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E-grāmata: Analytics for Insurance: The Real Business of Big Data

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  • Formāts: PDF+DRM
  • Sērija : The Wiley Finance Series
  • Izdošanas datums: 01-Aug-2016
  • Izdevniecība: John Wiley & Sons Inc
  • Valoda: eng
  • ISBN-13: 9781119141099
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Analytics for Insurance: The Real Business of Big DataPalielināt
  • Formāts: PDF+DRM
  • Sērija : The Wiley Finance Series
  • Izdošanas datums: 01-Aug-2016
  • Izdevniecība: John Wiley & Sons Inc
  • Valoda: eng
  • ISBN-13: 9781119141099
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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.

Recenzijas

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

Preface xiii
Acknowledgements xv
About the Author xvii
Chapter 1 Introduction -- The New `Real Business'
1(24)
1.1 On the Point of Transformation
2(8)
1.1.1 Big Data Defined by Its Characteristics
3(3)
1.1.2 The Hierarchy of Analytics, and How Value is Obtained from Data
6(1)
1.1.3 Next Generation Analytics
7(2)
1.1.4 Between the Data and the Analytics
9(1)
1.2 Big Data and Analytics for All Insurers
10(7)
1.2.1 Three Key Imperatives
10(3)
1.2.2 The Role of Intermediaries
13(1)
1.2.3 Geographical Perspectives
14(1)
1.2.4 Analytics and the Internet of Things
15(1)
1.2.5 Scale Benefit -- or Size Disadvantage?
15(2)
1.3 How Do Analytics Actually Work?
17(8)
1.3.1 Business Intelligence
18(2)
1.3.2 Predictive Analytics
20(2)
1.3.3 Prescriptive Analytics
22(1)
1.3.4 Cognitive Computing
23(1)
Notes
24(1)
Chapter 2 Analytics and the Office of Finance
25(12)
2.1 The Challenges of Finance
26(1)
2.2 Performance Management and Integrated Decision-Making
27(1)
2.3 Finance and Insurance
27(2)
2.4 Reporting and Regulatory Disclosure
29(1)
2.5 GAAP and IFRS
29(1)
2.6 Mergers, Acquisitions, and Divestments
30(1)
2.7 Transparency, Misrepresentation, the Securities Act and `SOX'
31(1)
2.8 Social Media and Financial Analytics
32(1)
2.9 Sales Management and Distribution Channels
33(4)
2.9.1 Agents and Producers
34(1)
2.9.2 Distribution Management
35(1)
Notes
36(1)
Chapter 3 Managing Financial Risk Across the Insurance Enterprise
37(14)
3.1 Solvency II
37(3)
3.2 Solvency II, Cloud Computing and Shared Services
40(1)
3.3 `Sweating the Assets'
40(1)
3.4 Solvency II and IFRS
41(1)
3.5 The Changing Role of the CRO
42(3)
3.6 CRO as Customer Advocate
45(1)
3.7 Analytics and the Challenge of Unpredictability
45(1)
3.8 The Importance of Reinsurance
46(1)
3.9 Risk Adjusted Decision-Making
46(5)
Notes
49(2)
Chapter 4 Underwriting
51(10)
4.1 Underwriting and Big Data
52(2)
4.2 Underwriting for Specialist Lines
54(1)
4.3 Telematics and User-Based Insurance as an Underwriting Tool
55(1)
4.4 Underwriting for Fraud Avoidance
56(1)
4.5 Analytics and Building Information Management (BIM)
57(4)
Notes
58(3)
Chapter 5 Claims and the "Moment of Truth'
61(30)
5.1 `Indemnity' and the Contractual Entitlement
61(1)
5.2 Claims Fraud
62(4)
5.2.1 Opportunistic Fraud
63(1)
5.2.2 Organized Fraud
64(2)
5.3 Property Repairs and Supply Chain Management
