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- Herausgeber: John Wiley & Sons
- Kategorie: Fachliteratur
- Serie: Wiley Finance Series
- Sprache: Englisch
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.
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Seitenzahl: 618
Veröffentlichungsjahr: 2016
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Contents
Cover
Series Page
Title Page
Copyright
Preface
Acknowledgements
About the Author
Chapter 1: Introduction – The New ‘Real Business’
1.1 On the Point of Transformation
1.2 Big Data and Analytics for All Insurers
1.3 How Do Analytics Actually Work?
Notes
Chapter 2: Analytics and the Office of Finance
2.1 The Challenges of Finance
2.2 Performance Management and Integrated Decision-Making
2.3 Finance and Insurance
2.4 Reporting and Regulatory Disclosure
2.5 GAAP and IFRS
2.6 Mergers, Acquisitions and Divestments
2.7 Transparency, Misrepresentation, The Securities Act and ‘SOX’
2.8 Social Media and Financial Analytics
2.9 Sales Management and Distribution Channels
Notes
Chapter 3: Managing Financial Risk Across the Insurance Enterprise
3.1 Solvency II
3.2 Solvency II, Cloud Computing and Shared Services
3.3 ‘Sweating the Assets’
3.4 Solvency II and IFRS
3.5 The Changing Role of the CRO
3.6 CRO as Customer Advocate
3.7 Analytics and the Challenge of Unpredictability
3.8 The Importance of Reinsurance
3.9 Risk Adjusted Decision-Making
Notes
Chapter 4: Underwriting
4.1 Underwriting and Big Data
4.2 Underwriting for Specialist lines
4.3 Telematics and User-Based Insurance as an Underwriting Tool
4.4 Underwriting for Fraud Avoidance
4.5 Analytics and Building Information Management (BIM)
Notes
Chapter 5: Claims and the ‘Moment of Truth’
5.1 ‘Indemnity’ and the Contractual Entitlement
5.2 Claims Fraud
5.3 Property Repairs and Supply Chain Management
5.4 Auto Repairs
5.5 Transforming the Handling of Complex Domestic Claims
5.6 Levels of Inspection
5.7 Motor Assessing and Loss Adjusting
Notes
Chapter 6: Analytics and Marketing
6.1 Customer Acquisition and Retention
6.2 Social Media Analytics
6.3 Demography and How Population Matters
6.4 Segmentation
6.5 Promotion Strategy
6.6 Branding and Pricing
6.7 Pricing Optimization
6.8 The Impact of Service Delivery on Marketing Success
6.9 Agile Development of New Products
6.10 The Challenge of ‘Agility’
6.11 Agile Vs Greater Risk?
6.12 The Digital Customer, Multi- and Omni-Channel
6.13 The Importance of the Claims Service in Marketing
Notes
Chapter 7: Property Insurance
7.1 Flood
7.2 Fire
7.3 Subsidence
7.4 Hail
7.5 Hurricane
7.6 Terrorism
7.7 Claims Process and the ‘Digital Customer’
Notes
Chapter 8: Liability Insurance and Analytics
8.1 Employers' Liability and Workers' Compensation
8.2 Public Liability
8.3 Product Liability
8.4 Directors and Officers Liability
Notes
Chapter 9: Life and Pensions
9.1 How Life Insurance Differs from General Insurance
9.2 Basis of Life Insurance
9.3 Issues of Mortality
9.4 The Role of Big Data in Mortality Rates
9.5 Purchasing Life Insurance in a Volatile Economy
9.6 How Life Insurers Can Engage with the Young
9.7 Life and Pensions for the Older Demographic
9.8 Life and Pension Benefits in the Digital Era
9.9 Life Insurance and Bancassurers
Notes
Chapter 10: The Importance of Location
10.1 Location Analytics
10.2 Telematics and User-Based Insurance (‘UBI’)
Notes
Chapter 11: Analytics and Insurance People
11.1 Talent Management
11.2 Talent, Employment and the Future of Insurance
11.3 Learning and Knowledge Transfer
11.4 Leadership and Insurance Analytics
Notes
Chapter 12: Implementation
12.1 Culture and Organization
12.2 Creating a Strategy
12.3 Managing the Data
12.4 Tooling and Skillsets
Notes
Chapter 13: Visions of the Future?
13.1 Auto 2025
13.2 The Digital Home in 2025 – ‘Property Telematics’
13.3 Commercial Insurance – Analytically Transformed
13.4 Specialist Risks and Deeper Insight
13.5 2025: Transformation of the Life and Pensions Industry
13.6 Outsourcing and the Move Away from Non-Core Activities
13.7 The Rise of the Super Supplier
Notes
Chapter 14: Conclusions and Reflections
14.1 The Breadth of the Challenge
14.2 Final Thoughts
Notes
Appendix A: Recommended Reading
Appendix B: Data Summary of Expectancy of Reaching 100
Appendix C: Implementation Flowcharts
Appendix D: Suggested Insurance Websites
Appendix E: Professional Insurance Organizations
Index
End User License Agreement
List of Tables
Table B.1
Table 1.1
Table 1.2
Table 1.3
Table 1.4
Table 1.5
Table 2.1
Table 3.1
Table 4.1
Table 4.2
Table 6.1
Table 6.2
Table 6.3
Table 6.4
Table 6.5
Table 6.6
Table 7.1
Table 9.1
Table 9.2
Table 14.1
List of Illustrations
Figure C.1
Figure C.2
Figure C.3
Figure C.4
Figure C.5
Figure C.6
Figure C.7
Figure C.8
Figure C.9
Figure C.10
Figure C.11
Figure C.12
Figure C.13
Figure C.14
Figure C.15
Figure C.16
Figure C.17
Figure C.18
Figure C.19
Figure C.20
Figure C.21
Figure C.22
Figure C.23
Figure C.24
Figure C.25
Figure C.26
Figure 1.1
Figure 1.2
Figure 1.3
Figure 1.4
Figure 1.5
Figure 1.6
Figure 2.1
Figure 5.1
Figure 5.2
Figure 7.1
Figure 7.2
Figure 10.1
Figure 11.1
Guide
Cover
Table of Contents
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Chapter 1
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‘Insurance was one of the first industries to use analytics, but now the game has changed. There are new types of analytics, new forms of data, and new business models based on them. Insurers need only read this book if they wish to remain in business.’
