ICA Paul Hately BSc FIA Global Head Underwriting and Client Services Swiss Re Life and Health London, United Kingdom

Transcription

1 ICA 2014 Paul Hately BSc FIA Global Head Underwriting and Client Services Swiss Re Life and Health London, United Kingdom

2 The Middle Market New business: Different business There is great potential for growth in the middle market, but it needs a fresh approach 2

3 Who is this "Middle Market"? 3

4 If we look at it by income % 90.00% 80.00% 70.00% 60.00% 50.00% 40.00% 30.00% 20.00% 10.00% 0.00% 21% 37% 32% 29% 17% 18% 8% 14% 28% 38% 36.00% 22% Switzerland Germany USA low income (<70% of median) lower middle (70% - 100% of median) upper middle (100% - 150% of median) high income (>150% of median) Source: Swiss Re Economic Research & Consulting "The Middle Market" is partly identified through a income definition 4

5 and how easily people make ends meat 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 63% 63% 61% 63% 64% 61% 61% 59% 37% 37% 39% 37% 36% 39% 39% 41% Germany Spain France Portugal Romania Sweden United Switzerland Households making ends meet fairly easily, easily or very easily Kingdom Households making ends meet with great difficulty, with difficulty or with some difficulty Source: Swiss Re Economic Research & Consulting but also its ability to make ends meet. 5

6 And claims experience does matter % 90.00% 80.00% 70.00% 60.00% 50.00% 40.00% 30.00% 20.00% 10.00% 0.00% 20.60% 29.40% 27.80% 22.10% Low mortality 37% 17% Average mortality High mortality 32.47% 17.59% 8% 13.94% 38% 36.00% Switzerland Germany USA low income (<70% of median) lower middle (70% - 100% of median) upper middle (100% - 150% of median) high income (>150% of median) Source: Swiss Re Economic Research & Consulting It is important to understand the underlying risk of this group 6

7 What are the numbers? Switzerland USA Germany Low 36% High 26% Low 43% High 36% Low 42% High 40% Middle 38% Middle 21% Middle 18% 3 million 66 million 14 million This is a market with numbers. But it is different. 7

8 The Protection Gap 8

9 The massive Protection Gap globally 9

10 Why are consumers not buying? Behavioural Economics Automated Underwriting Data Driven Target Marketing 10

11 Protection Gap Middle Market, US approx. Gen Y Gen X Young Boomers Generation X, middle market, is a major contributor to the Protection Gap 11

12 Where is the growth? GDP growth -> rising middle class -> opportunities for insurance 12

13 Why is there such a gap? Smaller policies Less Advice Driven Poor Communication Complex Propositions Fulfilment time Far Too Long 13

14 There is a clear opportunity here In the US, the middle market represents the largest segment of uninsured households, with half (36 million) admitting they need more life insurance LIMRA The top two reasons people don t buy life insurance are: 1. Competing financial priorities 2. Because they think they cannot afford it LIMRA Interestingly though, studies found that consumers overestimate the cost of life insurance by as much as three-fold (LIMRA/LIFE, Swiss Re European Insurance Report, ) 14

15 How do we crack this market? 15

16 Simplification is critical Formal Advice Is Not Always Tops Customer Experience Critical Reduce And Manage Fraud Risk Classification Critical 16

17 Product experience is critical Underwriting Reduce number of questions Move onto different technologies Advice / Information Remuneration based on experience Information easy to understand Clever Use of Data Use insights from data to understand profit potential of clients Consider new sources of data 17

18 Identifying "good" and "bad" agents Category Market reach Geography Range of products sales Lapse rates Conversion rates Number of medium to high risk disclosures Description An agent with a wide market reach will tend to sell larger volumes of business Some smaller agents may concentrate their sales locally which may be perfect for niche local products but not for nationwide reach Some agents may be good at selling a range of products such as life, CI and IPI, however some may be only comfortable with life. If a client is wishing to expand IPI then understanding the type of agent selling IPI will be useful The extent of churning business is a key factor when deciding upon the quality of a distributor. Insurers will want sticky business A good agent will convert a high number of applicants into policies The truthfulness and ability to complete an application form is important to ensure the correct disclosures are included 18

19 Understanding agent performance The red radar shows the average score for the aggregated industry universe Inside the radar is good, outside needs investigating 19

20 Understanding the target population 100% 90% 80% 8% 5% 6% 10% 70% 60% 50% 40% 30% 58% 62% 20% 10% 0% 24% 27% Insured Bank Customer Uninsured Bank Customer Preferred Standard Substandard Declined Source: Swiss Re Global Underwriting and Client Services There is a lot of value in the uninsured population that is waiting to be unlocked 20

21 Balancing risk management, pricing and efficiencies Additional mortality cost Guaranteed Issue High Low Underwriting effort Low Auto Accept Dec of Health 2-3 med questions only 2-5 med & non-med questions 20+ med & nonmed questions Medical tests Financial High 21

22 Case studies and insights 22

23 Predictive Underwriting Fictional model output Cut-off could be set anywhere within this range Percentage of standard & substandard Declined Up to 50 Standard All Model Output by 5 percentile 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 (see "All" column) 23

