Data alchemy in the insurance industry: the transformative power of big data analytics
This collected edition provides a comprehensive and practical roadmap for insurers, data scientists, technologists, and insurance enthusiasts alike, to navigate the data-driven revolution that is sweeping the insurance landscape
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Weitere Verfasser: | , , , |
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Leeds
Emerald Publishing
[2025]
|
| Ausgabe: | First edition |
| Online-Zugang: | DE-2070s DE-945 DE-29 Volltext |
| Zusammenfassung: | This collected edition provides a comprehensive and practical roadmap for insurers, data scientists, technologists, and insurance enthusiasts alike, to navigate the data-driven revolution that is sweeping the insurance landscape |
| Beschreibung: | 1 Online-Ressource (XVI, 219 Seiten) |
| ISBN: | 9781836085829 9781836085843 |
| DOI: | 10.1108/9781836085829 |
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| 245 | 1 | 0 | |a Data alchemy in the insurance industry |b the transformative power of big data analytics |c edited by Sanjay Taneja, Pawan Kumar, Reepu, Mohit Kukreti and Ercan Özen |
| 250 | |a First edition | ||
| 264 | 1 | |a Leeds |b Emerald Publishing |c [2025] | |
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| 505 | 8 | |a Cover -- Data Alchemy in the Insurance Industry -- Data Alchemy in the Insurance Industry: The Transformative Power of Big Data Analytics -- Copyright Page -- Contents -- About the Editors -- About the Contributors -- Foreword -- Preface -- Introduction of the Book -- 1. Data Alchemy in Insurance: A Catalyst for Improving Financial Inclusion Levels and Insurance Penetration -- Abstract -- 1 Introduction -- 2 Literature Review -- 3 Research Methodology -- 4 Data Analysis -- 5 Results -- 6 Implications -- 7 Conclusion -- References -- 2. Unlocking the Power of Big Data in Insurance: The Role of Data Analytics -- Abstract -- 1 Introduction -- 2 Global Insurance Industry -- 3 Big Data -- 4 Big Data Challenges -- 5 Big Data Analytics -- 6 The Role of Big Data Analytics in Insurance Transformation -- 7 The Types of Data in Insurance Industry -- 7.1 Prescriptive Data Analysis -- 7.2 Predictive Analytics -- 7.3 Diagnostic Data Analysis -- 7.4 Descriptive Data Analysis -- 8 Data Analytics in Insurance -- 8.1 Customer Acquisition and Retention -- 8.2 Risk Modeling and Pricing -- 8.3 Claims Management -- 9 Conclusion -- References -- 3. AI-Driven Personalized Risk Management in the Insurance Sector -- Abstract -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Data Analysis -- 4 Factor 1: Better Risk Management -- 5 Factor 2: Adoption and Anticipation -- 6 Factor 3: Better Customization -- 7 Conclusion -- References -- 4. Ethical Considerations in Data Analytics: Challenges, Principles, and Best Practices -- Abstract -- 1. Introduction -- 2. Key Ethical Challenges -- 2.1 Privacy and Data Protection -- 2.2 Transparency and Accountability -- 2.3 Bias and Fairness -- 3. Ethical Principles in Data Analytics -- 3.1 Respect for Individuals -- 3.2 Transparency and Accountability -- 3.3 Fairness and Equity | |
| 505 | 8 | |a 4. Best Practices for Ethical Data Analytics -- 4.1 Ethical Governance and Oversight -- 4.2 Data Transparency and Consent -- 4.3 Bias Detection and Mitigation -- 5. Methodology -- 6. Conclusion -- References -- 5. Analyzing Two and a Half Decades of Health Insurance and Big Data Analytics Research: A Bibliometric Study -- Abstract -- 1 Introduction -- 2 Research Methodology -- 3 Data Analysis and Interpretation -- 3.1 TheYearly Trend of Manuscripts Made for Health Insurance and Big Data Analytics -- 3.2 Top 10 Countries That Have Contributed in Publishing Manuscripts Made for Health Insurance and Big Data Analytics -- 3.3 Institutions That Are Sponsoring Projects for Manuscripts on Health Insurance and Big Data Analytics -- 3.4 The Word Cloud of the Manuscripts for Health Insurance and Big Data Analytics -- 3.5 The Thematic Map of Keywords for the Field of Health Insurance and Big Data Analytics -- 3.6 The Co-Occurrence Analysis of Keywords for the Field of Health Insurance and Big Data Analytics -- 4 Implications -- 5 Limitations and Recommendations -- 6 Conclusion -- References -- 6. Workers' Compensation in the Remote Work Era: Proactive Risk Management Through HR Policies and Data Alchemy Practices -- Abstract -- 1 Introduction -- 2 A Review of Literature -- 3 The Research Methodology -- 4 A Conceptual Model -- 5 Analysis and Discussion -- 5.1 Descriptive Statistics -- 6 Correlation Results -- 7 ANOVA -- 7.1 Regression Statistics -- 7.2 Coefficients -- 8 Conclusion -- References -- 7. The Philosopher's Stone: Applications of Data Alchemy-Customer Personalization, Profiling, and Retention -- Abstract -- 1 Introduction to Customer Personalization in Insurance -- 2 Theoretical and Foundational Understanding of Ethical and Privacy Considerations in Customer Personalization -- 2.1 Measuring and Evaluating Personalization Initiatives | |
| 505 | 8 | |a 2.2 Data Collection and Management for Personalization -- 2.3 Leveraging Big Data Analytics for Personalization -- 3 Objectives of the Study -- 4 Review of Literature and Hypotheses on Customer Personalization, Profiling, and Retention -- 4.1 Creating Personalized Customer Experiences -- 4.2 Understanding Customer Segmentation and Profiling -- 5 Research Methodology and Analysis -- 5.1 Impact of Personalized Customer Experiences on Customer Satisfaction -- 5.2 Impact of Customer Profiling Leads to More Effective Targeting of Marketing Efforts -- 6 Case Studies and Success Stories -- 6.1 Case Study: Progressive Insurance Snapshot Program -- 6.1.1 Background -- 6.1.2 Informed Consent -- 6.1.3 Data Ownership -- 6.1.4 Implementation -- 6.1.5 Impact -- 6.1.6 Conclusive Observation -- 6.2 Case Study: AXA Insurance DriveSave Program -- 6.2.1 Background -- 6.2.2 Informed Consent -- 6.2.3 Data Ownership -- 6.2.4 Implementation -- 6.2.5 Impact -- 6.2.6 Conclusive Observation -- 6.3 Success Story: Geico's Personalized Marketing Campaigns -- 6.3.1 Background -- 6.3.2 Data Analysis and Segmentation -- 6.3.3 Personalized Messaging -- 6.3.4 Dynamic Content Optimization -- 6.3.5 Measurable Results -- 6.3.6 Conclusive Observation -- 6.4 Success Story: Allstate's Usage-Based Insurance Program -- 6.4.1 Background -- 6.4.2 Data Collection and Analysis -- 6.4.3 Personalized Premiums -- 6.4.4 Customer Engagement and Satisfaction -- 6.4.5 Risk Management and Loss Prevention -- 6.4.6 Conclusive Observation -- 7 Challenges and Future Directions -- 8 Conclusion and Final Thoughts -- Acknowledgments -- References -- 8. Impact of Employee-Performance Data Management on Job Satisfaction in the Insurance Sector -- Abstract -- 1 Introduction -- 1.1 Data Management -- 1.2 Impact on Employee Performance -- 1.3 Role of Data Quality and Governance -- 1.4 Big Data Analytics | |
