Big Analytics unlocking Big Data

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2 Agenda What is Big Data and Predictive Analytics? How do Insurers stand to benefit? How it can benefit ERM practices What risks might Big Data bring to your organisation? Case studies (UBI, ALT) 2

3 What is Big Data? 90% of the data available today has been created in the last 2 years. Volume Amount of data generated from the dawn of humanity until 2003 is now created every 2 days. Velocity Variety Source: IBM, Google. The data world is changing creating many new opportunities and challenges 3

4 There are many sources of available data Internal information: External information: 4

5 Big Data represents a significant opportunity for the Insurance Industry Computer and electronic products 1 Information Historical productivity growth in the United States, (%) Administration, support and waste management Arts and entertainment Manufacturing Professional services Utilities Accommodation and food Retail trade Educational services Other Services Construction Transportation and warehousing Health care providers Natural resources Wholesale trade Real estate and rental Management of companies Big Data value potential index Finance and insurance Government Source: US Bureau of Labour Statistics. Insurance possesses one of the highest potentials for Big Data value 5

6 Big Data represents a significant opportunity for the Insurance Industry Some hurdles to overcome and risks to manage Change of mind-set as to drive innovation with Big Data (outside already defined schemas) An evolution of IT capability is required, by embedding new innovative technology solutions Some additional technical skills will be required (e.g. Hadoop architects / developers; machine learning experts) The data world is changing creating many new opportunities and challenges 6

7 Big Data innovation is today s reality TW Mega trend survey: which of the following trends do you believe will have the most significant impact on your business in the next five years? Regulatory and legislative constraints 54% Capital management challenges 40% Volatile economic environment 44% Shifting demographics 42% Advanced technology such as predictive modeling and "big data" 26% Consumer power as a result of social media 21% Growth in demand for insurance products in emerging markets 19% Global climate change 24% Talent attraction and retention 14% Global geo-political instability 16% Base: Total Respondents n=500+ Source: 2013 Towers Watson Mega Trends survey 7

9 How could it work for insurance? Personal posts on social media Term assurance 9

10 How could it work for insurance? combined with internal policy information Pet insurance 10

11 How could it work for insurance? enriched with weather data Travel insurance But we are not there yet! 11

12 Big Data techniques can be applied across all business functions of the insurance industry Next best action Real time offers Workforce assurance Workforce allocation, resource optimisation Workforce Analytics Real Time Decision Making Big Data Big Hub Data ERM? Customer Analytics Persistency management Campaign optimisation Personalised customer experience Quotes Analytics Leads value analysis Resource allocation / optimisation per sales channel Claim triage optimisation Claims performance analysis Fraud detection Fraud Analytics Claims Analytics Underwriting and Pricing Analytics Risk selection optimisation Granular profitability assessment Price optimisation Big Data hubs serve initiatives across Insurance Organisations 12

14 Big Data for risk management activities Enterprise risks overview Equity Property Legal Compliance Market Risk Interest rate Currency Concentration Volatility Inflation Operational Risk Internal Fraud External Fraud Employment practices Business disruption Data governance Property damage Credit and Funding Risk Default Contingent Settlement Longevity Reinsurance Funding Rate Morbidity Other Risks Process Political Reputational IT & Systems Regulatory Misconduct Mortality Catastrophe Strategic Emerging Insurance Risk Persistency Expense New business Pricing & Underwriting Many more Reserving Pricing 14

15 Risk use case 1: Product mis-selling Compliance Risk Reputational Risk Proposed analysis 1. Analyse Big Data sources to understand consumers views and opinions on the major product groups being offered. Start with your own data! 2. Cast net locally or globally; feed in trends in global insurance perspectives. 3. Assess satisfaction/dissatisfaction trends, sentiment, expectations and identify regulatory and legislative opinion. Examples of data sources to be considered Recorded customers calls Complaints log Customers satisfaction surveys Ombudsman complaint data Customer associations data Experian Hitwise Social media Potential benefits (throughout product cycle) Identify emerging product risk, assess its impact and undertake swift remedial action Minimize reputational risk / protect your brand Determine appropriate level of mis-selling reserve Key input into product design and the renewal process pre-emptive intervention possible to promote loyalty Meet regulatory expectations as well as customer perception 15