—Thomas H. Davenport, Distinguished Professor, Babson College; Research Fellow, MIT; Author, Competing on Analytics, Big Data at Work, and Only Humans Need Apply
‘If you want to understand how analytics is applied in insurance then this is THE book to read. Tony has succeeded in writing not just an authoritative and comprehensive review of the insurance industry and analytics but one that is actually enjoyable to read. He covers a range of topics that extends way beyond the core areas of underwriting, risk modeling and actuarial science for which the industry is known but delves into marketing, people and implementation too. This book brings together the author's extensive knowledge of both insurance and technology and presents it in a form that makes it essential reading for market practitioners and technologists alike.’
—Gary Nuttall, Head of Business Intelligence (2012–2016), Chaucer Syndicates
‘In this paradigm-shifting book, Tony Boobier provides us with the foundation to explore and rethink the future of the insurance industry. Visions of the future, a review of key processes and implementation concepts all combine to provide the essential guide to help you take your organization into the next decade.’
—Robert W Davies, Consultant; Author, The Era of Global Transition; Senior Visiting Fellow, Cass Business School, London
‘This book is a valuable read for any professional in the Insurance field who wishes to understand how spatial information and GIS can apply to their field. It introduces the first principals of location theory and goes on to illustrate how they can be applied practically. I would recommend it fully.’
—Jack Dangermond, President, Environmental Systems Research Institute (ESRI)
‘The number-one ranked finding from all recent buyer and customer research is that sales professionals today must be able to educate their buyers with new ideas and perspectives and have a real in-depth knowledge of their customers' burning issues. Tony Boobier explains clearly these key issues within insurers today. He goes further by explaining how insurers themselves can take full advantage of the dramatic advances in Analytics and new technologies. For those insurers seeking to optimize their own sales process and sales performance by using the power of Analytics to successfully target and capitalize on their customers' critical issues, this book is required reading. For those sales professionals seeking to successfully sell to the insurance industry, this book really does hit the mark of providing key insights and new perspectives that will enable a deep understanding of the issues affecting the insurance industry today.’
—Tom Cairns, Founder and Managing Director, SalesTechnique Limited
‘This book is very insightful and shows the author is again thinking ahead of everyone else. Analytics has a major part to play in the supply chain. More information received at FNOL will help provide the right solution to the problem and speed up the process.’
—Greg Beech, CEO, Service Solutions Group
‘This extensive and comprehensive text draws on the author's extended experience of working in the insurance sector in a variety of roles and levels over many years, whilst drawing on his unique insight gained in working in other spheres and disciplines, to provide a highly insightful and relevant account of the application and future application of analytics in insurance in the context of the emergence of Big Data. The text covers an extensive and impressive range of contemporary applications within insurance, including financial risk, finance, underwriting, claims, marketing, property insurance and flood risk, liability insurance, life and pensions, people and talent management. The text goes further in boldly providing a practical account and guidance on the approaches to the implementation of analytics.
Tony Boobier adopts a pragmatic and confident account that is useful to practitioners involved in insurance, and more widely in the use and application of Big Data. The text is also useful and accessible to those studying in the areas of finance, investment and analytics in providing an exhaustive account of the profession from the lens of a highly experienced and proven practitioner. I have no hesitation in recommending this text to practitioners and students of insurance and Big Data alike and I am sure it will become a highly valuable contribution to the “art of insurance”.’
—David Proverbs, Professor, Birmingham City University
‘This publication covers a huge amount of ground. “Big Data, analytics and new methodologies are not simply a set of tools, but rather a whole new way of thinking” seems to sum up the approach and value of this book, which offers fascinating insights into developments in our industry over recent years and raises important questions regarding how we approach the future. I found the Claims section full of illuminating information about the roles and approaches of all the parties involved in the process – insurers, supply chains and experts' roles and attitudes that makes for a fascinating read – it is technical, insightful, challenging and full of vision to take the insurance industry into the future. The section on leadership and talent should resonate with all of us working in insurance.’
—Candy Holland, Managing Director, Echelon Claims Consultants; Former President, Chartered Institute of Loss Adjusters
‘I feel it comprehensively brings the insurance business and analytics together in an easy-to-read/understand and professional, researched way. This book certainly indicates the width and depth of Tony's insurance and analytics knowledge. I also feel that it could be an effective overview and reference for existing and incoming insurance management, through to IT suppliers, other professions involved in the insurance markets, and also for students.
As someone who has been beavering away for thirty-five years at trying to narrow the divide between insurance and IT at strategic level, much of the content is music to my ears, and underlines that the author and I are, as always, singing from the same hymn sheet – analytics in its broadest sense is indeed an ideal catalyst to achieve this objective.’
– Doug Shillito, Editor, Insurance Newslink/Only Strategic
‘Analytics programs that are business driven have proven they deliver substantial benefits within the general insurance industry over a number of years. One of the key analytics challenges facing the market is to establish similar routes to value in more specialist sectors such as the London Markets. This book provides valuable food for thought for those keen to take on this challenge and gain a competitive advantage.’
