Big Data: A Game Changer for Insurance Industry (Emerald Studies in Finance, Insurance, and Risk Management, 6)

Half Title Page
	Series Editors Page
	Title Page
	Copyright Page
Contents
About the Editors
About the Contributors
Preface
Foreword
Chapter 1:Use of Wearable and Health Applications in Insurance Industry Using Internet of Things and Big Data
	I. Introduction
	II. What are Wearables?
	III. IoT and Wearable Devices
	IV. Integration of Wearables and Insurance
	V. Use of Wearables in Insurance
	Claims Settlement
	More Value-added Services
	Personalised Products
		Customer’s perception towards wearables
		Some of the pitfalls to be considered while using data from the wearables
	VII. Conclusion
	References
Chapter 2:Emerging Technologies of Big Data in the Insurance Market
	1. Introduction
		1.1. Characteristics of Big Data
		1.2. As per the McKinsey Global Institute, there are Five Significant Ways the Big Data Movement Benefits Organisations (Wielki, 2014). They are as follows:
		1.3. Types of Big Data
		1.4. Role of Big Data in the Insurance Industry
	2. Literature Review
	3. BDA
		3.1. Types of BDA (Faculty of Informatics, International University of Rabat, Technopolis parc, Sala el Jadida 11100, Morocco, 2018)
	4. Latest Trends in BDA (10 Latest Trends in Big Data Analytics that you Should Know in 2021, n.d.)
		4.1. Data Service
		4.2. Smarter AI
		4.3. Predictive Analytics
		4.4. Quantum Computing
		4.5. Edge Computing
		4.6. Hybrid Clouds
		4.7. Data Fabric
		4.8. Dark Data
	5. The Key Sectors of Insurance that BDA is Changing the Working of Insurance are as Follows
		5.1. Underwriting and Pricing
		5.2. Healthcare
		5.3. Settlements the Claims
		5.4. Tailored-made Insurance
		5.5. Customer Experience
	6. How Insurance Companies Can Get Competitive Advantages by Using Big Data
		6.1. Customer-related Competitive Advantages
			6.1.1. Enhanced Customer Engagement and Insight. With the digitalisation of insurance goods and services and the growing trend of consumers linking with brands or organisations in the digital arena, insurance service providers have an opportunity to incre
			6.1.2. Control over Customer Churn. Insurance firms do not pay enough attention to prevent client churn, or the frequent switching of customers, in the actuarial sector. The recovery of lost clients and the construction of early warning systems for impend
			6.1.3. Enable to Acquire New Customers. Each individual generates vast data through social media, emails, and feedback, which provides far more detailed information about their choices than any survey questionnaire. Insurance businesses can improve their
			6.1.4. Promote Better Marketing Services. Big data can assist insurance businesses to segment their existing clients, precisely identifying their essential needs, selling extra insurance and cross-sell products, increasing customer value, and enhancing co
			6.1.5. Innovation. When it comes to exploiting big data, a fresh perspective on goods and services will encourage the development of new or improved offerings. Big data may help us gain a deeper understanding of our customers and, as a result, can be util
		6.2. Risk-related Competitive Advantages
			6.2.1. Fraud Detection and Prevention. Institutions that provide insurance services have long been prone to fraud. Individuals and criminal organisations seek to defraud financial institutions, and the intelligence and complexity of these tactics are gro
			6.2.2. Control the Risk Effectively. When underwriting consumers, big data technologies may also enhance risk control by identifying possible risks ahead of time, preventing prospective risks from becoming actual risks. Big data technology can link inform
			6.2.3 Risk Assessment. Insurers have long placed a premium on verifying customers’ information when assessing risks, and big data technology can help speed up this process. Insurance businesses can use predictive modelling to evaluate potential concerns d
	7. Emerging Technologies in Big Data
		7.1 AI
			7.1.1 ML. It is a set of AI concerned with the science and engineering of allowing robots to ‘learn’ for themselves (Dugas et al., 2021). Learning from and producing data-driven forecasts based on data and learned experiences constitute an important strat