16 Risk use case 2: Manage persistency levels Persistency Risk Proposed analysis Examples of data sources to be considered 1. Analyse in-force portfolio to identify customer groups with high lifetime value 2. Analyse own proprietary data to identify, as far as possible, drivers of retention using latest analytical techniques (e.g. GLM) 3. Determine how to communicate and influence high value customers Econometrics (e.g. historic and prospective data for interest rates, investment returns, affordability statistics) Socio-demographics (e.g. birth, marriage, divorce rates, migration, revenue income) Credit scoring Sentiment analysis customers behavioural data Potential benefits (throughout product cycle) Assess extent of persistency risk within key in-force portfolio groups Influence pricing, underwriting and marketing decisions (2 nd line supporting 1 st line of business) Increase value of in-force portfolios Improve long-term relationships with high value clients 16

18 Even if the Risk Function chooses not to play, it has work to do! Big Data introduces new risks due to Privacy concerns Negative perception Inability to cope operationally Ethical and moral issues Flawed analysis / lack of maturity Data security risks Insurer ERM considerations Exploitation of Big Data Reputational and brand risk Operational risk Robust challenge of use of Big Data Establishment of Big Data policy and infrastructure Redesign of business governance and approval External communications and reassurance 18

20 Get CONNECTED to Big Data department and initiatives Consider the following areas: State of play of your Big Data capability Maturity of your data processes and ecosystem Security protocols and their needs to evolve Governance framework and controls Technology changes and their impact on the overall infrastructure Skills gaps, recruitment and training requirements Senior business leaders oversight Customers perception or likely perception Legislation changes 20

22 Volume The UBI game is changing fast Velocity Variety Now Next Future The emerging UBI market is focused on devices fitted by service providers, though the more advanced US market has moved quickly to self-install Many cars produced for the UK support self-install since 2001 Some cars still require light install option Market supports multiple device providers Insurers own the customer Stand-alone device in vehicle now self-installed: in ageing car pool and less developed geographic territories Vehicle manufacturers facilitate integrated technology in more sophisticated markets Multiple device providers remain Emerging use of Smartphone apps More opportunities to own the customer for insurance purposes Vehicle manufacturers are already introducing on-board connectivity but it will take time for the proportion of new vehicles in the car pool to rise significantly Use of Smartphone apps for insurance Multiple opportunities for primary owners of the customer: Vehicle manufacturer Combined TSP/brokers Insurer Mobile technology suppliers Data growth opening up enhanced and more granular analytics 22

23 Volume It is more than just widgets Velocity Variety Value Business optimisation Knowledge creation and decision support Analytics Global infrastructure Telematics Sensors Attract new customers Create novel products Improve profitability Support change management Develop competencies Device technology will change rapidly Distracts management Delays time to market and wastes resources Exposes obsolescence risk Then Then Then 23

24 Telematics if done right everybody wins More accurate pricing Company Significantly reduced risk Consumers Regulator Better claims handling Consumers: Control of premium Understandable rating based on how, when, where they drive Access to useful driving feedback Value-added services Insurers: Enhanced pricing Velocity Volume Variety Green and progressive products Product differentiation and brand awareness Reduced loss costs Improved consumer satisfaction and retention Societally beneficial Product differentiation Regulators: Saves lives Fairly discriminatory variables Consumer acceptance and premium control Environmentally friendly 24

25 Volume but does present new risks Velocity Variety Teenage driver crashed killing himself and friend 'as he raced home to beat insurance company-imposed curfew monitored by onboard computer' Coroner blamed death on telematics curfew. A breach of the curfew would have incurred a 100 penalty - but the driver would not have been penalised for speeding unless he was 50 per cent over the limit. Daily Mail 10 July

26 Big analytics support de-risking of UK pensions Volume Velocity The challenge UK pension schemes are increasingly focussed on reducing the volatility within the sponsors accounts From a strategic perspective, this has led to many schemes developing de-risking journey plans 26

27 Big analytics support de-risking of UK pensions Volume Velocity The solution Asset Liability Tracking - Daily tracking of both the schemes asset s and liabilities along with associated triggers Data sources Membership data Scheme benefit structure Details of the scheme specific asset strategy Investment management benchmarks Expected contributions, benefit payments and expenses Demographic and financial assumptions, and in some cases Market buy-in/buy-out pricing Analysis Value of assets Value of liabilities on up to 8 different basis Market based triggers Funding level triggers 27

28 Big analytics support de-risking of UK pensions Volume Velocity The benefits Maximises the probability of de-risking (or re-risking) actions taking place at the most appropriate time 28

29 It is happening right now don t become isolated 29