Session 45 PD, Life Insurance for the Digital Consumer An Actuarial Perspective. Moderator: Craig E. Hanford, FSA, MAAA

Transcription

1 Session 45 PD, Life Insurance for the Digital Consumer An Actuarial Perspective Moderator: Craig E. Hanford, FSA, MAAA Presenters: Stephen Abrokwah, ASA, CERA, MAAA Craig E. Hanford, FSA, MAAA Nathan P. Schelhaas, FSA, MAAA SOA Antitrust Disclaimer SOA Presentation Disclaimer

2 2018 SOA Life & Annuity Symposium LIFE INSURANCE FOR THE DIGITAL CONSUMER AN ACTUARIAL PERSPECTIVE Stephen Abrokwah, PhD., ASA, CERA, MAAA Nate Schelhaas, FSA, MAAA Craig Hanford, FSA, MAAA

3 SOCIETY OF ACTUARIES Antitrust Compliance Guidelines Active participation in the Society of Actuaries is an important aspect of membership. While the positive contributions of professional societies and associations are well-recognized and encouraged, association activities are vulnerable to close antitrust scrutiny. By their very nature, associations bring together industry competitors and other market participants. The United States antitrust laws aim to protect consumers by preserving the free economy and prohibiting anti-competitive business practices; they promote competition. There are both state and federal antitrust laws, although state antitrust laws closely follow federal law. The Sherman Act, is the primary U.S. antitrust law pertaining to association activities. The Sherman Act prohibits every contract, combination or conspiracy that places an unreasonable restraint on trade. There are, however, some activities that are illegal under all circumstances, such as price fixing, market allocation and collusive bidding. There is no safe harbor under the antitrust law for professional association activities. Therefore, association meeting participants should refrain from discussing any activity that could potentially be construed as having an anti-competitive effect. Discussions relating to product or service pricing, market allocations, membership restrictions, product standardization or other conditions on trade could arguably be perceived as a restraint on trade and may expose the SOA and its members to antitrust enforcement procedures. While participating in all SOA in person meetings, webinars, teleconferences or side discussions, you should avoid discussing competitively sensitive information with competitors and follow these guidelines: Do not discuss prices for services or products or anything else that might affect prices Do not discuss what you or other entities plan to do in a particular geographic or product markets or with particular customers. Do not speak on behalf of the SOA or any of its committees unless specifically authorized to do so. Do leave a meeting where any anticompetitive pricing or market allocation discussion occurs. Do alert SOA staff and/or legal counsel to any concerning discussions Do consult with legal counsel before raising any matter or making a statement that may involve competitively sensitive information. Adherence to these guidelines involves not only avoidance of antitrust violations, but avoidance of behavior which might be so construed. These guidelines only provide an overview of prohibited activities. SOA legal counsel reviews meeting agenda and materials as deemed appropriate and any discussion that departs from the formal agenda should be scrutinized carefully. Antitrust compliance is everyone s responsibility; however, please seek legal counsel if you have any questions or concerns. 2

4 Presentation Disclaimer Presentations are intended for educational purposes only and do not replace independent professional judgment. Statements of fact and opinions expressed are those of the participants individually and, unless expressly stated to the contrary, are not the opinion or position of the Society of Actuaries, its cosponsors or its committees. The Society of Actuaries does not endorse or approve, and assumes no responsibility for, the content, accuracy or completeness of the information presented. Attendees should note that the sessions are audio-recorded and may be published in various media, including print, audio and video formats without further notice. 3

5 Digital Consumer Experience Stephen Abrokwah - Ph.D., ASA, CERA, MAAA AVP & Marketing Actuary, Swiss Re

6 ? Live poll question Where do you fall? 4

7 Declining policy sales After stabilizing for a few years after the financial crisis, the number of newly issued individual life policies fell below 10 million in 2013, the lowest level since the industry began tracking this statistic in 1940 USD 200, , , , , ,000 80,000 60,000 40,000 20,000 0 Individual life insurance sales Million Average policy size # Policies (right) Sources: ACLI, LIMRA, Swiss Re ER&C 5

8 Technology is framing consumer preferences Consumer satisfaction with online experience, by industry 7 6

9 ? Live poll question 4

10 ? Live poll question 4

11 You may not be alone In a study of millennial perceptions of insurance, Maddock Douglas found that Protection: Policy: 67% thought it meant security or defense 12% said condoms only 10% said insurance 61% said rules or government only 33% said insurance When asked about their emotions about insurance: 38% said frustration 33% said confusion 32% said anxiety 10 7

12 Relevant products Protection gap Closing the protection gap Excellent customer experiences Underwriting speed and efficiency To close the protection gap and achieve business growth, we must deliver three things. 11

13 Journey listening Listening provides a perspective that data can t. 12

14 Journey mapping Steve s journey 111 Days Total Steve wanted to replace his existing 20-year term life insurance policy with a new policy that would reduce his total coverage over time. Actions Applied Took med exam Waited Received policy Ready and Willing Ok, let s do this. Consumer emotions Hungry and impatient Are you kidding? Satisfied This is good medical info. Frustrated No! It s your job to get my medical records. Angry I don t have insomnia! Exhausted That s not what I wanted, but okay. Insurer opportunity Day 1 Day 7 Day 1 6 Day 17 Day 110 Day improve medical exam 2. get medical records faster 3. product relevance 13 10

15 Journey mapping Jill s journey A Millennial s imagined journey 40 Days Total The traditional industry experience Buy a term-life policy from an agent Actions Applied Took med exam Waited Received policy Willing but annoyed Can t I do this online? Anxious I hope everything is fine. Wondering Why is this taking so long? Frustrated Is this company still in business? Relieved It s about time! # Finally! Consumer emotions Day 1 Day 7 Day 8 Day 39 Day 40 11