			7.1.2 Smart Robots. You can quickly automate business operations with a smart robotic process automation with an AI solution. Insurers routinely manage an unusually high amount of business procedures such as claims processing and underwriting and issuing
		7.2 Blockchain
		7.3 IoT
		7.4 Quantum Computing
		7.5 Chatbots
	8. Global Impact of Emerging Technologies in Insurance Sector
		8.1 Global AI Robots Market to Reach US$21.4 Billion by 2026(Wood, 2020)
		8.2 Open-source and Data Ecosystems in Global Market($66.84 Billion Open Source Services Market by Industry, Service Type, And Geography – Global Forecast to 2026 – ResearchAndMarkets.Com, 2020)
		8.3 Advances in Cognitive Technologies in Global Market(Gray, 2021)
	9. Conclusion
	References
Chapter 3:Adoption of Internet of Things and Services in the Indian Insurance Industry
	Introduction
	Literature Review
	IoT and its Vitality
		Lowering The Risk
		Customer Retention
	Customer Relationships with IoT
	Methodology
	Conclusion
	References
Chapter 4:Emerging Technologies in Insurance Sector: Evidence from Scientific Literature
	Introduction
	Emerging Technologies in the Insurance Sector
		AI
		Big Data
		Blockchain
		Chatbots
		Drones
		IoT
		Mobile Technology
		Predictive Analytics
		Social Media
		Telematics
		Low Codes
	Methodology
		Data Collection
	Findings and Discussions
	Conclusion
	Future Research Directions
	References
Chapter 5:Predictive Performance of Indian Insurance Industry Using Artificial Neural Network (ANN) and Support Vector Machine (SVM): A Comparative Study
	1.0. Introduction
	2.0. Background of the Study
	3.0. Methodology
	4.0. Data Analysis
	5.0. Conclusion
	References
Chapter 6:Blockchain Technology as an Emerging Technology in the Insurance Market
	1.0. Introduction
		1.1. Focus of Study
		1.2. Research Question
	2. Literature Review
		2.1. Overview of the Insurance Industry
		2.2. Overview of Blockchain Technology
		2.3. Usage of blockchain in the insurance industry
		2.3.1. Fraud Prevention and Risk Assessment
		2.3.2. Reducing cost and time of claim processing
		2.3.3. Use of Smart Contracts and IoT
		2.3.4. Policy Underwriting
		2.3.5. Micro-insurance
		2.3.6. Big Data
		2.3.7. Reinsurance
		2.3.8. Casualty and Property Insurance
		2.3.9 Subrogation in Claim
		2.3.10. Other Examples of Usage of Blockchain in the Insurance Sector
	3.0. Findings
		RQ1. What will be the effect of blockchain technology on the operations of the insurance industry?
		RQ2. What will be the application of blockchain technology in various functions of the insurance industry?
	4.0. Practical Implication of Blockchain in the Insurance Industry
	5.0. Conclusion
	6.0. Future Scope of the Study
	References
Chapter 7:Crowdsourcing, Insurance and Analytics: The Trio of Insurance Future
	Introduction
	Crowdsourcing
	Dimensions of Crowdsourcing
	Tipping Point: Insurance, Crowdsourcing and Analytics
	Emerging Trends
		Review Aggregator
		Kaggle Campaign ‘The Claim Prediction Challenge’
		Pet Insurance
		Agri-insurance
		Health Insurance
		Insurance-rating Platform
		Crowdsourcing Insurance in Case of Natural Disasters
	Motivations for Crowdsourcing
	Concerns for Merging Crowdsourcing Insurance and Analytics
		Transaction Costs and Knowledge Appropriately
		Crowdsourcing of Inventive Activities (CIA)
		Lack of Contributors
		Request Definition
		Quality Concerns
		Confidentiality and Privacy
		Plagiarism
		Intellectual Property Right
		Matching the Pay Scale
	Conclusion
	References
Chapter 8:Big Data in Insurance Innovation
	1. Introduction
	2. Big Data Use in Insurance Companies
	3. Application of Big Data in Insurance Innovation
		3.1. Depth Analysis of Insurance Products Innovation
		3.1.1. Enables the Customisation of Insurance Products. Big data is crucial in the customisation of insurance products. The insurance business can only develop suitable products for groups that satisfy specified criteria in traditional financial services,
			3.1.2. Improving Technology of New Insurance Products. Big data enables the creation of current insurance solutions. In the age of big data, some uninsurable risks induced by actuarial assumptions can be turned into insurable risks. Big data may be used t