| 505 | 8 | |a 1.5 Psychological and Work-Life Quality Factors -- 2 Literature Review -- 3 Objectives of the Study -- 3.1 Data Integration -- 3.2 Technology Integration -- 3.3 Ethical Considerations -- 4 Research Methodology -- 4.1 Empirical Analysis -- 5 Results and Discussions -- 6 Implications of the Study -- 6.1 Theoretical Implications -- 6.2 Practical Implications -- 7 Conclusion -- References -- 9. Revolutionizing Insurance Practices Through Advanced Data Alchemy -- Abstract -- 1 Introduction -- 2 Literature Review -- 2.1 Insurance Industry -- 2.2 Data Alchemy -- 2.3 Scopus Database -- 3 Research Methodology -- 4 Findings -- 4.1 Case Study of SmartRisk Insurance Company -- 4.1.1 Introduction -- 4.1.2 Challenge -- 4.1.3 Solution -- 4.1.4 Implementation -- 4.1.5 Results -- 4.1.6 Conclusion of the Case -- 5 Implications -- 6 Limitations and Recommendations -- 7 Conclusion -- References -- 10. The Future of Alchemy: Emerging Trends and Technologies Metaverse in Insurance - A Virtual Customer Experience -- Abstract -- 1 Introduction -- 2 Future of Internet: Metaverse Simplified -- 3 Literature Review -- 4 Revolutionary Potential of Metaverse Technology in Insurance -- 4.1 Digitalization of Processes -- 4.2 Data Analytics and Predictive Modeling -- 4.3 Personalized Products and Pricing -- 4.4 Insurtech Innovation -- 4.5 Customer Engagement and Experience -- 4.6 Remote Underwriting and Claims -- 4.7 Emphasis on Cybersecurity -- 5 Application of Metaverse to Insurance -- 5.1 Enhanced Customer Experience -- 5.2 Cost Reduction and Other Sources of Income -- 5.3 Operational Excellence -- 6 Enhancing the Appeal of Insurance -- 7 Metaverse: A Dual-Edged Weapon -- 7.1 Collection of Data by Third Parties -- 7.2 Countless Privacy Concerns -- 7.3 Cybersecurity risks -- 8 Metaverse's Impact on the Insurance Value Chain -- 8.1 Generating Revenue | |
| 505 | 8 | |a 8.2 Engaging Customer Experiences -- 8.3 Insights Derived from Data Analysis -- 8.4 Digitalization of Operations -- 8.5 Integration of Assets -- 8.6 Interoperability Across Platforms -- 8.7 Improved Insurance Procedures -- 8.8 Imagining Situations -- 8.9 Efficient Loss Adjustment -- 8.10 Online Goods Exchange Platform -- 9 Technological Barriers in Conventional Insurance Procedures -- 9.1 Outdated Systems -- 9.2 Data Management -- 9.3 Cybersecurity -- 9.4 Automation and Process Optimization -- 9.5 Customer Expectations -- 9.6 Adherence to Regulations -- 9.7 Data Analytics -- 10 Challenges and Risks Posed by the Metaverse in the Insurance Sector -- 10.1 Cybersecurity Weaknesses -- 10.2 Legal and Intellectual Property Challenges -- 10.3 Ambiguity in Regulations -- 10.4 Data Security and Privacy -- 10.5 Emerging and Incalculable Risks -- 10.6 Technical Proficiency Requirement -- 10.7 Difficulties in Customer Adoption -- 10.8 Server Outages and Technical Issues -- 11 Conclusion -- 12. Acknowledgments -- References -- 11. Trends and Patterns in Insurance Research: A Bibliometric Analysis (2020-2024) -- Abstract -- 1 Introduction -- 2 Objectives of the Study -- 3 Methodology -- 3.1 Bibliometrics -- 3.2 Keyword Extraction -- 3.3 Data Procurement -- 3.4 Techniques -- 4 Discussion on Results -- 4.1 Keyword Co-occurrence Analysis -- 4.2 Cocitation Analysis -- 4.2.1 Cluster I -- 4.2.2 Cluster II -- 4.2.3 Cluster III -- 4.3 Bibliographic Coupling -- 4.3.1 Cluster I: Digital Transformation in Insurance: Challenges and Opportunities -- 4.3.2 Cluster II: Innovation and Efficiency in Insurance Operations -- 4.3.3 Cluster III: Factors Influencing Insurance Uptake and Adoption -- 4.3.4 Cluster VI: Insurance Sector Dynamics: Economic Factors and Efficiency -- 4.3.5 Cluster V: Behavioral Dynamics in Insurance Markets | |
| 505 | 8 | |a 4.3.6 Cluster VI: Market Dynamics and Risk Management in Insurance | |
| 520 | |a This collected edition provides a comprehensive and practical roadmap for insurers, data scientists, technologists, and insurance enthusiasts alike, to navigate the data-driven revolution that is sweeping the insurance landscape | ||
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Datensatz im Suchindex
| _version_ | 1825857339828207616 |
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| adam_text | |
| any_adam_object | |
| author | Taneja, Sanjay |
| author2 | Taneja, Sanjay Kumar, Pawan 1974- Reepu Kukreti, Mohit Özen, Ercan |
| author2_role | edt edt edt edt edt |
| author2_variant | s t st p k pk r m k mk e ö eö |
| author_GND | (DE-588)1245184342 (DE-588)1191946037 |
| author_facet | Taneja, Sanjay Taneja, Sanjay Kumar, Pawan 1974- Reepu Kukreti, Mohit Özen, Ercan |
| author_role | aut |
| author_sort | Taneja, Sanjay |
| author_variant | s t st |
| building | Verbundindex |
| bvnumber | BV050102367 |
| collection | ZDB-55-BME ZDB-30-PQE |
| contents | Cover -- Data Alchemy in the Insurance Industry -- Data Alchemy in the Insurance Industry: The Transformative Power of Big Data Analytics -- Copyright Page -- Contents -- About the Editors -- About the Contributors -- Foreword -- Preface -- Introduction of the Book -- 1. Data Alchemy in Insurance: A Catalyst for Improving Financial Inclusion Levels and Insurance Penetration -- Abstract -- 1 Introduction -- 2 Literature Review -- 3 Research Methodology -- 4 Data Analysis -- 5 Results -- 6 Implications -- 7 Conclusion -- References -- 2. Unlocking the Power of Big Data in Insurance: The Role of Data Analytics -- Abstract -- 1 Introduction -- 2 Global Insurance Industry -- 3 Big Data -- 4 Big Data Challenges -- 5 Big Data Analytics -- 6 The Role of Big Data Analytics in Insurance Transformation -- 7 The Types of Data in Insurance Industry -- 7.1 Prescriptive Data Analysis -- 7.2 Predictive Analytics -- 7.3 Diagnostic Data Analysis -- 7.4 Descriptive Data Analysis -- 8 Data Analytics in Insurance -- 8.1 Customer Acquisition and Retention -- 8.2 Risk Modeling and Pricing -- 8.3 Claims Management -- 9 Conclusion -- References -- 3. AI-Driven Personalized Risk Management in the Insurance Sector -- Abstract -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Data Analysis -- 4 Factor 1: Better Risk Management -- 5 Factor 2: Adoption and Anticipation -- 6 Factor 3: Better Customization -- 7 Conclusion -- References -- 4. Ethical Considerations in Data Analytics: Challenges, Principles, and Best Practices -- Abstract -- 1. Introduction -- 2. Key Ethical Challenges -- 2.1 Privacy and Data Protection -- 2.2 Transparency and Accountability -- 2.3 Bias and Fairness -- 3. Ethical Principles in Data Analytics -- 3.1 Respect