—Glen Browse, MI, Data and Analytics Specialist (with over 20 years' experience across the banking and insurance industries)
The Wiley Finance series contains books written specifically for finance and investment professionals as well as sophisticated individual investors and their financial advisors. Book topics range from portfolio management to e-commerce, risk management, financial engineering, valuation and financial instrument analysis, as well as much more. For a list of available titles, visit our Web site at www.WileyFinance.com.
Founded in 1807, John Wiley & Sons is the oldest independent publishing company in the United States. With offices in North America, Europe, Australia and Asia, Wiley is globally committed to developing and marketing print and electronic products and services for our customers' professional and personal knowledge and understanding.
Analytics for Insurance
The Real Business of Big Data
Tony Boobier
This edition first published 2016© 2016 Wiley
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Preface
I never intended to work in insurance, technology or analytics, but rather those three things found me. Like so many others, my journey to insurance and analytics started elsewhere and for me it was on the engineering draughtsman's table. There I used mathematics to design new structures but my heart was not so much in the creation of new structures, but rather in the understanding of why structures fail – and then who might be responsible for such failure.
In the failure of structures, all roads lead to the insurance industry. Structures fail because of defective design, workmanship or materials, and there is insurance cover for all of these. With the passage of time I was to learn that in some cases it might be possible to anticipate the cause of failure even before a physical investigation by using data. It seemed an important thing to step away from my engineering background and qualifications to learn a new trade, that of insurance, and in time I became qualified in that industry. Along the way I also discovered the professions of marketing and supply chain management and added these as strings to my bow.
Each time I stepped outside one profession to learn another, it felt like stepping off the top diving board at the diving pool. Looking down, I could see the water but had no real sense of how deep or even how warm it was. I discovered the main barriers between professions were not just of capability but of language, with each profession having its own terminology. Beyond this, as an outsider I couldn't help but see the interdependency between all these professions within the insurance community.
Ten years ago, the lure of technology became overwhelming for me, and there was something in the North American market that I found compelling. At that time they were some years ahead of the UK market although since then the gap has narrowed significantly. They seemed to have recognized technology as the great enabler and not as a threat. Not only did I want to understand why, but also how.
I stepped off the top of the proverbial diving board yet again from the relative safety of the insurance community into the dark waters of technology but this time it was more difficult. The fast moving world of that newer environment made the transition harder. I came to realize that the future of insurance is not just about technology nor about insurance but rests somewhere in between. In a short time, insurance and technology will be irretrievably intertwined and because of this, the insurance industry will have become transformed. New professions will inevitably emerge which sit in that ‘no-man's land’ between insurance and technology and those who reside there will probably hold the key to the future of the insurance profession.
So my challenge is, who is best placed to sit in that ‘no-man's land’? Is it the technologist who has to understand insurance to appreciate the subtleties and nuances of the insurance contract, and without which any attempt to apply the opportunities of data and analytics will fail? Or is it the insurer who has to reconcile the principles of insurance with the new problems of data and gaining deeper insight? Or will new professions emerge, occupying not that place called ‘no-man's land’ but rather some ‘higher ground’? Won't this allow them to see in both directions, both towards the line of business and also towards the technology department (if in the future it still exists, as we currently know it)?
How will those individuals cope with stepping off the high diving board? What capabilities and characteristics will they have? How will they be supported by professional institutions which appear, at least for the moment, to be behind the times? How will those individuals learn?
This book aims to be some sort of guide for those looking to occupy either no-man's land or the higher ground, however they see it. It doesn't set out to be either a compendium of insurance, nor of technology. I have resisted commenting on any particular insurer or vendor. Others with a more independent viewpoint can do this elsewhere, and provide ‘real time’ assessment. For those readers who, like myself, are ‘longer in the tooth’ there is also a different, perhaps harder challenge, which is that of learning to forget old approaches in a new dynamic world.
Finally, I have attempted to offer some thoughts about implementation. Many insurers have a notion that they want to become ‘analytical’ but their challenge seems to be implementation. They think about the ‘what’ but struggle with the ‘how.’ At a time when many if not all insurers will want to jump on the data and analytics bandwagon, what are the issues around putting this into practice, and how might they be overcome? At a time when ‘agile’ is the trend, how might this be accommodated into our rather conservative industry?
So in conclusion, this book reflects what I have personally learned on my own journey. Emotional ups and downs; floods and droughts; risks and realities; integrity and fraud; suppliers and supplied to; inspectors and inspected; and the rest. It's really been quite a trip.
Tony BoobierFebruary 2016
Acknowledgements
Many of the ideas that appear in this book have been amassed whilst working in the insurance and technology industries for over 30 years. My thanks are therefore to all those who contributed directly and indirectly, and sometimes unknowingly, to all my experiences and learning over that time, leading to this book being created.
In particular, I want to thank Terry Clark and Stuart Hodgson at Robins who gave me the foundations of insurance, Garry Stone and Stuart Murray who both started me on the analytic path and Francesca Breeze who gave me the confidence to write.
In addition, I would like to thank all those who helped me on my journey in the technology sector, provided essential comradeship and shared their insights into industry trends. These especially include Craig Bedell, Owen Kimber and Vivian Braun at IBM, but there are many more there who have played an important part and to whom I owe a debt of gratitude.
Throughout my career I have depended on professional institutions to provide me with a window into their industries and professions. To that extent I would like to thank the Institute of Civil Engineers, the Chartered Institute of Marketing, the Chartered Institute of Loss Adjusters (these three institutes awarded me with Fellowship status), the Chartered Institute of Supply and Procurement, and last but not least, the Chartered Insurance Institute.