			3.1.3. Creation of Product Bundling or Risk Management Service Packages. It is possible to gain knowledge of customers’ tastes and insurance needs through big data evaluation of previous customers to determine the best amalgamation of reimbursement types
		3.2. Improvement of Insurance Product Pricing Accuracy
			3.2.1. Enhancement of Insurance Coverage Risk Factors. Insurers can combine the records got in a couple of approaches via big data technological know-how and enrich their calculation of danger factors and pricing and their inside commercial enterprise pla
			3.2.2. Attainment of Correct Pricing. Traditional actuarial science is based on the historical loss statistics of sample types. Insurance groups can rate existing insurance according to lifestyle table data, combined with activity rates, premium rates, an
			3.2.3. Consciousness of Energetic Exceptional Alterations. The general decoration rate is fixed within the insurance period in traditional insurance company operation, but this means that the implicit threat has not been linked because the threat situatio
		3.3. Consciousness of Detailed and Distinguished Marketing
			3.3.1. Effectiveness of Customers. Client sapience, or consumer sapience, explains trends in mortal guests that aim to increase the effectiveness of a consumer product or service and increase deals for the fiscal benefit of those furnishing the product or
			3.3.2. Allows the Correct Acquirement of Innovative Customers. Through the complete and accurate use of data and information, insurance companies can conduct multidimensional and three-dimensional research and analysis of customers, not only to realise co
			3.3.3. Encourages Potential Consumers to be Converted. Even though far too many insurance businesses have substantial client bases, the great majority of them are ‘quasi-customers’, or consumers who are offered free insurance. According to statistics, the
			3.3.4. Motivates Commodity Consumers’ Targeted Promotion. Consumers who have acquired insurance goods are referred to as stock customers. Big data can assist insurance businesses to segment their current customers, precisely identifying their essential ne
			3.3.5. Reduces Customer Loyalty. Insurance firms do not pay that much consideration to prevent customer churn or the frequent shifting of clients in the actuarial industry. Big data significantly impacts customer rehabilitation and early warning systems f
		3.4. Increases the Effectiveness and Effectiveness of Insurance Products
			3.4.1. Enhances the Standard of Insured Companies. Coverage agencies can use huge records to research clients’ characteristics, habits, and possibilities to improve provider exceptional. For instance, the ‘percent’ app, an important sales and provider pla
			3.4.2. Process Management Simplified. Big data streamlines the underwriter customer service, making it more convenient for insurers and consumers. In the age of big data, insurance firms may gather client information such as basic personally identifiable
			3.4.3. Enhances Operational Effectiveness. Big data increases the effectiveness of insurance products by allowing for the enhancement of customer data paperwork and their preparation, optimising fully automated commercial lending rules and dynamic web pro
			3.4.4. Offers Customised Services. Insurance businesses may utilise big data to define their clients’ personalities; segment them based on their purchase histories, service preferences, and other statistics; and provide more tailored services. Ping a Home
			3.4.5. Offers Value-added Facilities. Big data technologies may also supply clients with valuation services. Big data can support the implementation of insurance and reinsurance resources, bridge service efficiency in business model and multimedia applica
		3.5. Enhances Security Management and Anti-fraud Activities
			3.5.1. Enhances Risk Management. The advancement of big data has significantly enhanced risk management in insurance businesses. Insurance businesses may acquire detailed information on the insured and reliable information disclosure about clients. Big da
			3.5.2. Limits Potentially Problems. Insurance firms may improve customer intervention strategies by ‘monitoring’ customers’ behaviour, lowering the likelihood of authorised accidents, and reducing insurance company risk by utilising big data technologies.