66(5)
5.4 Auto Repairs
71(2)
5.5 Transforming the Handling of Complex Domestic Claims
73(4)
5.5.1 The Digital Investigator
73(2)
5.5.2 Potential Changes in the Claims Process
75(1)
5.5.3 Reinvention of the Supplier Ecosystem
76(1)
5.6 Levels of Inspection
77(4)
5.6.1 Reserving
78(1)
5.6.2 Business Interruption
79(1)
5.6.3 Subrogation
80(1)
5.7 Motor Assessing and Loss Adjusting
81(10)
5.7.1 Motor Assessing
82(1)
5.7.2 Loss Adjusting
83(1)
5.7.3 Property Claims Networks
84(3)
5.7.4 Adjustment of Cybersecurity Claims
87(1)
5.7.5 The Demographic Time Bomb in Adjusting
87(1)
Notes
88(3)
Chapter 6 Analytics and Marketing
91(18)
6.1 Customer Acquisition and Retention
93(3)
6.2 Social Media Analytics
96(1)
6.3 Demography and How Population Matters
97(1)
6.4 Segmentation
98(2)
6.5 Promotion Strategy
100(1)
6.6 Branding and Pricing
100(1)
6.7 Pricing Optimization
101(1)
6.8 The Impact of Service Delivery on Marketing Success
102(1)
6.9 Agile Development of New Products
103(1)
6.10 The Challenge of `Agility'
104(1)
6.11 Agile vs Greater Risk?
105(1)
6.12 The Digital Customer, Multi-and Omni-Channel
105(1)
6.13 The Importance of the Claims Service in Marketing
106(3)
Notes
107(2)
Chapter 7 Property Insurance
109(18)
7.1 Flood
109(3)
7.1.1 Predicting the Cost and Likelihood of Flood Damage
110(1)
7.1.2 Analytics and the Drying Process
111(1)
7.2 Fire
112(3)
7.2.1 Predicting Fraud in Fire Claims
113(2)
7.3 Subsidence
115(4)
7.3.1 Prediction of Subsidence
116(3)
7.4 Hail
119(2)
7.4.1 Prediction of Hail Storms
120(1)
7.5 Hurricane
121(1)
7.5.1 Prediction of Hurricane Damage
121(1)
7.6 Terrorism
122(2)
7.6.1 Predicting Terrorism Damage
123(1)
7.7 Claims Process and the `Digital Customer'
124(3)
Notes
125(2)
Chapter 9 Liability Insurance and Analytics
127(8)
8.1 Employers `Liability and Workers' Compensation
127(4)
8.1.1 Fraud in Workers' Compensation Claims
128(2)
8.1.2 Employers' Liability Cover
130(1)
8.1.3 Effective Triaging of EL Claims
130(1)
8.2 Public Liability
131(1)
8.3 Product Liability
132(1)
8.4 Directors and Officers Liability
133(2)
Notes
134(1)
Chapter 9 Life and Pensions
135(14)
9.1 How Life Insurance Differs from General Insurance
136(1)
9.2 Basis of Life Insurance
137(1)
9.3 Issues of Mortality
138(1)
9.4 The Role of Big Data in Mortality Rates
139(1)
9.5 Purchasing Life Insurance in a Volatile Economy
140(1)
9.6 How Life Insurers Can Engage with the Young
141(1)
9.7 Life and Pensions for the Older Demographic
142(1)
9.8 Life and Pension Benefits in the Digital Era
143(2)
9.9 Life Insurance and Bancassurers
145(4)
Notes
147(2)
Chapter 10 The Importance of Location
149(18)
10.1 Location Analytics
149(6)
10.1.1 The New Role of the Geo-Location Expert
149(1)
10.1.2 Sharing Location Information
150(1)
10.1.3 Geocoding
150(1)
10.1.4 Location Analytics in Fraud Investigation
151(1)
10.1.5 Location Analytics in Terrorism Risk
152(1)
10.1.6 Location Analytics and Flooding
152(2)
10.1.7 Location Analytics, Cargo and Theft
154(1)
10.2 Telematics and User-Based Insurance (`UBI')
155(12)
10.2.1 History of Telematics
155(2)
10.2.2 Telematics in Fraud Detection
157(1)