Many thanks to all those at Wiley who provided comments, suggestions and guidance, especially Thomas Hykiel. I first met Thomas at a conference in Amsterdam and I am extremely grateful to him for helping turn an idea into reality.
Last but not least I have my family in the UK, Chile and China to remember. Michelle for her support, patience and belief in my ability to finish this task. Chris for his unflagging support and for introducing me to new markets and cultures. Tim for his constructive suggestions when I started to run out of steam. And Ginette for always being in touch and keeping my feet on the ground.
About the Author
Tony Boobier BEng CEng FICE FCILA FCIM MCIPS has almost 30 years of broad-based experience in the insurance sector. After over 20 years of working for insurers and intermediaries in customer-facing operational roles, he crossed over to the world of technology in 2006, recognizing it as one of the great enablers of change in an increasingly complex world.
Based in Kent, UK, he is an award-winning insurance professional holding Fellowship qualifications in engineering, insurance and marketing ‘with other stuff picked up along the way.’ A frequent writer and international public speaker, he has had many articles published over three decades on a wide range of insurance topics ranging from claims management to analytical insight, including the co-creation of industry-wide best practice documents.
His insurance focus is both broad and deep, covering general insurance, life and pension, healthcare and reinsurance. He is particularly interested in the cross-fertilization of ideas across industries and geographies, and the ‘Big Data’ agenda which he believes will transform the insurance industry. ‘I lie awake at night thinking about the convergence between insurance and technology,’ he says.
Chapter 1Introduction – The New ‘Real Business’
‘The real business of insurance is the mitigation of countless misfortunes.’
—Joseph George Robins (1856–1927)
The purpose of this book is not to create a textbook on either insurance or technology, so those who are looking for great depth of information on either are likely to be disappointed. Others who need to know the ins and outs of legal case law such as Rylands v Fletcher, or the detailed working of a Hadoop network are also likely to be disappointed, and will need to look elsewhere. Indeed, there are many books which already do good service to that cause. Perhaps helpfully, a list of recommended other reading is shown in Appendix A. This book is somewhat different as it seeks to exist in one of the exciting interfaces between insurance and technology which we have come to know as the topic of Big Data and Analytics.
Readers are most likely to come from one of two camps. For those whose origins are as insurance practitioners, they are likely to either have taken technology for granted, perhaps turned a blind eye or simply become disaffected because of the jargon used. After all, isn't technology something which happens ‘over there’ and is done by ‘other people’?
The technologist might see matters in a different way. Their way is about the challenges of data management, governance, cleansing, tooling, and developing appropriate organizational and individual capabilities. The language of ‘apps’ and ‘widgets’ is as foreign to the insurance practitioner as are terms like ‘indemnity’ and ‘non-disclosure’ to the technologist.
The practice of insurance, and the implementation of technology should not – and cannot – become mutually exclusive. Technology has become the great enabler of change of the insurance industry, and will continue to be so especially in the area of Big Data and Analytics which is one of the hottest topics in the financial services sector.
So there is the crunch: 21st-century technology and how it impacts on a 300-year-old insurance industry. To understand the future it is necessary to think for a short while about the past, to allow current thinking to be placed in context.
1.1 On the Point of Transformation
The starting point of this journey is over 350 years ago, in 1666, when Sir Christopher Wren allowed in his plans for rebuilding London for an ‘Insurance Office’ to safeguard the interests of the leading men of the city whose lives had been ruined by the destruction of homes, businesses and livelihoods. Some might even argue that a form of insurance existed much earlier, in China, Babylon or Rome. Before the end of the 17th century several insurance societies were already operating to provide cover in respect of damage to property and marine, and the insurance of ‘life’ emerged in the early 18th century. It might be argued that mutuals and co-operatives existed much earlier, but that debate can be put aside for the moment.
The principles of insurance are founded on case law with the foundations of insurable interest, utmost good faith and indemnity being enshrined in the early 18th century, and remain substantially unchanged. Even some of the largest global insurance companies themselves have their feet in the past albeit with some name changes. Royal Sun Alliance can trace their history to 1710 and Axa to about 1720. Those walking the streets of London will be familiar with names and places on which are founded the heritage of the insurance industry as it is known today.
It is against that background of tradition that the insurance industry now finds itself in a period of transition, perhaps transformation, maybe even radicalization. Traditional approaches for sale and distribution of insurance products are being cast aside in favor of direct and less expensive channels. The industry is on the cusp of automated claims processes with minimal or perhaps no human intervention. Fraudsters have always existed in the insurance space, but are now more prevalent and behaving with a degree of professionalism seldom seen before. Insurers are increasingly able to develop products suited to an audience of one, not of many. Quite simply, the old rules of engagement are being reinvented.
Coupled with this is the challenge of different levels of analytical maturity by market sector, by company, by location, even by department. Figure 1.1 starts to give some indication of the way the insurance industry is structured.
But this is not just a book about an industry, or an insurance company, or department. It is as much a book about how individuals within the profession itself need to become transformed.
Figure 1.1 The insurance industry
Traditional skills will increasingly be replaced by new technologically driven solutions. New job descriptions will emerge. Old campaigners who cannot learn the new tools of the industry may find it difficult to cope. Professional institutes will increasingly need to reflect this new working environment in their training and examinations. The insurance industry as a whole also comprises multiple relationships (Figure 1.2), some of which are complex in nature.