			3.5.3. Constructs an Anti-fraud Network Connection. By constructing an anti-fraud network, big data technology connects all departments, third-party platforms, networks, and communication operators of insurance firms, reducing asymmetric information and i
			3.5.4. Aids in the Prevention and Mitigation of Claims. A huge quantity of compensation, often known as an ‘abnormal value’, frequently leads to a larger compensation ratio. Big data technologies may assist insurance businesses in detecting outliers in re
	4. Challenges Faced by Insurance Companies
		4.1. Conflicts of Market Development
		4.2. Data value of Insurance Industry
		4.3. Deficient Communications
		4.4. Sharing of Data Island
		4.5. Unlimited Competition
	5. Big Data Approaches of Insurance Corporations
	6. Conclusion
	References
Chapter 9:Big Data Analytics Application and Enhanced FDI Prospects for the Insurance Sector
	Introduction
	Literature Review
	The Advent of BDA
	Significance of Insurance Sector in Economic Development
	Scope of FDI in the Insurance Sector
	Decisions and Destinations of FDI Based on BDA
	Process Followed by IPAS for FDI in the Insurance Sector
	Conclusion
	References
Chapter 10:The Use of Big Data in the Insurance Industry Innovations in China
	1. Introduction: What’s Really Big Data, and Why Does it Make Much Difference?
	2. Importance of Big Data for Insurance Companies
	3. Insurance and Attributes of Big Data
	4. The Use of Big Data in Transformation of Insurance
		Customisation of Insurance Products
		New Insurance Product Development
		Product Bundles or Risk Assessment Service Packs can be Developed
		Accuracy In Insurance Product Pricing Improvement
		Insurance Risk Factors Enrichment
		Accurate Pricing Is Accomplished
		Dynamic Premium Adjustments are Implemented
		Precise and Differentiated Marketing Realisation
		Gives you a Better Understanding of your Customers
		Customer Acquisition
		Retention of Customers
		Assessment of the Risk
		Preventing and Detecting Fraud
		Reduced Costs
		Pricing and Service that is Personalised
		Internal Processes Impact
		Aids in the Prevention and Mitigation of Claims
		Helps in Creating Anti-fraud Network
	5. Threats Faced by Insurance Companies
		Controlling the Flow of Real-time Data
		Protecting digital data
		Data usage regulations
	6. Insurance Companies’ Big Data Initiatives
	7. Summary and Conclusion
	References
Chapter 11:New Developments in Banking Sector and Impact: Covid-19
	1. Introduction
	2. Literature Review
	3. Methodology
		3.1. Research Design
		3.1.1. Data Sources. This is a kind of review paper on the existing literature and tries to elaborate on the current state of understanding on the topic. Due care has been placed to collect the data from reliable and authentic sources.
	4. Analysis of New Developments in Banking Sector and Impact
		4.1. Impact on Banks
		4.2. Challenges in Retail Banking
			4.2.1. Liquidity. Given the lockdown within the country, the default cases may have risen up and substantially numerous companies might have lost income, returns, or revenues for an extended time. A rise in defaults will probably root up many issues in li
			4.2.2. Compressed Net Interest Income Margin. The funding costs are going to lower the output on bank assets. This is primarily because of raised competition level in mortgage loans. Also, it can be due to the safety of grade assets investments. This impa
			4.2.3. Potential Drawdown on Credit Facilities by Clients. Banks play a crucial part in ensuring the supply of financial resources is adequate to hold up individuals and businesses with no risk at their liquidity position. Hence, banks may have to re-regu
			4.2.4. Revision to Loan Loss Provision Estimates. Since the economic outlook always remains exceedingly volatile, the probable credit losses formerly calculated will have to be re-examined to account for the uncertainty and to the level of the pandemic. B
			4.2.5. Loss of Trust in the Banking Entities. It happened after many decades that a private sector bank had seen a cessation which the RBI had forced. The news of Yes Bank had been more than enough to alarm within the market. The devastation of multistate