for Individuals -- 3.2 Transparency and Accountability -- 3.3 Fairness and Equity 4. Best Practices for Ethical Data Analytics -- 4.1 Ethical Governance and Oversight -- 4.2 Data Transparency and Consent -- 4.3 Bias Detection and Mitigation -- 5. Methodology -- 6. Conclusion -- References -- 5. Analyzing Two and a Half Decades of Health Insurance and Big Data Analytics Research: A Bibliometric Study -- Abstract -- 1 Introduction -- 2 Research Methodology -- 3 Data Analysis and Interpretation -- 3.1 TheYearly Trend of Manuscripts Made for Health Insurance and Big Data Analytics -- 3.2 Top 10 Countries That Have Contributed in Publishing Manuscripts Made for Health Insurance and Big Data Analytics -- 3.3 Institutions That Are Sponsoring Projects for Manuscripts on Health Insurance and Big Data Analytics -- 3.4 The Word Cloud of the Manuscripts for Health Insurance and Big Data Analytics -- 3.5 The Thematic Map of Keywords for the Field of Health Insurance and Big Data Analytics -- 3.6 The Co-Occurrence Analysis of Keywords for the Field of Health Insurance and Big Data Analytics -- 4 Implications -- 5 Limitations and Recommendations -- 6 Conclusion -- References -- 6. Workers' Compensation in the Remote Work Era: Proactive Risk Management Through HR Policies and Data Alchemy Practices -- Abstract -- 1 Introduction -- 2 A Review of Literature -- 3 The Research Methodology -- 4 A Conceptual Model -- 5 Analysis and Discussion -- 5.1 Descriptive Statistics -- 6 Correlation Results -- 7 ANOVA -- 7.1 Regression Statistics -- 7.2 Coefficients -- 8 Conclusion -- References -- 7. The Philosopher's Stone: Applications of Data Alchemy-Customer Personalization, Profiling, and Retention -- Abstract -- 1 Introduction to Customer Personalization in Insurance -- 2 Theoretical and Foundational Understanding of Ethical and Privacy Considerations in Customer Personalization -- 2.1 Measuring and Evaluating Personalization Initiatives 2.2 Data Collection and Management for Personalization -- 2.3 Leveraging Big Data Analytics for Personalization -- 3 Objectives of the Study -- 4 Review of Literature and Hypotheses on Customer Personalization, Profiling, and Retention -- 4.1 Creating Personalized Customer Experiences -- 4.2 Understanding Customer Segmentation and Profiling -- 5 Research Methodology and Analysis -- 5.1 Impact of Personalized Customer Experiences on Customer Satisfaction -- 5.2 Impact of Customer Profiling Leads to More Effective Targeting of Marketing Efforts -- 6 Case Studies and Success Stories -- 6.1 Case Study: Progressive Insurance Snapshot Program -- 6.1.1 Background -- 6.1.2 Informed Consent -- 6.1.3 Data Ownership -- 6.1.4 Implementation -- 6.1.5 Impact -- 6.1.6 Conclusive Observation -- 6.2 Case Study: AXA Insurance DriveSave Program -- 6.2.1 Background -- 6.2.2 Informed Consent -- 6.2.3 Data Ownership -- 6.2.4 Implementation -- 6.2.5 Impact -- 6.2.6 Conclusive Observation -- 6.3 Success Story: Geico's Personalized Marketing Campaigns -- 6.3.1 Background -- 6.3.2 Data Analysis and Segmentation -- 6.3.3 Personalized Messaging -- 6.3.4 Dynamic Content Optimization -- 6.3.5 Measurable Results -- 6.3.6 Conclusive Observation -- 6.4 Success Story: Allstate's Usage-Based Insurance Program -- 6.4.1 Background -- 6.4.2 Data Collection and Analysis -- 6.4.3 Personalized Premiums -- 6.4.4 Customer Engagement and Satisfaction -- 6.4.5 Risk Management and Loss Prevention -- 6.4.6 Conclusive Observation -- 7 Challenges and Future Directions -- 8 Conclusion and Final Thoughts -- Acknowledgments -- References -- 8. Impact of Employee-Performance Data Management on Job Satisfaction in the Insurance Sector -- Abstract -- 1 Introduction -- 1.1 Data Management -- 1.2 Impact on Employee Performance -- 1.3 Role of Data Quality and Governance -- 1.4 Big Data Analytics 1.5 Psychological and Work-Life Quality Factors -- 2 Literature Review -- 3 Objectives of the Study -- 3.1 Data Integration -- 3.2 Technology Integration -- 3.3 Ethical Considerations -- 4 Research Methodology -- 4.1 Empirical Analysis -- 5 Results and Discussions -- 6 Implications of the Study -- 6.1 Theoretical Implications -- 6.2 Practical Implications -- 7 Conclusion -- References -- 9. Revolutionizing Insurance Practices Through Advanced Data Alchemy -- Abstract -- 1 Introduction -- 2 Literature Review -- 2.1 Insurance Industry -- 2.2 Data Alchemy -- 2.3 Scopus Database -- 3 Research Methodology -- 4 Findings -- 4.1 Case Study of SmartRisk Insurance Company -- 4.1.1 Introduction -- 4.1.2 Challenge -- 4.1.3 Solution -- 4.1.4 Implementation -- 4.1.5 Results -- 4.1.6 Conclusion of the Case -- 5 Implications -- 6 Limitations and Recommendations -- 7 Conclusion -- References -- 10. The Future of Alchemy: Emerging Trends and Technologies Metaverse in Insurance - A Virtual Customer Experience -- Abstract -- 1 Introduction -- 2 Future of Internet: Metaverse Simplified -- 3 Literature Review -- 4 Revolutionary Potential of Metaverse Technology in Insurance -- 4.1 Digitalization of Processes -- 4.2 Data Analytics and Predictive Modeling -- 4.3 Personalized Products and Pricing -- 4.4 Insurtech Innovation -- 4.5 Customer Engagement and Experience -- 4.6 Remote Underwriting and Claims -- 4.7 Emphasis on Cybersecurity -- 5 Application of Metaverse to Insurance -- 5.1 Enhanced Customer Experience -- 5.2 Cost Reduction and Other Sources of Income -- 5.3 Operational Excellence -- 6 Enhancing the Appeal of Insurance -- 7 Metaverse: A Dual-Edged Weapon -- 7.1 Collection of Data by Third Parties -- 7.2 Countless Privacy Concerns -- 7.3 Cybersecurity risks -- 8 Metaverse's Impact on the Insurance Value Chain -- 8.1 Generating Revenue 8.2 Engaging Customer Experiences -- 8.3 Insights Derived from Data Analysis -- 8.4 Digitalization of Operations -- 8.5 Integration of Assets -- 8.6 Interoperability Across Platforms -- 8.7 Improved Insurance Procedures -- 8.8 Imagining Situations -- 8.9 Efficient Loss Adjustment -- 8.10 Online Goods Exchange Platform -- 9 Technological Barriers in Conventional Insurance Procedures -- 9.1 Outdated Systems -- 9.2 Data Management -- 9.3 Cybersecurity -- 9.4 Automation and Process Optimization -- 9.5 Customer Expectations -- 9.6 Adherence to Regulations -- 9.7 Data Analytics -- 10 Challenges and Risks Posed by the Metaverse in the Insurance Sector -- 10.1 Cybersecurity Weaknesses -- 10.2 Legal and Intellectual Property Challenges -- 10.3 Ambiguity in Regulations -- 10.4 Data Security and Privacy -- 10.5 Emerging and Incalculable Risks -- 10.6 Technical Proficiency Requirement -- 10.7 Difficulties in Customer Adoption -- 10.8 Server Outages and Technical Issues -- 11 Conclusion -- 12. Acknowledgments -- References -- 11. Trends and Patterns in Insurance Research: A Bibliometric Analysis (2020-2024) -- Abstract -- 1 Introduction -- 2 Objectives of the Study -- 3 Methodology -- 3.1 Bibliometrics -- 3.2 Keyword Extraction -- 3.3 Data Procurement -- 3.4 Techniques -- 4 Discussion on Results -- 4.1 Keyword Co-occurrence Analysis -- 4.2 Cocitation Analysis -- 4.2.1 Cluster I -- 4.2.2 Cluster II -- 4.2.3 Cluster III -- 4.3 Bibliographic Coupling -- 4.3.1 Cluster I: Digital Transformation in Insurance: Challenges and Opportunities -- 4.3.2 Cluster II: Innovation and Efficiency in Insurance Operations -- 4.3.3 Cluster III: Factors Influencing Insurance Uptake and Adoption -- 4.3.4 Cluster VI: Insurance Sector Dynamics: Economic Factors and Efficiency -- 4.3.5 Cluster V: Behavioral Dynamics in Insurance Markets 4.3.6 Cluster VI: Market Dynamics and Risk Management in Insurance |