Figure 1.2 Relationships between parties
Even within single insurance organizations there are many functions and departments. Some operate as relative silos with little or no interference from their internal peers. Others such as Head Office functions like HR sit across the entirety of the business (Figure 1.3). All of these functions have the propensity for change, and at the heart of all these changes rests the topic of Big Data and Analytics.
Figure 1.3 Insurance functions
1.1.1 Big Data Defined by Its Characteristics
Big Data may be ‘big news’ but it is not entirely ‘new news’. The rapid growth of information has been recognized for over 50 years although according to Gil Press who wrote about the history of Big Data in Forbes1 the expression was first used in a white paper published in 2008.
With multiple definitions available, Big Data is best described by five key characteristics (Figure 1.4) which are:
Figure 1.4 Big Data defined by its characteristics
Volume
– the sheer amount of structured and unstructured data that is available. There are differing opinions as to how much data is being created on a daily basis, usually measured in petabytes or gigabytes, one suggestion being that 2.5 billion gigabytes of information is created daily.
2
(A ‘byte’ is the smallest component of computer memory which represents a single letter or number. A petabyte is 10
15
bytes. A ‘gigabyte’ is one-thousand million bytes or 10
20
bytes.) But what does this mean? In 2010 the outgoing CEO of Google, Eric Schmidt, said that the same amount of information – 5 gigabytes – is created in 48 hours as had existed from ‘the birth of the world to 2003.’ For many it is easier to think in terms of numbers of filing cabinets and whether they might reach the moon or beyond but such comparisons are superfluous. Others suggest that it is the equivalent of the entire contents of the British Library being created
every day
.
It is also tempting to try and put this into an insurance context. In 2012 the UK insurance industry created almost 90 million policies, which conservatively equates to somewhere around 900 million pages of policy documentation. The 14m books (at say 300 pages apiece) in the British Library equate to about 4.2 billion pages or equivalent to around five years of annual UK policy documentation. In other words, it would take insurers five years to fill the equivalent of the British Library with policy documents (assuming they wanted to). But let's not play games – it is sufficient to acknowledge that the amount of data and information now available to us is at an unprecedented level.
Perhaps because of the enormity of scale, we seek to define Big Data not just by its size but by its characteristics.
Velocity
– the speed at which the data comes to us, especially in terms of live streamed data. We also describe this as ‘data in motion’ as opposed to stable, structured data which might sit in a data warehouse (which is not, as some might think, a physical building, but rather a repository of information that is designed for query and analysis rather than for transaction processing).
‘Streamed data’ presents a good example of data in motion in that it comes to us through the internet by way of movies and TV. The speed is not one which is measured in linear terms but rather in bytes per second. It is governed not only by the ability of the source of the data to transmit the information but the ability of the receiver to ‘absorb’ it. Increasingly the technical challenge is not so much that of creating appropriate bandwidth to support high speed transmittal but rather the ability of the system to manage the security of the information.
In an insurance context, perhaps the most obvious example is the whole issue of telematics information, which flows from mobile devices not only at the speed of technology but also at the speed of the vehicle (and driver) involved.
Variety
– Big data comes to the user from many sources and therefore in many forms – a combination of structured, semi-structured and unstructured. Semi-structured data presents problems as it is seldom consistent. Unstructured data (for example plain text or voice) has no structure whatsoever.
In recent years an increasing amount of data is unstructured, perhaps as much as 80%. It is suggested that the winners of the future will be those organizations which can obtain insight and therefore extract value from the unstructured information.
In an insurance context this might comprise data which is based on weather, location, sensors, and also structured data from within the insurer itself – all ‘mashed’ together to provide new and compelling insights. One of the clearer examples of this is in the case of catastrophe modeling where insurers have the potential capability to combine policy data, policyholder input (from social media), weather, voice analysis from contact centers, and perhaps other key data sources which all contribute to the equation.
Veracity
– This is normally taken to mean the reliability of the data. Not all data is equally reliable as it comes from different sources. One measure of veracity is the ‘signal to noise’ ratio which is an expression for the usefulness of information compared to false or irrelevant data. (The expression has its origin in the quality of a radio signal compared to the background noise.)
In an insurance context this may relate to the amount of ‘spam’ or off-topic posts on a social media site where an insurer is looking for insight into the customers' reaction to a new media campaign.
As organizations become obsessed with data governance and integrity there is a risk that any data which is less than perfect is not reliable. This is not necessarily true. One major UK bank for example gives a weighting to the veracity, or ‘truthfulness’ of the data. It allows them to use imperfect information in their decisions. The reality is that even in daily life, decisions are made on the best information available to us even if not perfect and our subsequent actions are influenced accordingly.
Value
– the final characteristic and one not widely commentated on is that of the value of the data. This can be measured in different ways: value to the user of the data in terms of giving deeper insight to a certain issue; or perhaps the cost of acquiring key data to give that information, for example the creditworthiness of a customer.
There is a risk in thinking that all essential information is out there ‘in the ether’ and it is simply a matter of finding it and creating a mechanism for absorption. It may well be that certain types of data are critical to particular insights, and there is a cost benefit case for actively seeking it.
In an insurance context, one example might be where remote aerial information obtained from either a satellite or unmanned aerial device (i.e., a drone) would help in determining the scale of a major loss and assist insurers in more accurately setting a financial reserve. Drones were used in the New Zealand earthquake of 2011 and currently US insurers are already investigating the use of this technology.
Beyond these five ‘V's of data, it is likely that other forms of data and information will inevitably emerge. Perhaps future data analysis might even consider the use of ‘graphology’ – the study of people's handwriting to establish character – as a useful source of information. Those who are perhaps slightly skeptical of this as a form of insight might reflect on the words of Confucius who about 500 BC warned ‘Beware of a man whose handwriting sways like a reed in the wind.’