			4.2.6. Connecting with Changing Customers Habit. Because of severe social removal standards, many shoppers utilise web-based financial channels to deal with their cash. This is regularly prone to a more long-lasting change in client inclinations to advanc
			4.2.7. Credit Risk. There has been an undeniable surge in credit and obligation rebuilding solicitations’ during this time, especially from small- and medium-sized enterprises (SMEs). Banks will have to update or redraft their ways to deal with credit haz
			4.2.8. Digital Banking. Numerous clients, who were hesitant to interface carefully with their banks, are ‘constrained’ to attempt it during isolation. Subsequently, banks have significantly increased their computerised collaborations with clients during t
			4.2.9. Digital Threats. Monetary establishments face conceivable ascent in cyberattacks and extortion endeavours. One ought to appreciate the development of computerised banking administrations, which helps the client and even representatives working from
			4.2.10. Emergency Management. The dubious and dynamic climate prompts banks to shape fast choices and become acclimated rapidly. The functioning style and client cooperation with representatives and workers’ connection with partners are changed. Banks bec
			4.2.11. Decrease in Income. A sharp decline in banking income has been observed due to lower client footfall in banks during pandemic. Further lower interest rates, diminished and remote working of staff is a possible reason for decrease in banking income
				4.2.12. Expansion in Expenses. Another Challenge in Retail Banking is expansion in its expenses. A reasonable weight on net interest payments because of lower interest has been imposed. Also a prerequisite of extra provisioning as in anticipated focus on
	5. RBI Took Steps for the Banking Sector to Cope UP with the COVID-19 Impact
		5.1. Repo Rate
		5.2. Reverse Repo
		5.3. Loan Moratorium
		5.4. Cash Reserve Ratio (CRR)
		5.5. Long-term Repo Operation (LTRO)
		5.6. Ease of Working Capital Financing
		5.7. Working Capital Interest
		5.8. Deferment of Net Stable Funding Ratio (NSFR)
		5.9. Marginal Standing Facility (MSF)
		5.10. Fresh Liquidity
	6. Recommendations
		6.1. New Developments in Banking due to Covid-19
		6.2. Covid Challenges Customary Financial Propensities
		6.3. Accepting Neo Technologies
		6.4. Covid is Energising the Development towards Computerised Banking
	7. Conclusions and Future Implications
	References
Chapter 12:Foreign Direct Investment Impact and Effect on the Indian Insurance Sector: Major Key Drivers
	Introduction
		Indian Insurance Market
		The Scenario of the Indian Insurance Market
			Life-insured Companies. These offer coverage to the life of human beings. It safeguards a person against any unnatural or untimely demise of the individual. It safeguards the dependent person on him. These insurance companies also gave the chance or optio
			Non-life-insured Players. General insurance provides coverage for routine like insured things related to basic human requirements tours, wellness, two-wheeler, four-wheeler, electronic gadgets, etc. These are some of the products available for the country
	Literature Review
	Research Methodology
	Regulatory Framework of the Insurance
		IRDAI Activities
		Duties of IRDA
		Source of Data and Period of Analysis
	Conclusions and Recommendations
	References
Chapter 13:Big Data Analytics – Tools and Techniques – Application in the Insurance Sector
	1. Introduction
	2. Evolution of Big Data
	3. BDA
		3.1. Types of BDA
	4. Tools for BDA
		4.1. Apache Hadoop and Map–Reduce
		4.2. Apache Spark
		4.3. MongoDB
	5. Applications of BDA
		5.1. Healthcare
		5.2. Banking
		5.3. Education
		5.4. Media and Entertainment
	6. BDA Applications in Insurance
		6.1. Customisation of Insurance Products
			6.1.2. Developing new insurance products. Big data creates a possibility of developing new insurance products and transforming uninsurable risks caused by actuarial realities into insurable risks. Many new insurance products have been developed using big
		6.2. Customer Acquisition
		6.3. Risk Assessment
		6.4. Fraud Detection