| ctrlnum | (ZDB-30-PQE)EBC31492188 (ZDB-30-PAD)EBC31492188 (ZDB-89-EBL)EBL31492188 (OCoLC)1468527078 (DE-599)BVBBV050102367 |
| dewey-full | 368 |
| dewey-hundreds | 300 - Social sciences |
| dewey-ones | 368 - Insurance |
| dewey-raw | 368 |
| dewey-search | 368 |
| dewey-sort | 3368 |
| dewey-tens | 360 - Social problems and services; associations |
| discipline | Wirtschaftswissenschaften |
| doi_str_mv | 10.1108/9781836085829 |
| edition | First edition |
| format | Electronic eBook |
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AI-Driven Personalized Risk Management in the Insurance Sector -- Abstract -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Data Analysis -- 4 Factor 1: Better Risk Management -- 5 Factor 2: Adoption and Anticipation -- 6 Factor 3: Better Customization -- 7 Conclusion -- References -- 4. Ethical Considerations in Data Analytics: Challenges, Principles, and Best Practices -- Abstract -- 1. Introduction -- 2. Key Ethical Challenges -- 2.1 Privacy and Data Protection -- 2.2 Transparency and Accountability -- 2.3 Bias and Fairness -- 3. Ethical Principles in Data Analytics -- 3.1 Respect for Individuals -- 3.2 Transparency and Accountability -- 3.3 Fairness and Equity</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">4. Best Practices for Ethical Data Analytics -- 4.1 Ethical Governance and Oversight -- 4.2 Data Transparency and Consent -- 4.3 Bias Detection and Mitigation -- 5. Methodology -- 6. Conclusion -- References -- 5. Analyzing Two and a Half Decades of Health Insurance and Big Data Analytics Research: A Bibliometric Study -- Abstract -- 1 Introduction -- 2 Research Methodology -- 3 Data Analysis and Interpretation -- 3.1 TheYearly Trend of Manuscripts Made for Health Insurance and Big Data Analytics -- 3.2 Top 10 Countries That Have Contributed in Publishing Manuscripts Made for Health Insurance and Big Data Analytics -- 3.3 Institutions That Are Sponsoring Projects for Manuscripts on Health Insurance and Big Data Analytics -- 3.4 The Word Cloud of the Manuscripts for Health Insurance and Big Data Analytics -- 3.5 The Thematic Map of Keywords for the Field of Health Insurance and Big Data Analytics -- 3.6 The Co-Occurrence Analysis of Keywords for the Field of Health Insurance and Big Data Analytics -- 4 Implications -- 5 Limitations and Recommendations -- 6 Conclusion -- References -- 6. Workers' Compensation in the Remote Work Era: Proactive Risk Management Through HR Policies and Data Alchemy Practices -- Abstract -- 1 Introduction -- 2 A Review of Literature -- 3 The Research Methodology -- 4 A Conceptual Model -- 5 Analysis and Discussion -- 5.1 Descriptive Statistics -- 6 Correlation Results -- 7 ANOVA -- 7.1 Regression Statistics -- 7.2 Coefficients -- 8 Conclusion -- References -- 7. The Philosopher's Stone: Applications of Data Alchemy-Customer Personalization, Profiling, and Retention -- Abstract -- 1 Introduction to Customer Personalization in Insurance -- 2 Theoretical and Foundational Understanding of Ethical and Privacy Considerations in Customer Personalization -- 2.1 Measuring and Evaluating Personalization Initiatives</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">2.2 Data Collection and Management for Personalization -- 2.3 Leveraging Big Data Analytics for Personalization -- 3 Objectives of the Study -- 4 Review of Literature and Hypotheses on Customer Personalization, Profiling, and Retention -- 4.1 Creating Personalized Customer Experiences -- 4.2 Understanding Customer Segmentation and Profiling -- 5 Research Methodology and Analysis -- 5.1 Impact of Personalized Customer Experiences on Customer Satisfaction -- 5.2 Impact of Customer Profiling Leads to More Effective Targeting of Marketing Efforts -- 6 Case Studies and Success Stories -- 6.1 Case Study: Progressive Insurance Snapshot Program -- 6.1.1 Background -- 6.1.2 Informed Consent -- 6.1.3 Data Ownership -- 6.1.4 Implementation -- 6.1.5 Impact -- 6.1.6 Conclusive Observation -- 6.2 Case Study: AXA Insurance DriveSave Program -- 6.2.1 Background -- 6.2.2 Informed Consent -- 6.2.3 Data Ownership -- 6.2.4 Implementation -- 6.2.5 Impact -- 6.2.6 Conclusive Observation -- 6.3 Success Story: Geico's Personalized Marketing Campaigns -- 6.3.1 Background -- 6.3.2 Data Analysis and Segmentation -- 6.3.3 Personalized Messaging -- 6.3.4 Dynamic Content Optimization -- 6.3.5 Measurable Results -- 6.3.6 Conclusive Observation -- 6.4 Success Story: Allstate's Usage-Based Insurance Program -- 6.4.1 Background -- 6.4.2 Data Collection and Analysis -- 6.4.3 Personalized Premiums -- 6.4.4 Customer Engagement and Satisfaction -- 6.4.5 Risk Management and Loss Prevention -- 6.4.6 Conclusive Observation -- 7 Challenges and Future Directions -- 8 Conclusion and Final Thoughts -- Acknowledgments -- References -- 8. Impact of Employee-Performance Data Management on Job Satisfaction in the Insurance Sector -- Abstract -- 1 Introduction -- 1.1 Data Management -- 1.2 Impact on Employee Performance -- 1.3 Role of Data Quality and Governance -- 1.4 Big Data Analytics</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">1.5 Psychological and Work-Life Quality Factors -- 2 Literature Review -- 3 Objectives of the Study -- 3.1 Data Integration -- 3.2 Technology Integration -- 3.3 Ethical Considerations -- 4 Research Methodology -- 4.1 Empirical Analysis -- 5 Results and Discussions -- 6 Implications of the Study -- 6.1 Theoretical Implications -- 6.2 Practical Implications -- 7 Conclusion -- References -- 9. Revolutionizing Insurance Practices Through Advanced Data Alchemy -- Abstract -- 1 Introduction -- 2 Literature Review -- 2.1 Insurance Industry -- 2.2 Data Alchemy -- 2.3 Scopus Database -- 3 Research Methodology -- 4 Findings -- 4.1 Case Study of SmartRisk Insurance Company -- 4.1.1 Introduction -- 4.1.2 Challenge -- 4.1.3 Solution -- 4.1.4 Implementation -- 4.1.5 Results -- 4.1.6 