24 How intelligent can data be? Customer in receipt of incapacity benefit (last 12 months) Likelihood of being rated or declined (left axis) Percentage of the population (right axis) Average % of rated or declined across whole population Up to X X to Y Y+ 0

25 Digging deeper Value of Health Transactions in last 12 months Likelihood of being rated or declined (left axis) Percentage of the population (right axis) Average % of rated or declined across whole population Females less than X Females over X Males less than X Males over X 0

26 Further refining 18 ATM cash withdrawals in last month Likelihood of being rated or declined (left axis) Percentage of the population (right axis) Average % of rated or declined across whole population 0 0

27 Optimising through targeting: Low Risk Asthmatics Population 61.6% BMI less than % BMI % BMI % BMI over % Original SA less than 50k 65.1% Non smoker 74.9% Original SA k 72.7% Smoker 47.9% Original SA 100k % Shows proportion of applicants with Asthma disclosure & standard rates ACI 83.5% TERM 67.6% Only Asthma 89.7% Have other impairment 78.6% Source: Swiss Re Global Underwriting and Client Services App Questions 40 or less 71.3% App Questions 41 or more 81.3% Savings from understanding the trade-off between evidence and extra mortality 27

28 Data analysis to understand trends and the effects of a change in underwriting philosophy Diabetes Mellitus Source: Swiss Re Global Underwriting and Client Services

29 Rulebase Optimisation One of the key uses of Magnum data is to optimise performance of the rulebase. We perform analysis on the operation of the rulebase to identify areas where changes could be made. This has helped clients achieve market-leading acceptance rates and significantly reduce costs Step 1: Review current performance Step 2: Identify rules for improvement Step 3: Model rule changes Case Study Client Magnum Client since 2000 Processing around 150,000 cases p.a Action Result Rule review conducted driven by Magnum data & Swiss Re analysis Changes identified to around 100 commonly used rules Rulebase development period = 3 months 15% increase in acceptance rates Cost savings through medical evidence improvements uw costs reduction = 450,000 p.a.

30 Lapse rate analysis by life stage and affluence provides insight into policyholder behaviour Lapse by affluence Higher lapses at all durations for lives in the lower affluence banding Young lives exhibit higher lapses than those in older life stages Lapse by Life Stage Source: Swiss Re Global Underwriting and Client Services

31 Insights enabled through technology Underwriting Data Insights Easy to buy Little advice Not big premiums Immediate This middle market sees a high proportion of lives with these preferences 31

32 Sum assured ( 000's) Middle Rung Living Mid age and less affluent Proportion of universe 20% Proportion of UK population 14% Male percentage 48% Non Smoker percentage 78% Joint life percentage 24% Product Mix Life CI 1% 1% 63% 62% IP 2% 3% Life Stage Young Retired ACI 26% 32% Affluence Low High WoL 1% 6% BMI Underweight Morbidly Obese Other 1% 2% This group of lives are mid age with an average age of 40 years This is a over represented segment with 20% of the MDP universe, compared to UK population of 14%. Lapse rates and in force are similar for this segment compared to the market at almost 20% of the total There is an overrepresentation of ACI policies, and higher than average sum assured in IP Segment size: 59.7k policies, 81.6k applications UK market size: circa 4.0m individuals 0% 10% 20% 30% 40% 50% 60% 70% Segment Universe Sum Assured profile by product Policy outcome 1000 Interquartile range Min 750 Median Max Life CI IP ACI WoL 100% 80% 60% 40% 20% 0% Segment MDP Lapsed Inforce Claim Source: Swiss Re Global Underwriting and Client Services

33 Underwriting platforms drive improvements 90% 85% 80% 75% 70% 65% No Underwriting platform Insurer X Introduction of rules engine to website Rules engine introduced into Tele-UW 63% Use underwriting platform data to conduct major rulebase review 64% Worked on simplification project 70% Roll-out of underwriting platform to branches 80% 60% 60% 60% 55% 50% "Best practices" and integrating data insights into processes allows insurers not only to continue to target the best lives but also improve the process and create a more cost effective and pleasant consumer experience Acceptance rates (Life Cover)

34 Interesting insights 100% 90% 80% 70% 60% 50% 40% 30% Follow Up Refer Loaded Decline Standard STP Conversion Rate 20% 10% 0% H1 Time taken to make a decision has a big influence 34

35 Increased risk can still mean increased profits 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 20% 20% 10% 12% 15% 7% 55% 61% Commission Profit Expenses Risk Rate 0% Standard Process Tweaked Process Profit is a function of multiple factors explore their interaction to optimise profits 35

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37 Legal notice 2014 Swiss Re. All rights reserved. You are not permitted to create any modifications or derivative works of this presentation or to use it for commercial or other public purposes without the prior written permission of Swiss Re. The information and opinions contained in the presentation are provided as at the date of the presentation and are subject to change without notice. Although the information used was taken from reliable sources, Swiss Re does not accept any responsibility for the accuracy or comprehensiveness of the details given. All liability for the accuracy and completeness thereof or for any damage or loss resulting from the use of the information contained in this presentation is expressly excluded. Under no circumstances shall Swiss Re or its Group companies be liable for any financial or consequential loss relating to this presentation. 37