			6.4.1. Social Network Analysis (Sna) for Fraud Detection. The SNA allows the insurers to proactively look into large volumes of data and identify the relationships through the links and nodes of the social network. This tool implements a hybrid approach t
			6.4.2. Predictive Analytics for Fraud Detection. Predictive analytics uses both text and sentiment analysis for fraud detection. BDA helps analyse the unstructured data and detect fraud proactively, which was not possible with earlier methods. This techno
		6.5. Personalised Service and Pricing
	7. Big Data Challenges in Insurance Sector
		7.1. Managing the Data Flow
		7.2. Data Privacy and Security
		7.3. Storage Issues
	8. Artificial Intelligence (AI) and ML in Insurance
		8.1. Conversational Agents
		8.2. Computer Vision
	9. Conclusions
	References
Chapter 14:Revamping Indian Non-Life Insurance Industry with a Trusted Network: Blockchain Technology
	1. Introduction
		1.1. History of blockchain
	2. Literature Review
	3. Application of Blockchain Technology
	4. Market Share of Top 10 Business Segments of theNon-life Insurance Industry
	5. Applications of Blockchain Technology in Various Segments of the Non-life Insurance Industry
		5.1. Blockchain Technology in Health Insurance
		5.2. Blockchain Technology in Motor Insurance
		5.3. Blockchain Technology in Marine Insurance
		5.4. Blockchain Technology and Reinsurance
		5.5. Blockchain in Crop Insurance
		5.6. BlockChain in the Aviation Industry
	5. Conclusions
	References
Chapter 15:Digital Financial Inclusion
	1. Introduction
	2. Definition and Goal of Digital Financial Inclusion
		2.1. Definition of Digital Financial Inclusion
		2.2. Goal of Digital Financial Inclusion
	3. Components of Digital Financial Inclusion
	4. Providers and Instruments for Digital Financial Inclusion
		4.1. Types of Digital Financial Service Providers for Digital Financial Inclusion
		4.2. Instruments for digital financial inclusion
	5. Important Digital Financial Inclusion Research
	6. Benefits of Digital Financial Inclusion
	7. Risks and Regulatory Issues of Digital Financial Inclusion
		7.1. Risks of Digital Financial Inclusion
		7.2. Regulatory Issues
	8. Digital Financial Inclusion: Making it Work
	9. Limitations of Digital Technology in Promoting Financial Inclusion
		9.1. It Lacks the Human Touch
		9.2. A Garbage-in-garbage-out (GIGO) Approach to Financial Inclusion
	10. Conclusion
	Reference
Chapter 16:Perceived Effectiveness of Digital Transformation and InsurTech Use in Malta: A Study in the Context of the European Union’s Green Deal
	Introduction
	Literature Review
		Local Environment
		Effectiveness of Digital Systems – Insurance Ecosystem
			Customer centricity and experience innovation. In a survey conducted by McKinsey and Company, it was identified that if a company innovates its customer service, the satisfaction of the policyholders is increased by 33%, while expenses are reduced by 25%
			Product marketing. One of the main difficulties in obtaining loyal customers is overcoming the online noise and appealing them (Blue Corona, 2019). Consumer acquisition normally takes up the largest amount of costs for insurance companies. This can be mad
			Communication. Ideally, insurers should be able to communicate directly and regularly with all policyholders to provide a more personalised service. AI merged with customer data creates intelligent communication that is able to close the gaps by suiting c
			Efficiency. For incumbent insurers, meaning an existing company with an evident level of success in the market, operational efficiency is given extreme importance to their strategic objectives. The effective investment of digital innovation by insurance c
			Insurance fraud detection software. Walker (2019) argued that in the United Kingdom, it is estimated that 50% of all claims have an ‘element’ of fraud. Premiums and fraudulent cases are directly proportional. When the number of fraudulent cases falls, pre
			Product development. The majority of the prototypes used in product developments are established using historical data. Policyholders favour customised products rather than having one-size-fits-all policy (WNS, 2020). In fact, a research held by an InsurT
			Internal processes. 