10.2.3 What is the Impact on Motor Insurers?
157(1)
10.2.4 Telematics and Vehicle Dashboard Design
158(1)
10.2.5 Telematics and Regulation
159(1)
10.2.6 Telematics -- More Than Technology
160(1)
10.2.7 User-Based Insurance in Other Areas
161(1)
10.2.8 Telematics in Commercial Insurances
162(2)
Notes
164(3)
Chapter 11 Analytics and Insurance People
167(18)
11.1 Talent Management
167(6)
11.1.1 The Need for New Competences
168(1)
11.1.2 Essential Qualities and Capabilities
169(4)
11.2 Talent, Employment and the Future of Insurance
173(1)
11.2.1 Talent Analytics and the Challenge for Human Resources
173(1)
11.3 Learning and Knowledge Transfer
174(4)
11.3.1 Reading Materials
175(1)
11.3.2 Formal Qualifications and Structured Learning
175(1)
11.3.3 Face-to-Face Training
176(1)
11.3.4 Social Media and Technology
177(1)
11.4 Leadership and Insurance Analytics
178(7)
11.4.1 Knowledge and Power
179(1)
11.4.2 Leadership and Influence
179(2)
11.4.3 Analytics and the Impact on Employees
181(1)
11.4.4 Understanding Employee Resistance
182(2)
Notes
184(1)
Chapter 12 Implementation
185(26)
12.1 Culture and Organization
188(5)
12.1.1 Communication and Evangelism
192(1)
12.1.2 Stakeholders' Vision of the Future
193(1)
12.2 Creating a Strategy
193(9)
12.2.1 Program Sponsorship
194(1)
12.2.2 Building a Project Program
195(2)
12.2.3 Stakeholder Management
197(1)
12.2.4 Recognizing Analytics as a Tool of Empowerment
198(1)
12.2.5 Creation of Open and Trusting Relationships
199(1)
12.2.6 Developing a Roadmap
200(2)
12.2.7 Implementation Flowcharts
202(1)
12.3 Managing the Data
202(5)
12.3.1 Master Data Management
203(1)
12.3.2 Data Governance
203(1)
12.3.3 Data Quality
204(1)
12.3.4 Data Standardization
204(1)
12.3.5 Storing and Managing Data
205(2)
12.3.6 Security
207(1)
12.4 Tooling and Skillsets
207(4)
12.4.1 Certification and Qualifications
208(1)
12.4.2 Competences
208(1)
Notes
209(2)
Chapter 13 Visions of the Future?
211(16)
13.1 Auto 2025 21113.2 The Digital Home in 2025 -- `Property Telematics'
214(4)
13.3 Commercial Insurance -- Analytically Transformed
218(2)
13.4 Specialist Risks and Deeper Insight
220(1)
13.5 2025: Transformation of the Life and Pensions Industry
221(2)
13.6 Outsourcing and the Move Away from Non-Core Activities
223(1)
13.7 The Rise of the Super Supplier
224(3)
Notes
225(2)
Chapter 14 Conclusions and Reflections
227(6)
14.1 The Breadth of the Challenge
229(1)
14.2 Final Thoughts
230(3)
Notes
231(2)
Appendix A Recommended Reading 233(2)
Appendix B Data Summary of Expectancy of Reaching 100 235(4)
Appendix C Implementation Flowcharts 239(35)
Appendix D Suggested Insurance Websites 265(2)
Appendix E Professional Insurance Organizations 267(1)
Index 268
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.
Biežāk uzdotie jautājumi par e-grāmatām Biežāk uzdotie jautājumi par e-grāmatām
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