Big Data: A Game Changer for Insurance Industry
- Length: 448 pages
- Edition: 1
- Language: English
- Publisher: Emerald Publishing
- Publication Date: 2022-07-19
- ISBN-10: 1802626069
- ISBN-13: 9781802626063
- Sales Rank: #0 (See Top 100 Books)
Big data – unstructured and/or structured data being used to influence underwriting, rating, pricing, forms, marketing and claims handling and incentivize risk reduction – is a relatively recent development in the insurance industry, the data sets previously being too impossibly great to analyse through traditional methods. However, with the global capacity to collect and store data growing alongside advancements in AI and machine learning technology, insurers need to seriously evaluate their technology stacks to ensure they can remain competitive and respond to growing customer demand.
Striking a balance between the technical characteristics of the subject and the practical aspects of decision making, spanning from fraud analytics in claims management, to customer analytics, to risk analytics in solvency, the comprehensive coverage presented makes Big Data an invaluable resource for any insurance professional.
Providing high quality academic research, Emerald Studies in Finance, Insurance, and Risk Management provides a platform for authors to explore, analyse and discuss current and new financial models and theories, and engage with innovative research on an international scale. Subjects of interest may include banking, accounting, auditing, compliance, sustainability, behaviour, management, and business economics.
Cover Title Copyright 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 Abstract 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 Abstract 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 6.2. Risk-related Competitive Advantages 7. Emerging Technologies in Big Data 7.1 AI 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 Abstract 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 Abstract 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 Abstract 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 Abstract 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 Abstract 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 Abstract 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 3.2. Improvement of Insurance Product Pricing Accuracy 3.3. Consciousness of Detailed and Distinguished Marketing 3.4. Increases the Effectiveness and Effectiveness of Insurance Products 3.5. Enhances Security Management and Anti-fraud Activities 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 Abstract 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 Abstract 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 Abstract 1. Introduction 2. Literature Review 3. Methodology 3.1. Research Design 4. Analysis of New Developments in Banking Sector and Impact 4.1. Impact on Banks 4.2. Challenges in Retail Banking 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 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 Abstract 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 6.2. Customer Acquisition 6.3. Risk Assessment 6.4. Fraud Detection 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* Abstract 1. Introduction 1.1. History of blockchain 2. Literature Review 3. Application of Blockchain Technology 4. Market Share of Top 10 Business Segments of the Non-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 Abstract 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* Abstract Introduction Literature Review Local environment Effectiveness of Digital Systems – Insurance Ecosystem 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* Abstract 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? Concluding Remarks References Appendix: Survey Specific to insurance companies Chapter 18. Cybersecurity Law-based Insurance Market Abstract 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’ 3.3 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
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