Conclusion of the Case -- 5 Implications -- 6 Limitations and Recommendations -- 7 Conclusion -- References -- 10. The Future of Alchemy: Emerging Trends and Technologies Metaverse in Insurance - A Virtual Customer Experience -- Abstract -- 1 Introduction -- 2 Future of Internet: Metaverse Simplified -- 3 Literature Review -- 4 Revolutionary Potential of Metaverse Technology in Insurance -- 4.1 Digitalization of Processes -- 4.2 Data Analytics and Predictive Modeling -- 4.3 Personalized Products and Pricing -- 4.4 Insurtech Innovation -- 4.5 Customer Engagement and Experience -- 4.6 Remote Underwriting and Claims -- 4.7 Emphasis on Cybersecurity -- 5 Application of Metaverse to Insurance -- 5.1 Enhanced Customer Experience -- 5.2 Cost Reduction and Other Sources of Income -- 5.3 Operational Excellence -- 6 Enhancing the Appeal of Insurance -- 7 Metaverse: A Dual-Edged Weapon -- 7.1 Collection of Data by Third Parties -- 7.2 Countless Privacy Concerns -- 7.3 Cybersecurity risks -- 8 Metaverse's Impact on the Insurance Value Chain -- 8.1 Generating Revenue</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">8.2 Engaging Customer Experiences -- 8.3 Insights Derived from Data Analysis -- 8.4 Digitalization of Operations -- 8.5 Integration of Assets -- 8.6 Interoperability Across Platforms -- 8.7 Improved Insurance Procedures -- 8.8 Imagining Situations -- 8.9 Efficient Loss Adjustment -- 8.10 Online Goods Exchange Platform -- 9 Technological Barriers in Conventional Insurance Procedures -- 9.1 Outdated Systems -- 9.2 Data Management -- 9.3 Cybersecurity -- 9.4 Automation and Process Optimization -- 9.5 Customer Expectations -- 9.6 Adherence to Regulations -- 9.7 Data Analytics -- 10 Challenges and Risks Posed by the Metaverse in the Insurance Sector -- 10.1 Cybersecurity Weaknesses -- 10.2 Legal and Intellectual Property Challenges -- 10.3 Ambiguity in Regulations -- 10.4 Data Security and Privacy -- 10.5 Emerging and Incalculable Risks -- 10.6 Technical Proficiency Requirement -- 10.7 Difficulties in Customer Adoption -- 10.8 Server Outages and Technical Issues -- 11 Conclusion -- 12. Acknowledgments -- References -- 11. Trends and Patterns in Insurance Research: A Bibliometric Analysis (2020-2024) -- Abstract -- 1 Introduction -- 2 Objectives of the Study -- 3 Methodology -- 3.1 Bibliometrics -- 3.2 Keyword Extraction -- 3.3 Data Procurement -- 3.4 Techniques -- 4 Discussion on Results -- 4.1 Keyword Co-occurrence Analysis -- 4.2 Cocitation Analysis -- 4.2.1 Cluster I -- 4.2.2 Cluster II -- 4.2.3 Cluster III -- 4.3 Bibliographic Coupling -- 4.3.1 Cluster I: Digital Transformation in Insurance: Challenges and Opportunities -- 4.3.2 Cluster II: Innovation and Efficiency in Insurance Operations -- 4.3.3 Cluster III: Factors Influencing Insurance Uptake and Adoption -- 4.3.4 Cluster VI: Insurance Sector Dynamics: Economic Factors and Efficiency -- 4.3.5 Cluster V: Behavioral Dynamics in Insurance Markets</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">4.3.6 Cluster VI: Market Dynamics and Risk Management in Insurance</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">This collected edition provides a comprehensive and practical roadmap for insurers, data scientists, technologists, and insurance enthusiasts alike, to navigate the data-driven revolution that is sweeping the insurance landscape</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Taneja, Sanjay</subfield><subfield code="4">edt</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Kumar, Pawan</subfield><subfield code="d">1974-</subfield><subfield code="0">(DE-588)1245184342</subfield><subfield code="4">edt</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Reepu</subfield><subfield code="4">edt</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Kukreti, Mohit</subfield><subfield code="4">edt</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Özen, Ercan</subfield><subfield code="0">(DE-588)1191946037</subfield><subfield code="4">edt</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe</subfield><subfield code="a">Taneja, Sanjay</subfield><subfield code="t">Data Alchemy in the Insurance Industry</subfield><subfield code="d">Leeds : Emerald Publishing Limited,c2025</subfield><subfield code="z">9781836085836</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1108/9781836085829</subfield><subfield code="x">Verlag</subfield><subfield code="z">URL des Erstveröffentlichers</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-55-BME</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-PQE</subfield></datafield><datafield tag="940" ind1="1" ind2=" "><subfield code="q">ZDB-55-BME24</subfield></datafield><datafield tag="943" ind1="1" ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-035439529</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://ebookcentral.proquest.com/lib/hwr/detail.action?docID=31492188</subfield><subfield code="l">DE-2070s</subfield><subfield code="p">ZDB-30-PQE</subfield><subfield code="q">HWR_PDA_PQE</subfield><subfield code="x">Aggregator</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1108/9781836085829</subfield><subfield code="l">DE-945</subfield><subfield code="p">ZDB-55-BME</subfield><subfield code="q">ZDB-55-BME24</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1108/9781836085829</subfield><subfield code="l">DE-29</subfield><subfield code="p">ZDB-55-BME</subfield><subfield code="q">UER_Paketkauf_2024</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield></record></collection> |
| id | DE-604.BV050102367 |
| illustrated | Not Illustrated |
| indexdate | 2025-03-06T15:00:50Z |
| institution | BVB |
| isbn | 9781836085829 9781836085843 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-035439529 |
| oclc_num | 1468527078 |
| open_access_boolean | |
| owner | DE-2070s DE-29 DE-945 |
| owner_facet | DE-2070s DE-29 DE-945 |
| physical | 1 Online-Ressource (XVI, 219 Seiten) |
| psigel | ZDB-55-BME ZDB-30-PQE ZDB-55-BME24 ZDB-30-PQE HWR_PDA_PQE ZDB-55-BME ZDB-55-BME24 ZDB-55-BME UER_Paketkauf_2024 |
| publishDate | 2025 |
| publishDateSearch | 2025 |
| publishDateSort | 2025 |
| publisher | Emerald Publishing |
| record_format | marc |
