The business guide to Big Data in insurance, with practical application insight Big Data and Analytics for Insurers is the industry-specific guide to creating operational effectiveness, managing risk, improving financials, and retaining customers. Written from a non-IT perspective, this book focusses less on the architecture and technical details, instead providing practical guidance on translating analytics into target delivery. The discussion examines implementation, interpretation, and application to show you what Big Data can do for your business, with insights and examples targeted specifically to the insurance industry. From fraud analytics in claims management, to customer analytics, to risk analytics in Solvency 2, comprehensive coverage presented in accessible language makes this guide an invaluable resource for any insurance professional. The insurance industry is heavily dependent on data, and the advent of Big Data and analytics represents a major advance with tremendous potential yet clear, practical advice on the business side of analytics is lacking. This book fills the void with concrete information on using Big Data in the context of day-to-day insurance operations and strategy. * Understand what Big Data is and what it can do * Delve into Big Data's specific impact on the insurance industry * Learn how advanced analytics can revolutionise the industry * Bring Big Data out of IT and into strategy, management, marketing, and more Big Data and analytics is changing business but how? The majority of Big Data guides discuss data collection, database administration, advanced analytics, and the power of Big Data but what do you actually do with it? Big Data and Analytics for Insurers answers your questions in real, everyday business terms, tailored specifically to the insurance industry's unique needs, challenges, and targets.
Preface xi Acknowledgements xiii About the Author xv CHAPTER 1 Introduction The New Real Business 1 1.1 On the Point of Transformation 2 1.1.1 Big Data Defined by Its Characteristics 3 1.1.2 The Hierarchy of Analytics, and how Value is Obtained from Data 6 1.1.3 Next Generation Analytics 7 1.1.4 Between the Data and the Analytics 9 1.2 Big Data and Analytics for All Insurers 10 1.2.1 Three Key Imperatives 10 1.2.2 The Role of Intermediaries 13 1.2.3 Geographical Perspectives 14 1.2.4 Analytics and the Internet of Things 15 1.2.5 Scale Benefit or Size Disadvantage? 15 1.3 How Do Analytics Actually Work? 17 1.3.1 Business Intelligence 18 1.3.2 Predictive Analytics 20 1.3.3 Prescriptive Analytics 22 1.3.4 Cognitive Computing 23 Notes 24 CHAPTER 2 Analytics and the Office of Finance 25 2.1 The Challenges of Finance 26 2.2 Performance Management and Integrated Decision-making 27 2.3 Finance and Insurance 27 2.4 Reporting and Regulatory Disclosure 29 2.5 GAAP and IFRS 29 2.6 Mergers, Acquisitions, and Divestments 30 2.7 Transparency, Misrepresentation, The Securities Act and SOX 31 2.8 Social Media and Financial Analytics 32 2.9 Sales Management and Distribution Channels 33 2.9.1 Agents and Producers 34 2.9.2 Distribution Management 35 Notes 36 CHAPTER 3 Managing Financial Risk across the Insurance Enterprise 37 3.1 Solvency II 37 3.2 Solvency II, Cloud Computing and Shared Services 40 3.3 Sweating the Assets 40 3.4 Solvency II and IFRS 41 3.5 The Changing Role of the CRO 42 3.6 CRO as the Customer Advocate 45 3.7 Analytics and the Challenge of Unpredictability 45 3.8 The Importance of Reinsurance 46 3.9 Risk Adjusted Decision-Making 46 Notes 49 CHAPTER 4 Underwriting 51 4.1 Underwriting and Big Data 52 4.2 Underwriting for Specialist Lines 54 4.3 Telematics and User-Based Insurance as an Underwriting Tool 55 4.4 Underwriting for Fraud Avoidance 56 4.5 Analytics and Building Information Management (BIM) 57 Notes 58 CHAPTER 5 Claims and the Moment of Truth 61 5.1 Indemnity and the Contractual Entitlement 61 5.2 Claims Fraud 62 5.2.1 Opportunistic Fraud 63 5.2.2 Organized Fraud 64 5.3 Property Repairs and Supply Chain Management 66 5.4 Auto Repairs 71 5.5 Transforming the Handling of Complex Domestic Claims 73 5.5.1 The Digital Investigator 73 5.5.2 Potential Changes in the Claims Process 75 5.5.3 Reinvention of the Supplier Ecosystem 76 5.6 Levels of Inspection 77 5.6.1 Reserving 78 5.6.2 Business Interruption 79 5.6.3 Subrogation 80 5.7 Motor Assessing and Loss Adjusting 81 5.7.1 Motor Assessing 82 5.7.2 Loss Adjusting 83 5.7.3 Property Claims Networks 84 5.7.4 Adjustment of Cybersecurity Claims 87 5.7.5 The Demographic Time Bomb in Adjusting 87 Notes 88 CHAPTER 6 Analytics and Marketing 91 6.1 Customer Acquisition and Retention 93 6.2 Social Media Analytics 96 6.3 Demography and How