This chapter is a conceptual work that builds on relevant literature.

Findings: This chapter analyses what insurance industry managers should consider when coordinating crowdsourced activities and how they may benefit from collective intelligence combined with data analytics in terms of efficient and real-time response management for the insurance industry. Furthermore, it is demonstrated how they may use crowdsourcing to exploit information and benefit from invoking additional resources and eliminating the institutional voids present in the industry.

Practical implications: Exemplary applications that take advantage of crowdsourcing and data analytics would help the insurance sector respond flexibly, efficiently, and effectively in real time.

Originality/value: This chapter offers new collaborative ways to enhance the decision-making of insurance industry managers. The relevance of overcoming institutional voids is expanded, and repercussions from the given framework are suggested using data analytics.

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.

The insurance industry often experiences criticism for unethical and frequently illegal activities. This document suggests that insurers operate in an uncompetitive environment and that the nature of insurer operations leads otherwise ethical individuals in the direction of questionable ethical decisions throughout the operations of an insurance company.