| spelling | Data alchemy in the insurance industry the transformative power of big data analytics edited by Sanjay Taneja, Pawan Kumar, Reepu, Mohit Kukreti and Ercan Özen First edition Leeds Emerald Publishing [2025] ©2025 1 Online-Ressource (XVI, 219 Seiten) txt rdacontent c rdamedia cr rdacarrier Cover -- Data Alchemy in the Insurance Industry -- Data Alchemy in the Insurance Industry: The Transformative Power of Big Data Analytics -- Copyright Page -- Contents -- About the Editors -- About the Contributors -- Foreword -- Preface -- Introduction of the Book -- 1. Data Alchemy in Insurance: A Catalyst for Improving Financial Inclusion Levels and Insurance Penetration -- Abstract -- 1 Introduction -- 2 Literature Review -- 3 Research Methodology -- 4 Data Analysis -- 5 Results -- 6 Implications -- 7 Conclusion -- References -- 2. Unlocking the Power of Big Data in Insurance: The Role of Data Analytics -- Abstract -- 1 Introduction -- 2 Global Insurance Industry -- 3 Big Data -- 4 Big Data Challenges -- 5 Big Data Analytics -- 6 The Role of Big Data Analytics in Insurance Transformation -- 7 The Types of Data in Insurance Industry -- 7.1 Prescriptive Data Analysis -- 7.2 Predictive Analytics -- 7.3 Diagnostic Data Analysis -- 7.4 Descriptive Data Analysis -- 8 Data Analytics in Insurance -- 8.1 Customer Acquisition and Retention -- 8.2 Risk Modeling and Pricing -- 8.3 Claims Management -- 9 Conclusion -- References -- 3. AI-Driven Personalized Risk Management in the Insurance Sector -- Abstract -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Data Analysis -- 4 Factor 1: Better Risk Management -- 5 Factor 2: Adoption and Anticipation -- 6 Factor 3: Better Customization -- 7 Conclusion -- References -- 4. Ethical Considerations in Data Analytics: Challenges, Principles, and Best Practices -- Abstract -- 1. Introduction -- 2. Key Ethical Challenges -- 2.1 Privacy and Data Protection -- 2.2 Transparency and Accountability -- 2.3 Bias and Fairness -- 3. Ethical Principles in Data Analytics -- 3.1 Respect for Individuals -- 3.2 Transparency and Accountability -- 3.3 Fairness and Equity 4. Best Practices for Ethical Data Analytics -- 4.1 Ethical Governance and Oversight -- 4.2 Data Transparency and Consent -- 4.3 Bias Detection and Mitigation -- 5. Methodology -- 6. Conclusion -- References -- 5. Analyzing Two and a Half Decades of Health Insurance and Big Data Analytics Research: A Bibliometric Study -- Abstract -- 1 Introduction -- 2 Research Methodology -- 3 Data Analysis and Interpretation -- 3.1 TheYearly Trend of Manuscripts Made for Health Insurance and Big Data Analytics -- 3.2 Top 10 Countries That Have Contributed in Publishing Manuscripts Made for Health Insurance and Big Data Analytics -- 3.3 Institutions That Are Sponsoring Projects for Manuscripts on Health Insurance and Big Data Analytics -- 3.4 The Word Cloud of the Manuscripts for Health Insurance and Big Data Analytics -- 3.5 The Thematic Map of Keywords for the Field of Health Insurance and Big Data Analytics -- 3.6 The Co-Occurrence Analysis of Keywords for the Field of Health Insurance and Big Data Analytics -- 4 Implications -- 5 Limitations and Recommendations -- 6 Conclusion -- References -- 6. Workers' Compensation in the Remote Work Era: Proactive Risk Management Through HR Policies and Data Alchemy Practices -- Abstract -- 1 Introduction -- 2 A Review of Literature -- 3 The Research Methodology -- 4 A Conceptual Model -- 5 Analysis and Discussion -- 5.1 Descriptive Statistics -- 6 Correlation Results -- 7 ANOVA -- 7.1 Regression Statistics -- 7.2 Coefficients -- 8 Conclusion -- References -- 7. The Philosopher's Stone: Applications of Data Alchemy-Customer Personalization, Profiling, and Retention -- Abstract -- 1 Introduction to Customer Personalization in Insurance -- 2 Theoretical and Foundational Understanding of Ethical and Privacy Considerations in Customer Personalization -- 2.1 Measuring and Evaluating Personalization Initiatives 2.2 Data Collection and Management for Personalization -- 2.3 Leveraging Big Data Analytics for Personalization -- 3 Objectives of the Study -- 4 Review of Literature and Hypotheses on Customer Personalization, Profiling, and Retention -- 4.1 Creating Personalized Customer Experiences -- 4.2 Understanding Customer Segmentation and Profiling -- 5 Research Methodology and Analysis -- 5.1 Impact of Personalized Customer Experiences on Customer Satisfaction -- 5.2 Impact of Customer Profiling Leads to More Effective Targeting of Marketing Efforts -- 6 Case Studies and Success Stories -- 6.1 Case Study: Progressive Insurance Snapshot Program -- 6.1.1 Background -- 6.1.2 Informed Consent -- 6.1.3 Data Ownership -- 6.1.4 Implementation -- 6.1.5 Impact -- 6.1.6 Conclusive Observation -- 6.2 Case Study: AXA Insurance DriveSave Program -- 6.2.1 Background -- 6.2.2 Informed Consent -- 6.2.3 Data Ownership -- 6.2.4 Implementation -- 6.2.5 Impact -- 6.2.6 Conclusive Observation -- 6.3 Success Story: Geico's Personalized Marketing Campaigns -- 6.3.1 Background -- 6.3.2 Data Analysis and Segmentation -- 6.3.3 Personalized Messaging -- 6.3.4 Dynamic Content Optimization -- 6.3.5 Measurable Results -- 6.3.6 Conclusive Observation -- 6.4 Success Story: Allstate's Usage-Based Insurance Program -- 6.4.1 Background -- 6.4.2 Data Collection and Analysis -- 6.4.3 Personalized Premiums -- 6.4.4 Customer Engagement and Satisfaction -- 6.4.5 Risk Management and Loss Prevention -- 6.4.6 Conclusive Observation -- 7 Challenges and Future Directions -- 8 Conclusion and Final Thoughts -- Acknowledgments -- References -- 8. Impact of Employee-Performance Data Management on Job Satisfaction in the Insurance Sector -- Abstract -- 1 Introduction -- 1.1 Data Management -- 1.2 Impact on Employee Performance -- 1.3 Role of Data Quality and Governance -- 1.4 Big Data Analytics 1.5 Psychological and Work-Life Quality Factors -- 2 Literature Review -- 3 Objectives of the Study -- 3.1 Data Integration -- 3.2 Technology Integration -- 3.3 Ethical Considerations -- 4 Research Methodology -- 4.1 Empirical Analysis -- 5 Results and Discussions -- 6 Implications of the Study -- 6.1 Theoretical Implications -- 6.2 Practical Implications -- 7 Conclusion -- References -- 9. Revolutionizing Insurance Practices Through Advanced Data Alchemy -- Abstract -- 1 Introduction -- 2 Literature Review -- 2.1 Insurance Industry -- 2.2 Data Alchemy -- 2.3 Scopus Database -- 3 Research Methodology -- 4 Findings -- 4.1 Case Study of SmartRisk Insurance Company -- 4.1.1 Introduction -- 4.1.2 Challenge -- 4.1.3 Solution -- 4.1.4 Implementation -- 4.1.5 Results -- 4.1.6 Conclusion of the Case -- 5 Implications -- 6 Limitations and Recommendations -- 7 Conclusion -- References -- 10. The Future of Alchemy: Emerging Trends and Technologies Metaverse in Insurance - A Virtual Customer Experience -- Abstract -- 1 Introduction -- 2 Future of Internet: Metaverse Simplified -- 3 Literature Review -- 4 Revolutionary Potential of Metaverse Technology in Insurance -- 4.1 Digitalization of Processes -- 4.2 Data Analytics and Predictive Modeling -- 4.3 Personalized Products and Pricing -- 4.4 Insurtech Innovation -- 4.5 Customer Engagement and Experience -- 4.6 Remote Underwriting and Claims -- 4.7 Emphasis on Cybersecurity -- 5 