Such thinking about graphology has become a recognized subject in many European countries and even today is used in some recruitment processes. Perhaps one day, the use of analytics will demonstrate a clearer correlation between handwriting, personality, speech and behavior. In an insurance context where on-line applications prevail, the use of handwriting is increasingly likely to be the exception and not the norm. Because of this the need for such correlation between handwriting and behavioral insight is probably unlikely to be very helpful to insurers in the short term.
1.1.2 The Hierarchy of Analytics, and How Value is Obtained from Data
Analytics, or the analysis of data, is generally recognized as the key by which data insights are obtained. Put another way, analytics unlocks the ‘value’ of the data.
There is a hierarchy of analytics (Figure 1.5).
Figure 1.5 The hierarchy of analytics
Analytics which serves simply to report on what has happened or what is happening which is generally known as descriptive analytics. In insurance, this might relate to the reporting of claims for a given date, for example.
Analytics which seeks to predict on the balance of probabilities – what is likely to happen next, which we call ‘predictive analytics.’ An example of this is the projection of insurance sales and premium revenue, and in doing so allowing insurers to take a view as to what corrective campaign action might be needed.
Analytics which not only anticipates what will happen next but what should be done about it. This is called ‘prescriptive analytics’ on the basis that it ‘prescribes’ (or suggests) a course of action. One example of this might be the activities happening within a contact center. Commonly also known as ‘next best action,’ perhaps this would be better expressed as ‘best next action,’ as it provides the contact center agent with insight to help them position the best next proposed offering to make to the customer to close the deal.
It need not unduly concern us that predictive and prescriptive are probabilistic in nature. The insurance industry is based on probability, not certainty, so to that extent insurers should feel entirely comfortable with that approach. One argument is that prediction is a statistical approach responding only to large numbers. This might suggest that these methods are more relevant to retail insurance (where larger numbers prevail) rather than specialty or commercial insurances which are more niche in nature. Increasingly the amount of data available to provide insight in niche areas is helping reassure sceptics who might previously have been uncertain.
In all these cases there is an increasing quality of visualization either in the form of dashboards, advanced graphics or some type of graphical mapping. Such visualizations are increasingly important as a tool to help users understand the data, but judgments based on the appearance of a dashboard are no substitute for the power of an analytical solution ‘below’ the dashboard. One analogy is that of an iceberg, with 80% of the volume of the iceberg being below the waterline. It is much the same with analytics: 80% or more of the true value of analytics is out of the sight of the user.
The same may be said of geospatial analytics – the analytics of place – which incorporates geocoding into the analytical data to give a sense of location in any decision. Increasingly geospatial analytics (the technical convergence of bi-directional GIS and analytics) has allowed geocoding of data to evolve from being an isolated set of technical tools or capabilities into becoming a serious contributor to the analysis and management of multiple industries and parts of society.
Overall it is important to emphasize that analytics is not the destination, but rather what is done with the analytics. Analysis provides a means to an end, contributing to a journey from the data to the provision of customer delight for example (Figure 1.6). The ultimate destination might equally be operational efficiency or better risk management. Insight provided should feed in to best practices, manual and automatic decisioning, and strategic and operational judgments. To that extent, the analytical process should not sit in isolation to the wider business but rather be an integral part of the organization, which we might call the ‘analytical enterprise.’
Figure 1.6 From ‘data’ to ‘customer delight’
1.1.3 Next Generation Analytics
Next generation analytics is likely to be ‘cognitive’ in nature, not only providing probabilistic insight based on some degree of machine learning but also with a more natural human interface (as opposed to requiring machine coding). Cognitive analytics is not ‘artificial intelligence’ or ‘AI’ out of the mold of HAL in Kubrick's ‘2001 – A Space Odyssey’ but rather represents a different relationship between the computer and the user. We are already on that journey as evidenced by Siri, Cortana and Watson. Speculators are already beginning to describe ‘cognitive’ analytics as ‘soft AI.’ This is a trend which is likely to continue as a panacea to the enormous volumes of data which appears to be growing exponentially and the need for enhanced computer assistance to help sort it. Cognitive analytics may also have a part to play in the insurance challenges of skill shortages and the so-called demographic explosion.
Forms of cognitive computing are already being used in healthcare and asset management and it is only a matter of time before it finds its way into mainstream insurance activities.
Coupled with this is the likely emergence of contextual analytics. Insurance organizations will become increasingly good at knowing and optimizing their own performance. Unless consideration is given to what is happening outside their own organization, for example amongst their competitors, then these viewpoints are being made in a vacuum. The American scientist Alan Kay expressed it succinctly in these words: ‘Context is worth 80 IQ points.’
In the cold light of day, there are two key objectives which need to be adopted by insurers: Firstly, to outperform direct competitors, and secondly, to achieve strategic objectives. To do one and not the other is a job only partly completed. Often but not always the two key objectives go hand in hand.
Outperformance of competitors by insurers may be measured in varying forms:
Finance performance – profit, revenue, profitable growth.
Customers – retention, sentiment, propensity to buy more products.
Service – both direct and through third parties such as loss adjusters who are considered, by extension, as part of the insurer themselves.
Staff – retention, sentiment.
These issues need to be considered in the context of the wider environment, for example the macro-economy or the risk environment. In a time of austerity or where there is rapid growth in the cost of living, individual families may choose to spend more on food than on insurance products. At a time when the agenda of insurers has been dominated by risks associated with capital and solvency, perhaps their eyes have been temporarily taken off the ball in terms of other risks such as underwriting risk, reputational risk and political risk but that position is relatively easily and quickly remedied.
