Analytics for Insurance

"..essential reading for insurance management of all levels and specialities, for students, and for IT suppliers to the insurance industry." (Only Strategic, December 2016)

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.

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