Application of Metaverse to Insurance -- 5.1 Enhanced Customer Experience -- 5.2 Cost Reduction and Other Sources of Income -- 5.3 Operational Excellence -- 6 Enhancing the Appeal of Insurance -- 7 Metaverse: A Dual-Edged Weapon -- 7.1 Collection of Data by Third Parties -- 7.2 Countless Privacy Concerns -- 7.3 Cybersecurity risks -- 8 Metaverse's Impact on the Insurance Value Chain -- 8.1 Generating Revenue 8.2 Engaging Customer Experiences -- 8.3 Insights Derived from Data Analysis -- 8.4 Digitalization of Operations -- 8.5 Integration of Assets -- 8.6 Interoperability Across Platforms -- 8.7 Improved Insurance Procedures -- 8.8 Imagining Situations -- 8.9 Efficient Loss Adjustment -- 8.10 Online Goods Exchange Platform -- 9 Technological Barriers in Conventional Insurance Procedures -- 9.1 Outdated Systems -- 9.2 Data Management -- 9.3 Cybersecurity -- 9.4 Automation and Process Optimization -- 9.5 Customer Expectations -- 9.6 Adherence to Regulations -- 9.7 Data Analytics -- 10 Challenges and Risks Posed by the Metaverse in the Insurance Sector -- 10.1 Cybersecurity Weaknesses -- 10.2 Legal and Intellectual Property Challenges -- 10.3 Ambiguity in Regulations -- 10.4 Data Security and Privacy -- 10.5 Emerging and Incalculable Risks -- 10.6 Technical Proficiency Requirement -- 10.7 Difficulties in Customer Adoption -- 10.8 Server Outages and Technical Issues -- 11 Conclusion -- 12. Acknowledgments -- References -- 11. Trends and Patterns in Insurance Research: A Bibliometric Analysis (2020-2024) -- Abstract -- 1 Introduction -- 2 Objectives of the Study -- 3 Methodology -- 3.1 Bibliometrics -- 3.2 Keyword Extraction -- 3.3 Data Procurement -- 3.4 Techniques -- 4 Discussion on Results -- 4.1 Keyword Co-occurrence Analysis -- 4.2 Cocitation Analysis -- 4.2.1 Cluster I -- 4.2.2 Cluster II -- 4.2.3 Cluster III -- 4.3 Bibliographic Coupling -- 4.3.1 Cluster I: Digital Transformation in Insurance: Challenges and Opportunities -- 4.3.2 Cluster II: Innovation and Efficiency in Insurance Operations -- 4.3.3 Cluster III: Factors Influencing Insurance Uptake and Adoption -- 4.3.4 Cluster VI: Insurance Sector Dynamics: Economic Factors and Efficiency -- 4.3.5 Cluster V: Behavioral Dynamics in Insurance Markets 4.3.6 Cluster VI: Market Dynamics and Risk Management in Insurance This collected edition provides a comprehensive and practical roadmap for insurers, data scientists, technologists, and insurance enthusiasts alike, to navigate the data-driven revolution that is sweeping the insurance landscape Taneja, Sanjay edt Kumar, Pawan 1974- (DE-588)1245184342 edt Reepu edt Kukreti, Mohit edt Özen, Ercan (DE-588)1191946037 edt Erscheint auch als Druck-Ausgabe Taneja, Sanjay Data Alchemy in the Insurance Industry Leeds : Emerald Publishing Limited,c2025 9781836085836 https://doi.org/10.1108/9781836085829 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Taneja, Sanjay Data alchemy in the insurance industry the transformative power of big data analytics Cover -- Data Alchemy in the Insurance Industry -- Data Alchemy in the Insurance Industry: The Transformative Power of Big Data Analytics -- Copyright Page -- Contents -- About the Editors -- About the Contributors -- Foreword -- Preface -- Introduction of the Book -- 1. Data Alchemy in Insurance: A Catalyst for Improving Financial Inclusion Levels and Insurance Penetration -- Abstract -- 1 Introduction -- 2 Literature Review -- 3 Research Methodology -- 4 Data Analysis -- 5 Results -- 6 Implications -- 7 Conclusion -- References -- 2. Unlocking the Power of Big Data in Insurance: The Role of Data Analytics -- Abstract -- 1 Introduction -- 2 Global Insurance Industry -- 3 Big Data -- 4 Big Data Challenges -- 5 Big Data Analytics -- 6 The Role of Big Data Analytics in Insurance Transformation -- 7 The Types of Data in Insurance Industry -- 7.1 Prescriptive Data Analysis -- 7.2 Predictive Analytics -- 7.3 Diagnostic Data Analysis -- 7.4 Descriptive Data Analysis -- 8 Data Analytics in Insurance -- 8.1 Customer Acquisition and Retention -- 8.2 Risk Modeling and Pricing -- 8.3 Claims Management -- 9 Conclusion -- References -- 3. AI-Driven Personalized Risk Management in the Insurance Sector -- Abstract -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Data Analysis -- 4 Factor 1: Better Risk Management -- 5 Factor 2: Adoption and Anticipation -- 6 Factor 3: Better Customization -- 7 Conclusion -- References -- 4. Ethical Considerations in Data Analytics: Challenges, Principles, and Best Practices -- Abstract -- 1. Introduction -- 2. Key Ethical Challenges -- 2.1 Privacy and Data Protection -- 2.2 Transparency and Accountability -- 2.3 Bias and Fairness -- 3. Ethical Principles in Data Analytics -- 3.1 Respect for Individuals -- 3.2 Transparency and Accountability -- 3.3 Fairness and Equity 4. Best Practices for Ethical Data Analytics -- 4.1 Ethical Governance and Oversight -- 4.2 Data Transparency and Consent -- 4.3 Bias Detection and Mitigation -- 5. Methodology -- 6. Conclusion -- References -- 5. Analyzing Two and a Half Decades of Health Insurance and Big Data Analytics Research: A Bibliometric Study -- Abstract -- 1 Introduction -- 2 Research Methodology -- 3 Data Analysis and Interpretation -- 3.1 TheYearly Trend of Manuscripts Made for Health Insurance and Big Data Analytics -- 3.2 Top 10 Countries That Have Contributed in Publishing Manuscripts Made for Health Insurance and Big Data Analytics -- 3.3 Institutions That Are Sponsoring Projects for Manuscripts on Health Insurance and Big Data Analytics -- 3.4 The Word Cloud of the Manuscripts for Health Insurance and Big Data Analytics -- 3.5 The Thematic Map of Keywords for the Field of Health Insurance and Big Data Analytics -- 3.6 The Co-Occurrence Analysis of Keywords for the Field of Health Insurance and Big Data Analytics -- 4 Implications -- 5 Limitations and Recommendations -- 6 Conclusion -- References -- 6. Workers' Compensation in the Remote Work Era: Proactive Risk Management Through HR Policies and Data Alchemy Practices -- Abstract -- 1 Introduction -- 2 A Review of Literature -- 3 The Research Methodology -- 4 A Conceptual Model -- 5 Analysis and Discussion -- 5.1 Descriptive Statistics -- 6 Correlation Results -- 7 ANOVA -- 7.1 Regression Statistics -- 7.2 Coefficients -- 8 Conclusion -- References -- 7. The Philosopher's Stone: Applications of Data Alchemy-Customer Personalization, Profiling, and Retention -- Abstract -- 1 Introduction to Customer Personalization in Insurance -- 2 Theoretical and Foundational Understanding of Ethical and Privacy Considerations in Customer Personalization -- 2.1 Measuring and Evaluating Personalization Initiatives 2.2 Data Collection and Management for Personalization -- 