1.1.4 Between the Data and the Analytics
Big Data in either its structured or unstructured forms does not naturally flow into analytic outcomes, which usually takes the form of reports, predictions or recommended actions, but relies on intermediate processes which exist ‘between the data and the analytics.’
How this is done in practice is a matter for the technical experts but in simple terms the raw data needs to be captured, then brought into the system where it is filtered, cleansed and usually stored. Massive volumes of data lend themselves to complex sorting systems or ‘landing zones,’ most of which have their own language and jargon. Often a datamart or staging layer is created to ensure that an analytical outcome can be created relatively quickly. The process by which data is moved through the system is referred to as ETL, or ‘extract, transfer, load.’
There are other alternatives, such as ‘data warehouse appliances’ which provide a parallel processing approach and create a modular, scalable, easy-to-manage database system. These high speed solutions allow very rapid computing power by providing an alternative to traditional linear processing, and often come with pre-bundled analytical and geospatial capabilities. In effect this is a ‘plug and play’ approach to Big Data and Analytics. These serve as a reminder that, as was experienced with the internet in the early days, both organizations and individuals will increasingly press for computing power in the form of analytics to be provided ‘at speed.’ It doesn't seem that long ago that, in a domestic environment, connecting to the internet was accompanied by some form of whistling and other strange noises down the telephone line. Now instant 4G connectivity is expected anytime, anyplace, anywhere – within reason. Perhaps in that light, if one level of differentiation between technology vendors is that of the breadth and depth of analytical capability, the other differentiating factor may well be speed of delivery of the analytical insight. The need for speed potentially opens the door for interesting alliances of what might previously have been competing organizations.
‘Cloud’ computing also needs to be considered here. One good and simple description of cloud computing, often just referred to as simply ‘the cloud,’ is the delivery of on-demand computing resources. This includes everything from applications to data centers – on a pay-for-use basis, often accessible through wireless. For the record (and just in case anyone is thinking it) this is not a process in the sky or somewhere in the ether, but rather is an expression to reflect a capability. Users should not be misled by the fact that there are usually no cables or physical connectivity involved. As with the other processes described above, the technology is too complex to be considered in detail, and in fact cloud computing as a topic is worthy of its own book (and there have been many of them). But cloud computing also provides another example of how a paradigm shift in the thinking of the insurance industry needs to take place. The entire concept opens the door to new thinking, and those who do not have an open mind will be disadvantaged. In their 2014 document ‘Predicts 2015: Cloud Computing Goes Beyond IT into Digital Business’ Gartner indicate that business leaders will need to ‘constantly adapt their strategies to leverage increasing cloud capabilities.’
It is increasingly critical that business users need to have some understanding not only of current IT capabilities but what are likely to be the IT capabilities of the future, in order to effectively manage their business and create new and compelling strategies.
It is easy to get bogged down in terminology. Readers should try not to become either distracted or confused by many expressions which are not familiar to them. It may be sufficient for individuals simply to become aware of what they do not know, and as a result have an open mind about technology and change. Some may view this as a catalyst, a personal challenge or perhaps a call to action in order to find out about new elements of their own industry and other associated industries. Managers may wish to encourage their direct reports to become more familiar with technology as part of their annual personal development planning.
1.2 Big Data and Analytics for All Insurers
At face value, Big Data and Analytics are for big insurers who have the economy of scale to supplement data external to their organization with a firm foundation of internal information. Many of the industry proof points, for example fraud analysis and telematics, are firmly aimed at the property and casualty market, and especially at the B2C sector. But insurance is a broad church, and there are many parts of the industry, perhaps all of them, that can benefit from an analytical approach.
1.2.1 Three Key Imperatives
At the highest level, all insurers are interested in three key elements
Operational efficiency – delivered through cost reduction, claims management and productivity strategies.
Profitable growth – delivered through profitable customer acquisition and retention, cross selling and upselling.
Risk management – delivered through capital efficiency and operational risk management.
Underpinning these three elements is what might be described as a ‘pure play,’ that of financial performance management. It is called ‘pure’ because the analytical approaches used in the Office of Finance are generally transferable from industry to industry. All CFOs are interested in the financial performance of their organization and need to report to stockholders using standardized techniques. In the case of insurance CFOs, there is often less certainty in the figures which invariably make projections for ‘IBNR’ (Incurred But Not Reported), a situation where insurers need to take into account the amount owed by them to customers who are covered for a claim but have not yet reported it, such as in the case of a major weather event. The effect of long tail claims, i.e., claims of lengthy duration, is also an important part of the consideration of the insurance CFO and their team.
Increasingly insurers are gaining greater insight into the convergence of the risk, compliance and financial performance management process. This approach, where data is reused and where reporting software for instance is repurposed, allows insurers to gain added value from the compliance process. It also creates a soft benefit in that it starts to break down the silos of risk and finance that exist in many organizations and increasingly embeds a risk culture into operational decisions.
It is tempting to suggest what are the typical trends for any given segment of the insurance sector, but different trends occur within the industry, in different sectors at different times and in different places. A typical example of this might be the Solvency II initiative in Europe, replicated to some degree in many other locations such as South Africa and parts of Latin America. While Solvency II has been a burning (and non-negotiable) platform, insurers had no real option but to pour in money and resource albeit to the detriment of other programs. For some insurers this represented 80% of their IT development budget. To that extent, risk and regulation have been at the top of the league table in terms of prioritization, although risk and compliance in Europe are increasingly assuming a ‘business as usual’ status. Although some fine tuning is likely to occur especially around risk reporting, the topic seems less critical at the moment. Even so, there is a school of thought which indicates that now that insurers have crossed the Solvency II compliance ‘deadline’ of January 2016, the topic of risk and compliance will be revisited as insurers drive for improved operational efficiency and cost reduction.