Population Matters 97 6.4 Segmentation 98 6.5 Promotion Strategy 100 6.6 Branding and Pricing 100 6.7 Pricing Optimization 101 6.8 The Impact of Service Delivery on Marketing Success 102 6.9 Agile Development of New Products 103 6.10 The Challenge of Agility 104 6.11 Agile vs Greater Risk? 105 6.12 The Digital Customer, Multi- and Omni-Channel 105 6.13 The Importance of the Claims Service in Marketing 106 Notes 107 CHAPTER 7 Property Insurance 109 7.1 Flood 109 7.1.1 Predicting the Cost and Likelihood of Flood Damage 110 7.1.2 Analytics and the Drying Process 111 7.2 Fire 112 7.2.1 Predicting Fraud in Fire Claims 113 7.3 Subsidence 115 7.3.1 Prediction of Subsidence 116 7.4 Hail 119 7.4.1 Prediction of Hail Storms 120 7.5 Hurricane 121 7.5.1 Prediction of Hurricane Damage 121 7.6 Terrorism 122 7.6.1 Predicting Terrorism Damage 123 7.7 Claims Process and the Digital Customer 124 Notes 125 CHAPTER 8 Liability Insurance and Analytics 127 8.1 Employers Liability and Workers Compensation 127 8.1.1 Fraud in Workers Compensation Claims 128 8.1.2 Employers Liability Cover 130 8.1.3 Effective Triaging of EL Claims 130 8.2 Public Liability 131 8.3 Product Liability 132 8.4 Directors and Officers Liability 133 Notes 134 CHAPTER 9 Life and Pensions 135 9.1 How Life Insurance Differs from General Insurance 136 9.2 Basis of Life Insurance 137 9.3 Issues of Mortality 138 9.4 The Role of Big Data in Mortality Rates 139 9.5 Purchasing Life Insurance in a Volatile Economy 140 9.6 How Life Insurers Can Engage with the Young 141 9.7 Life and Pensions for the Older Demographic 142 9.8 Life and Pension Benefits in the Digital Era 143 9.9 Life Insurance and Bancassurers 145 Notes 147 CHAPTER 10 The Importance of Location 149 10.1 Location Analytics 149 10.1.1 The New Role of the Geo-Location Expert 149 10.1.2 Sharing Location Information 150 10.1.3 Geocoding 150 10.1.4 Location Analytics in Fraud Investigation 151 10.1.5 Location Analytics in Terrorism Risk 152 10.1.6 Location Analytics and Flooding 152 10.1.7 Location Analytics, Cargo and Theft 154 10.2 Telematics and User-Based Insurance ( UBI ) 155 10.2.1 History of Telematics 155 10.2.2 Telematics in Fraud Detection 157 10.2.3 What is the Impact on Motor Insurers? 157 10.2.4 Telematics and Vehicle Dashboard Design 158 10.2.5 Telematics and Regulation 159 10.2.6 Telematics More than Technology 160 10.2.7 User-Based Insurance in Other Areas 161 10.2.8 Telematics in Commercial Insurances 162 Notes 164 CHAPTER 11 Analytics and Insurance People 167 11.1 Talent Management 167 11.1.1 The Need for New Competences 168 11.1.2 Essential Qualities and Capabilities 169 11.2 Talent, Employment and the Future of Insurance 173 11.2.1 Talent Analytics and the Challenge for Human Resources 173 11.3 Learning and Knowledge Transfer 174 11.3.1 Reading Materials 175 11.3.2 Formal Qualifications and Structured Learning 175 11.3.3 Face-to-Face Training 176 11.3.4 Social Media and Technology 177 11.4 Leadership and Insurance Analytics 178 11.4.1 Knowledge and Power 179 11.4.2 Leadership and Influence 179 11.4.3 Analytics and the Impact on Employees 181 11.4.4 Understanding Employee Resistance 182 Notes 184 CHAPTER 12 Implementation 185 12.1 Culture and Organization 188 12.1.1 Communication and Evangelism 192 12.1.2 Stakeholders Vision of the Future 193 12.2 Creating a Strategy 193 12.2.1 Program Sponsorship 194 12.2.2 Building a Project Program 195 12.2.3 Stakeholder Management 197 12.2.4 Recognizing Analytics as a Tool of Empowerment 198 12.2.5 Creation of Open and Trusting Relationships 199 12.2.6 Developing a Roadmap 200 12.2.7 Implementation Flowcharts 202 12.3 Managing the Data 202 12.3.1 Master Data Management 203 12.3.2 Data Governance 203 12.3.3 Data Quality 204 12.3.4 Data Standardization 204 12.3.5 Storing and Managing Data 205 12.3.6 Security 207 12.4 Tooling and Skillsets 207 12.4.1 Certification and Qualifications 208 12.4.2 Competences 208 Notes 209 CHAPTER 13 Visions of the Future? 211 13.1 Auto 2025 211 13.2 The Digital Home in 2025 Property Telematics 214 13.3 Commercial Insurance Analytically Transformed 218 13.4 Specialist Risks and Deeper Insight 220 13.5 2025: Transformation of the Life and Pensions Industry 221 13.6 Outsourcing and the Move Away from Non-Core Activities 223 13.7 The Rise of the Super Supplier 224 Notes 225 CHAPTER 14 Conclusions and Reflections 227 14.1 The Breadth of the Challenge 229 14.2 Final Thoughts 230 Notes 231 APPENDIX A Recommended Reading 233 APPENDIX B Data Summary of Expectancy of Reaching 100 235 APPENDIX C Implementation Flowcharts 239 APPENDIX D Suggested Insurance Websites 265 APPENDIX E Professional Insurance Organizations 267 Index 269
(0 Comentarios)
