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  • M.U.D. CONFERENCE | Jan. 30 2012 | New York

    Predictive Underwriting: an update Improved customer experience and its impact Paul Hately, Global Head Accelerated Underwriting, Swiss Re

  • M.U.D. CONFERENCE | Jan. 30 2012 | New York

    Session Overview

    Predictive Underwriting: definition and context

    Correlations between lifestyle and mortality: examples

    Building a predictive model

    What is sold through predictive underwriting?

    Learnings & consumer reaction

    The US mid-market

    Q&A

    2

  • M.U.D. CONFERENCE | Jan. 30 2012 | New York

    Turning the traditional underwriting philosophy on its head

    Traditional underwriting is about identifying the unhealthy minority amongst the applicants

    Predictive Underwriting enables us to approach the healthy majority of the population (who haven't applied for protection)

    3

    "Are you ill?"

    "Are you well? (statistically)"

  • M.U.D. CONFERENCE | Jan. 30 2012 | New York

    What do we mean by Predictive Underwriting?

    The intelligent use of data held on consumers to reach a view as to their health status

    Developments in the UK have focused on reducing the amount of traditional underwriting needed (where there is an existing data-rich relationship in place)

    In the US, life industry focus is more on using predictive techniques to triage the underwriting process and avoid expensive medical tests for healthy people

    Both approaches are valid and applicable in either markets

    4

    "You haven't applied for protection, but based on what we know about you, we will pre-approve you and make you an offer"

    "Now you are applying for protection, let's run some data on you to remove certain tests, and speed up the process"

  • M.U.D. CONFERENCE | Jan. 30 2012 | New York

    Scene-setter: the mid-market

    If

    consumers are massively under-insured

    they perceive that Protection costs more than it does

    medical underwriting is a barrier to sales

    then the key to success is having access to the consumer to engage with them, and:

    drawing their attention to their need for Protection

    demonstrating the relative cost and value of Protection

    making Protection easy-to-buy (hassle-free)

    5

    Source: Swiss Re Mortality Protection Gap 2011

  • M.U.D. CONFERENCE | Jan. 30 2012 | New York

    Making use of available data

    Intelligent data use is all around us

    Amazon: "Customers Who Bought This Also Bought"

    YouTube / iTunes: recommended song you may like based on your download history and people like you

    Facebook: ads personalised to your interests, hobbies, searches

    Match.com: uses an algorithm to suggest possible partners based on your preferences and your behaviour on the site

    Google: ads in your Gmail account personalised to you

    Supermarkets online or using loyalty card: shopping basket advice and intelligent voucher/offers

    Life insurance is woefully under-developed in this area

    General insurance moving faster than Life & Health (e.g. motor insurers using credit scores )

    What if we could predict mortality based on everyday information?

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  • M.U.D. CONFERENCE | Jan. 30 2012 | New York

    The premise:

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    There are correlations between lifestyle factors and mortality you just need to unearth them

    The following were found in depersonalised datasets

  • M.U.D. CONFERENCE | Jan. 30 2012 | New York 8

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    In Receipt of Disability Living Benefit Last 12 months

    Likelihood of being rated or declined % (Left axis)

    % of total population (Right axis)

    Data fields held by a UK bancassurer

    Average % of rated or declined across whole population

  • M.U.D. CONFERENCE | Jan. 30 2012 | New York 9

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    Incapacity Benefit Last 12 months

    Likelihood of being rated or declined % (Left axis)

    % of total population (Right axis)

    Data fields held by a UK bancassurer

    Average % of rated or declined across whole population

  • M.U.D. CONFERENCE | Jan. 30 2012 | New York

    Your Turn: are these positively or negatively correlated with good health?

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    Pets Jogging Smoking No Multi-Channel TV

    Household Interests (Acxiom ILU Characteristics Analysis, UK)

    Likelihood of being rated or declined % (Left Axis)

    % of total population (Right axis)

    Average % of poor health across whole population

  • M.U.D. CONFERENCE | Jan. 30 2012 | New York

    Which of these occupations is most/least correlated with bad health?

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    Likelihood of being rated or declined % (Left Axis)

    % of total population (Right axis)

    Average % of poor health across whole population

  • M.U.D. CONFERENCE | Jan. 30 2012 | New York

    What will the shape of this graph be?

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    Affluence Ranking - Acxiom ILU Characteristics Analysis (UK)

    Likelihood of being rated or declined % (Left Axis)

    % of total population (Right axis)

    Low High

    Average % of poor health across whole population

  • M.U.D. CONFERENCE | Jan. 30 2012 | New York

    Taking it to the next level

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    Females less than 200 Females over 200 Males less than 200 Males over 200

    Value of Health Transactions in last 12 months (UK Bancassurer)

    Likelihood of being rated or declined % (Left Axis)

    % of total population (Right axis)

    Average % of rated or declined across whole population

  • M.U.D. CONFERENCE | Jan. 30 2012 | New York

    Further refining

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    Age under 40 and less than 15 ATM trans

    Age under 40 and more than 15 ATM trans

    Age over 40 and less than 15 ATM trans

    Age over 40 and more than 15 ATM trans

    ATM cash withdrawals in last month (UK Bancassurer)

    Likelihood of being rated or declined % (Left Axis)

    % of total population (Right axis)

    Average % of rated or declined across whole population

  • M.U.D. CONFERENCE | Jan. 30 2012 | New York 15

    Building a predictive model

    Bottom line: any information held on a customer could be predictive of their health status let the data do the talking

    Combining all the predictive variables, an algorithm is built that ranks each customer from worst to best prospect, in terms of "likelihood of being given standard rates at application stage"

    Probability of being a bad risk = 1/(1+e-y) y = a+bx1+cx2dx3+ex4+fx5+gx6+hx7ix8+jx9kx10lx11+..+ where: x1 is age related

    x2 is related to value of home x3 is a brand identifier x4, x5, x7 are related to occupation x6, x9, x11 are account activity related x8 x10 are neighbourhood / community related

  • M.U.D. CONFERENCE | Jan. 30 2012 | New York

    What's needed?

    16

    Two matchable depersonalised data sources

    Risk data:

    c. 50,000 final underwriting decisions from a Life Office

    The more cases the better

    Descriptive Data:

    bank checking account, loyalty card, potentially home/motor

    insurance

    The richer the data the better

    Correlations are found in the descriptive data (the "predictors")

    Model can be run on whole customer universe to highlight the best prospects

  • M.U.D. CONFERENCE | Jan. 30 2012 | New York

    Model Output example

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    5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 All

    Percentage of standard & substandard

    Model Output by 5 percentile

    Fictional model output

    Declined

    100+

    51-99

    Up to 50

    Standard

    Cut-off could be set anywhere within this range

    This tells us, for example, that the top 5% of the model contains a "rated or decline" rate of 5%, as opposed to 14% were no model built (

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