2.3 Leveraging Big Data Analytics for Personalization -- 3 Objectives of the Study -- 4 Review of Literature and Hypotheses on Customer Personalization, Profiling, and Retention -- 4.1 Creating Personalized Customer Experiences -- 4.2 Understanding Customer Segmentation and Profiling -- 5 Research Methodology and Analysis -- 5.1 Impact of Personalized Customer Experiences on Customer Satisfaction -- 5.2 Impact of Customer Profiling Leads to More Effective Targeting of Marketing Efforts -- 6 Case Studies and Success Stories -- 6.1 Case Study: Progressive Insurance Snapshot Program -- 6.1.1 Background -- 6.1.2 Informed Consent -- 6.1.3 Data Ownership -- 6.1.4 Implementation -- 6.1.5 Impact -- 6.1.6 Conclusive Observation -- 6.2 Case Study: AXA Insurance DriveSave Program -- 6.2.1 Background -- 6.2.2 Informed Consent -- 6.2.3 Data Ownership -- 6.2.4 Implementation -- 6.2.5 Impact -- 6.2.6 Conclusive Observation -- 6.3 Success Story: Geico's Personalized Marketing Campaigns -- 6.3.1 Background -- 6.3.2 Data Analysis and Segmentation -- 6.3.3 Personalized Messaging -- 6.3.4 Dynamic Content Optimization -- 6.3.5 Measurable Results -- 6.3.6 Conclusive Observation -- 6.4 Success Story: Allstate's Usage-Based Insurance Program -- 6.4.1 Background -- 6.4.2 Data Collection and Analysis -- 6.4.3 Personalized Premiums -- 6.4.4 Customer Engagement and Satisfaction -- 6.4.5 Risk Management and Loss Prevention -- 6.4.6 Conclusive Observation -- 7 Challenges and Future Directions -- 8 Conclusion and Final Thoughts -- Acknowledgments -- References -- 8. Impact of Employee-Performance Data Management on Job Satisfaction in the Insurance Sector -- Abstract -- 1 Introduction -- 1.1 Data Management -- 1.2 Impact on Employee Performance -- 1.3 Role of Data Quality and Governance -- 1.4 Big Data Analytics 1.5 Psychological and Work-Life Quality Factors -- 2 Literature Review -- 3 Objectives of the Study -- 3.1 Data Integration -- 3.2 Technology Integration -- 3.3 Ethical Considerations -- 4 Research Methodology -- 4.1 Empirical Analysis -- 5 Results and Discussions -- 6 Implications of the Study -- 6.1 Theoretical Implications -- 6.2 Practical Implications -- 7 Conclusion -- References -- 9. Revolutionizing Insurance Practices Through Advanced Data Alchemy -- Abstract -- 1 Introduction -- 2 Literature Review -- 2.1 Insurance Industry -- 2.2 Data Alchemy -- 2.3 Scopus Database -- 3 Research Methodology -- 4 Findings -- 4.1 Case Study of SmartRisk Insurance Company -- 4.1.1 Introduction -- 4.1.2 Challenge -- 4.1.3 Solution -- 4.1.4 Implementation -- 4.1.5 Results -- 4.1.6 Conclusion of the Case -- 5 Implications -- 6 Limitations and Recommendations -- 7 Conclusion -- References -- 10. The Future of Alchemy: Emerging Trends and Technologies Metaverse in Insurance - A Virtual Customer Experience -- Abstract -- 1 Introduction -- 2 Future of Internet: Metaverse Simplified -- 3 Literature Review -- 4 Revolutionary Potential of Metaverse Technology in Insurance -- 4.1 Digitalization of Processes -- 4.2 Data Analytics and Predictive Modeling -- 4.3 Personalized Products and Pricing -- 4.4 Insurtech Innovation -- 4.5 Customer Engagement and Experience -- 4.6 Remote Underwriting and Claims -- 4.7 Emphasis on Cybersecurity -- 5 Application of Metaverse to Insurance -- 5.1 Enhanced Customer Experience -- 5.2 Cost Reduction and Other Sources of Income -- 5.3 Operational Excellence -- 6 Enhancing the Appeal of Insurance -- 7 Metaverse: A Dual-Edged Weapon -- 7.1 Collection of Data by Third Parties -- 7.2 Countless Privacy Concerns -- 7.3 Cybersecurity risks -- 8 Metaverse's Impact on the Insurance Value Chain -- 8.1 Generating Revenue 8.2 Engaging Customer Experiences -- 8.3 Insights Derived from Data Analysis -- 8.4 Digitalization of Operations -- 8.5 Integration of Assets -- 8.6 Interoperability Across Platforms -- 8.7 Improved Insurance Procedures -- 8.8 Imagining Situations -- 8.9 Efficient Loss Adjustment -- 8.10 Online Goods Exchange Platform -- 9 Technological Barriers in Conventional Insurance Procedures -- 9.1 Outdated Systems -- 9.2 Data Management -- 9.3 Cybersecurity -- 9.4 Automation and Process Optimization -- 9.5 Customer Expectations -- 9.6 Adherence to Regulations -- 9.7 Data Analytics -- 10 Challenges and Risks Posed by the Metaverse in the Insurance Sector -- 10.1 Cybersecurity Weaknesses -- 10.2 Legal and Intellectual Property Challenges -- 10.3 Ambiguity in Regulations -- 10.4 Data Security and Privacy -- 10.5 Emerging and Incalculable Risks -- 10.6 Technical Proficiency Requirement -- 10.7 Difficulties in Customer Adoption -- 10.8 Server Outages and Technical Issues -- 11 Conclusion -- 12. Acknowledgments -- References -- 11. Trends and Patterns in Insurance Research: A Bibliometric Analysis (2020-2024) -- Abstract -- 1 Introduction -- 2 Objectives of the Study -- 3 Methodology -- 3.1 Bibliometrics -- 3.2 Keyword Extraction -- 3.3 Data Procurement -- 3.4 Techniques -- 4 Discussion on Results -- 4.1 Keyword Co-occurrence Analysis -- 4.2 Cocitation Analysis -- 4.2.1 Cluster I -- 4.2.2 Cluster II -- 4.2.3 Cluster III -- 4.3 Bibliographic Coupling -- 4.3.1 Cluster I: Digital Transformation in Insurance: Challenges and Opportunities -- 4.3.2 Cluster II: Innovation and Efficiency in Insurance Operations -- 4.3.3 Cluster III: Factors Influencing Insurance Uptake and Adoption -- 4.3.4 Cluster VI: Insurance Sector Dynamics: Economic Factors and Efficiency -- 4.3.5 Cluster V: Behavioral Dynamics in Insurance Markets 4.3.6 Cluster VI: Market Dynamics and Risk Management in Insurance |
| title | Data alchemy in the insurance industry the transformative power of big data analytics |
| title_auth | Data alchemy in the insurance industry the transformative power of big data analytics |
| title_exact_search | Data alchemy in the insurance industry the transformative power of big data analytics |
| title_full | Data alchemy in the insurance industry the transformative power of big data analytics edited by Sanjay Taneja, Pawan Kumar, Reepu, Mohit Kukreti and Ercan Özen |
| title_fullStr | Data alchemy in the insurance industry the transformative power of big data analytics edited by Sanjay Taneja, Pawan Kumar, Reepu, Mohit Kukreti and Ercan Özen |
| title_full_unstemmed | Data alchemy in the insurance industry the transformative power of big data analytics edited by Sanjay Taneja, Pawan Kumar, Reepu, Mohit Kukreti and Ercan Özen |
| title_short | Data alchemy in the insurance industry |
| title_sort | data alchemy in the insurance industry the transformative power of big data analytics |
| title_sub | the transformative power of big data analytics |
| url | https://doi.org/10.1108/9781836085829 |
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