				Cost reduction. Most of the licenced InsurTech companies focus mainly on pricing and underwriting. With insurers needing to assemble external data from multiple aspects of risk – including geographical locations, customer profiles and risk characteristics
				Data collection. Another factor in the insurance function chain is policy administration systems and central management systems. In total, 28% of licenced InsurTech firms primarily focus of the abovementioned systems (Walker, 2019). Implementing a policy
				Business processes. Similar to the above processes, the remaining 8% of the 760 licenced global InsurTech companies assessed by Walker (2019), focuses their operations on the claims process. The current business logics within the insurance industry focuse
				Internal processes effectiveness. According to Neely (1995), effectiveness is referred to as the extent to which customer requirements are met. In terms of effectiveness, if one reaches a higher level of product reliability, this might lead to greater cus
	Method
		The Research Instrument
		Research Questions
		Sampling procedure
		Data analysis
	Results
		Sample Characteristics
		EFA
	Discussion and Conclusions
	References
Chapter 17:The General Data Protection Regulation (GDPR) for Risk Mitigation in the Insurance Industry
	List of abbreviations
	Background
	Purpose
	The Introduction of the GDPR
	Main Themes of the GDPR
		Consent (Articles 4, 6–9, 22, and 49)
		DPO (Articles 35 and 37–39)
		Email Marketing (Articles 6, 7, 21, and 95)
		Encryption (Articles 6, 32, and 34)
		Fines or Penalties (Articles 58, 70, 83, and 84)
		Personal Data (Articles 4 and 9)
		Privacy by Design and Default (Article 25)
		Privacy Impact Assessment (PIA) (Articles 5, 35, 36, and 57)
		Processing (Articles 4, 27–30, 40, 42, 44–47, and 82)
		Records of Processing Activities (Articles 5 and 30)
		Right of Access (Articles 12, 15, and 46)
		Right to be Forgotten (Articles 17 and 19)
		Right to be Informed (Articles 12–14)
		Third Countries (Articles 40, 42, 44–49, and 63)
	The GDPR Overall
	Impact of the GDPR on Insurance Companies
		Nature of Information
		Pricing and Underwriting
		Direct Marketing
		Claims Processing
		Automated Decision-making
		Right to Data Portability
		Right to Erasure
		Data Retention
	The GDPR Issues in Biomedical Research and Technological Advances
	The Proportionality Directive
	Research Design
	Data Collection
	Analysis of the Results
	Demographics of the Survey Respondents
		Gender
		Age of Participants
		How Confident are you in the GDPR?
		Are you a Client or do you hold a Position within an Insurance Entity?
	Questions Targeted Towards Insurance Employees
		The GDPR has Allowed Underwriters to Charge a more Accurate Premium.
		GDPR gives Freedom of Interpretation
		Deciphering the Expectations of GDPR itself was not Challenging
		The Gdpr has Helped Increase Training to Ensure that Employees Remain Aware of their Responsibilities Regarding the Protection of Personal Data and the Identification of Personal Data Breaches as Soon as Possible
		Training Costs have not Increased Due to the Gdpr
		The Gdpr has not Increased Employees’ Workload or Necessitated Extra Labour Resources
		The System of Fines for Companies who have a Breach in their System is Fair
		The GDPR has Reduced Paperwork
		The Factor Variables which show the Effectiveness of the GDPR in Risk Mitigation
		Extraction Method: PCA (Table 13).
		Do these Factors Vary with the Different Demographics?
			Factor 1: Purpose 
			The multiple regression analysis results in F(4,367) = 15.196 (p < 0.01) and the variables explained 13.3% of the variability in Factor 1, which is termed ‘Practice’.
			Factor 2: Practice 
			The multiple regression analysis of Factor 2, termed ‘Practice’, was computed, resulting in F(4,367) = 15.206, p < 0.01. The variables explain 13.3% of the variability in Factor 2’s GEMRM model. This means that the overall model is a sign of the outcome o
			Factor 3: Proficiency 
			The multiple regression analysis of Factor 3 results in F(4,367) = 24.37, and variables explained 20.1% of the variability in Factor 3. This means that the model is a significant predictor (Tables 22 and 23).
			Factor 4: Performance 
			The multiple linear regression of Factor 4 is presented in the following tables.
	Concluding Remarks
	References
Chapter 18:Cybersecurity Law-based Insurance Market
	1. Introduction
		1.1 Chapter Outline
	2. Evolution of Cyberattacks
		2.1 Cases (Cyberattacks and Solutions)
		2.2 Strategic Principles of Cybersecurity
		2.3 Cybersecurity measures
	3. Cybercrime and Cyber Law
		3.1 Cybercrime
		3.2 ‘History of Cybercrime’
			3.2.1 Evolution of cybercrime. From Morris worm to extortion, cybercrime has progressed. Many governments, particularly India, are attempting to stop such atrocities or cyberattacks, but they are constantly evolving and impacting our country (see Table 1)
		3.3 Classifications of Cybercrime
		3.4 Safety in Cyberspace
	4. The Cybercrime and Cyberterrorism Threat
	5. Data Breaches: Rising Costs and Liability Exposure
	6. Lack of Information Sharing
	7. Expected Impact of a Well-developed Cyber Insurance Market
	8. Conclusion
	References
Index