Gespeichert in:
| Beteilige Person: | |
|---|---|
| Weitere beteiligte Personen: | , , , |
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Leeds
Emerald Publishing
2025
|
| Ausgabe: | First edition |
| Links: | https://www.emerald.com/insight/publication/doi/10.1108/9781836085829 https://www.emerald.com/insight/publication/doi/10.1108/9781836085829 https://ebookcentral.proquest.com/lib/hwr/detail.action?docID=31492188 https://doi.org/10.1108/9781836085829 https://doi.org/10.1108/9781836085829 https://doi.org/10.1108/9781836085829 |
| 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 |
| Umfang: | 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 (Graphic Era Deemed to be University, India), Pawan Kumar (Chandigarh University India), Reepu (Chandigarh University India), Mohit Kukreti (University of Technology and Apllied Sciences-Ibri, Sultanate of Oman) and Ercan Özen (University of Uşak, Turkey) |
| 250 | |a First edition | ||
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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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| 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 |
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| 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 |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>00000nam a2200000zc 4500</leader><controlfield tag="001">BV050102367</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20250414</controlfield><controlfield tag="007">cr|uuu---uuuuu</controlfield><controlfield tag="008">241218s2025 xx o|||| 00||| eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781836085829</subfield><subfield code="c">Online, pdf</subfield><subfield code="9">978-1-83608-582-9</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781836085843</subfield><subfield code="c">Epub</subfield><subfield code="9">978-1-83608-584-3</subfield></datafield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1108/9781836085829</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ZDB-30-PQE)EBC31492188</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ZDB-30-PAD)EBC31492188</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ZDB-89-EBL)EBL31492188</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1468527078</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV050102367</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-2070s</subfield><subfield code="a">DE-29</subfield><subfield code="a">DE-945</subfield><subfield code="a">DE-863</subfield><subfield code="a">DE-862</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">368</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Data alchemy in the insurance industry</subfield><subfield code="b">the transformative power of big data analytics</subfield><subfield code="c">edited by Sanjay Taneja (Graphic Era Deemed to be University, India), Pawan Kumar (Chandigarh University India), Reepu (Chandigarh University India), Mohit Kukreti (University of Technology and Apllied Sciences-Ibri, Sultanate of Oman) and Ercan Özen (University of Uşak, Turkey)</subfield></datafield><datafield tag="250" ind1=" " ind2=" "><subfield code="a">First edition</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Leeds</subfield><subfield code="b">Emerald Publishing</subfield><subfield code="c">2025</subfield></datafield><datafield tag="264" ind1=" " ind2="4"><subfield code="c">©2025</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (xvi, 219 Seiten)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="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</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">978-1-83608-583-6</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://www.emerald.com/insight/publication/doi/10.1108/9781836085829</subfield><subfield code="l">DE-863</subfield><subfield code="p">ZDB-55-BME</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://www.emerald.com/insight/publication/doi/10.1108/9781836085829</subfield><subfield code="l">DE-862</subfield><subfield code="p">ZDB-55-BME</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</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 | 2025-04-14T10:01:08Z |
| institution | BVB |
| isbn | 9781836085829 9781836085843 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-035439529 |
| oclc_num | 1468527078 |
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| owner | DE-2070s DE-29 DE-945 DE-863 DE-BY-FWS DE-862 DE-BY-FWS |
| owner_facet | DE-2070s DE-29 DE-945 DE-863 DE-BY-FWS DE-862 DE-BY-FWS |
| 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 (Graphic Era Deemed to be University, India), Pawan Kumar (Chandigarh University India), Reepu (Chandigarh University India), Mohit Kukreti (University of Technology and Apllied Sciences-Ibri, Sultanate of Oman) and Ercan Özen (University of Uşak, Turkey) 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 978-1-83608-583-6 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 (Graphic Era Deemed to be University, India), Pawan Kumar (Chandigarh University India), Reepu (Chandigarh University India), Mohit Kukreti (University of Technology and Apllied Sciences-Ibri, Sultanate of Oman) and Ercan Özen (University of Uşak, Turkey) |
| title_fullStr | Data alchemy in the insurance industry the transformative power of big data analytics edited by Sanjay Taneja (Graphic Era Deemed to be University, India), Pawan Kumar (Chandigarh University India), Reepu (Chandigarh University India), Mohit Kukreti (University of Technology and Apllied Sciences-Ibri, Sultanate of Oman) and Ercan Özen (University of Uşak, Turkey) |
| title_full_unstemmed | Data alchemy in the insurance industry the transformative power of big data analytics edited by Sanjay Taneja (Graphic Era Deemed to be University, India), Pawan Kumar (Chandigarh University India), Reepu (Chandigarh University India), Mohit Kukreti (University of Technology and Apllied Sciences-Ibri, Sultanate of Oman) and Ercan Özen (University of Uşak, Turkey) |
| 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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