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More About This Title Analytics for Insurance - The Real Business of Big Data
English
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.
English
TONY BOOBIER is a worldwide executive at IBM focussing on the insurance industry. With over 30 years of experience, he is a frequent writer and international public speaker. As author of numerous articles on a wide range of topics ranging from claims management to analytical insight, he possesses a deep understanding of the application of business intelligence and analytics in the international insurance industry and holds a successful track record conceiving and introducing changes in the operations and management of national service and delivery organizations.
English
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