Standard techniques such as PEST and SWOT analysis remain available to insurers to allow them to identify key issues. Such a methodology remains valid although increasingly there is concern that some traditional management school thinking may be slowly becoming out of date due to the nature, impact and speed of change. In such formal techniques, topics such as disruptive technology may be both an opportunity and a threat. Beyond this, the influence of disruptive technology and ‘agile’ change is forcing organizations to re-evaluate their view towards risk management.
Notwithstanding, it is still possible to identify the key business drivers of each industry sector albeit that the prioritization of each business driver may differ at a local level, and these have been tabulated below.
Life and pensions insurance comprises the largest sector representing 60% globally and usually also at a local level (although there are some exceptions due to local economic considerations and market maturity). Life and pension companies have similar key drivers (Table 1.1).
Table 1.1 Key drivers of life and pension insurers
Business driver
External influences
Analytical response
Profitable growth
Market conditions and volatility
Asset and liability management
Risk management
Political, technological and economic uncertainty
Operational risk management
Customer behavior
Competitive environment, personal and corporate uncertainty, disposition to withdraw funds
Predictive behavioral analysis
Healthcare takes on different flavors geographically. Many insurers offer healthcare insurance cover, as well as travel accident insurance. That part of the insurance industry generally comprises two elements with similar business drivers (Table 1.2):
Healthcare (wellness)
Travel and Accident.
Table 1.2 Key drivers of healthcare insurers
Business driver
External influences
Analytical response
Rising cost of healthcare provision
Lifestyle and behavior, effectiveness and availability of state provision.
Effective underwriting
Increased claims cost
Rising cost of treatment
Effective triage, claims management, fraud analytics
Regulatory changes
Shift from public to private purse
Customer analytics, risk and operational management
Property and casualty – often known as General Insurance – comprise 40% of the market, although this is also broken up into subsets such as retail (or personal lines), commercial lines, and specialty lines such as terrorism, marine, fine art and the like.
Key business drivers (Table 1.3) are consistent to some degree although invariably differ dependent on the type of insurance business in operation:
Retail
Commercial Lines
Specialty.
Table 1.3 Key drivers of general insurers
Business driver
External influences
Analytical response
Cost containment
Claims experience through weather volatility; too many frictional process costs
Effective claims management; effective customer onboarding; effective supply chain management
Fraud management
Economic environment, consumer behavior
Fraud management at point of claim and underwriting
Customer retention and growth
Overcapacity of local insurance marketplaces; retail insurance as a commodity; low consumer trust/loyalty
Customer analytics to understand and avert propensity to churn.
Regulatory compliance
Solvency and other local regimes
Capital and risk management
Reinsurers and Captives: Beyond these, there are reinsurance companies who underwrite the primary carriers or cedants, and captive insurers who act only for their commercial owners. Their key business drivers (Table 1.4) are less orientated towards issues concerning the customer and more towards the management of financial performance and risk.
Reinsurance
Captives.
Table 1.4 Key drivers of reinsurers and captives
Business driver
External influences
Analytical response
Effective understanding of major incidents
Climate change, political volatility
Predictive modeling; what if modeling; understanding of risk accumulation through spatial analytics
Financial risk management
Economic and political volatility, risk accumulation
Capital and risk management
1.2.2 The Role of Intermediaries
Insurers do not operate in a vacuum but rather depend on third parties to help them discharge their obligations, or optimize their operations. If insurers have an interest in Big Data and Analytics, then so too must their intermediaries. Such ‘intermediaries’ include:
Tied Agents
– A company sales person who promotes the products of their employer only. Under section 39 of the Financial Services Markets Act 2000 (FSMA) they must make their status clear to the applicant/purchaser at the earliest opportunity.
Independent Agents
– Also known as an insurance sales agent or ‘producer,’ the independent agent usually sells a variety of insurance products and is paid a commission or remuneration. Usually the independent agent is an independent contractor, often with an individual business. National Alliance Research indicates that on average an independent (US) agent concurrently works with 13 property and casualty insurers, and six life insurers on a regular basis.
Loss Adjusters
– Independent or tied claims specialists whose duty is to administer a submitted claim within the terms and conditions of the policy. The expression ‘adjuster’ leads many to believe that the role of the professional involved is one of adjusting, or ‘reducing’ the claim as presented. Whilst that may the case in some instances, the profession can trace its roots back to the late 17th century and since that time they have been variously known as ‘valuers,’ ‘surveyors,’ ‘assessors’ and more recently ‘adjusters’ – a term which seems to have become more commonplace in the mid-1950s.
Repairers, Body Shops and Restoration Contractors
– A broad group who are variously appointed either directly or indirectly by the insurer, or the policyholder in the event of a claim occurring. Their responsibility is to undertake the repair of either a property or vehicle to a prescribed required standard. This must be to the standard of the local building or construction regulation, or the required standards of the motor manufacturer. In the case of a restoration contractor, this function is usually initially to ‘stabilize’ the building following fire or flood prior to permanent works taking place. In some cases, the restoration contractor is able to undertake the permanent repairs.
These independent parties directly involved in the repair/fulfillment process came to the fore as a result of the desire of insurers to gain greater control over the repair process, usually in the light of claims costs increasing and also the impact of policyholder fraud. Historically the policyholder was invited to provide three estimates for a repair, and from time to time these were found to be provided by the same repairer albeit using different letterheads. (Astute claims handlers were usually able to identify spelling errors which were consistently made in each of the three estimates.)