16 Journey mapping Shelly s journey - The convenience experience 8 Days Tot al Shelly s actual journey Buy a term-life policy for a good price online. Actions Researched Applied Took Med Exam Received Policy Provided Feedback Curious Wow. Maybe I do need more LI. Hopeful Good price. No exam. I ll take it. Relieved Just 30 min. Easy peasy. Annoyed Huh?! Why do I need an exam? Impressed Ok. At least, it s easy to do More Impressed That was fast. Empowered I ll give Consumer emotions Pre-application Day 1 Day 2 Day 3 Day 7 Day

17 Accelerated underwriting Nate Schelhaas FSA, MAAA VP & Actuary Individual Life

18 Agenda AU description Setting expectations Honest feedback

19 Accelerated underwriting Process/model that determines whether or not to jet issue a policy

20 Why? Insurance gap Business market Process improvement Increased sales

21 Parameters Age: Size: $50,000 - $1,000,000 Products: Any retail

22 Checklist Build Medical condition U.S. resident* Blood pressure Cholesterol Drug use Family history Financial Criminal history Insurance history *Citizen or permanent resident 21

23 Data used TeleApp Disclosed information, reflexive questions MIB Motor Vehicle Records (MVR) Prescription (Rx) Impairment codes, e.g. pacemaker, nicotine License status, standard violation codes Prescription drug history 22

24 Expectations Actuarial Underwriting Distribution Random holdout 23

25 Expectations Large impact on experience 24

26 Advisor usage None 45% Checklist 20% All 35% 25

27 Data and reports Substitute facts for opinions Information Accountability Drive change in behavior 26

28 The frustrations Advisors Random holdout No explanation Approval percent Customers Different process No explanation 27

29 The successes Advisors Quick turnaround Increased close rates Efficient Customers Easy No one coming to house Fits my schedule 28

30 Convenient, cost effective way to get clients the coverage they need. Jenny 29

31 We couldn t believe how fast and simple the process was! Jason 30

32 Thank you

33 Applications of Predictive Modeling Craig Hanford - FSA, MAAA VP & Marketing Actuary, Swiss Re

34 Predictive analytics How did we get here? Traditional underwriting and sales processes are broken Process takes too long Expenses are too high Requirements are too invasive 33

35 Connected Devices Forecast to grow fivefold in the next 10 years Wearable devices by area of market focus, % of total

36 Different types of predictive models Propensity to Buy Identify customers who are more likely to buy a particular product. Improve customer s decision making process. Combine with Behavioral Economics for more effective client portfolio intervention services. Propensity to Survive Target underwriting effort accordingly to predict health risk of individuals. Streamline and automate underwriting process through predictive models. Propose new questions, improve current questions, or remove the ones which are no longer needed. Propensity to Lapse Predict lapse behavior and identify customers who are more likely to lapse. Understand and benchmark main lapse drivers across multiple portfolios. Enable the development and testing of successful retention strategies. 35

37 Predictive models for risk classification Most prominent use of predictive modeling for life insurance to date Potential types of predictive models Credit Health/ Prescription Social media Facial Analytics Genetics Others Goal is to be able to find a model(s) to assess risks effectively Can we price appropriately? Have we significantly improved the insurance buying process? 36

38 Mortality cost implications New accelerated approaches bring mortality cost much closer to fully underwritten in this example Non-Med UW ($$$) Accelerated UW ($$) Fully underwritten ($) Non-medical Underwriting No blood/urine Rx, MVR, MIB Higher Price to account for no fluids Accelerated Underwriting No blood/urine Rx, MVR, MIB Predictive model triage approach Prices closer to Fully UW due to P.M. Full Underwriting Paramed with blood/urine Rx, MVR, MIB Lowest Price Pricing differential depends on the choices carriers make in the design of their program Percent qualifying for AU Model type & thresholds of predictive risk scores Monitoring safeguards pre/post issue 37

39 Evaluating a predictive model Retrospective study Use your own data, as results can vary widely by company Examine how the model results vary by preferred, standard, rated, declined, not taken Risk classification using a model Risk classification using traditional UW Other tools (detailed application, prescription profile) can reduce differences Neither approach is perfect; understand how the model will classify risks and price accordingly 38

40 How are these models used? Generally use some form of triaging Depends largely on what proportion of risks you want to run through predictive model accelerated process, and desired competitiveness of pricing Three examples Triage based on a single predictive model Triage based on a multiple predictive models Non-triage, using predictive model to simply classify risks 39

41 Accelerated underwriting Single predictive model triage 40

42 Accelerated underwriting Double predictive model triage 41

43 Issues and considerations Single predictive model triage If you desire multiple preferred classes, how do you differentiate? Usually based on non-medical preferred criteria, which often have little variation between Preferred Best and Preferred This will lead to significant distribution shift to Preferred Best Could use the predictive model risk score to differentiate between preferred classes Allows you to dial in cut-points to desired risk class distribution May not see quite the extent of mortality differentiation between classes as typically seen in traditional underwriting 42

44 Predictive model, Non-triage 43

45 Issues and considerations Predictive model, Non-triage Potentially most efficient structure- quick with significant reduction in underwriting expense Possible implications: Price with higher levels of expected mortality Reject a higher proportion of applicants Specifically priced/ loaded product for non-qualifiers Increased uncertainty on the profitability of the block 44

46 Evaluating the model performance You should have an approach to understand the quality of risks qualifying through the use of the predictive model Waiting for credible mortality results to emerge is not recommended Two primary approaches are developing Random Hold-Outs Post-Issue APSs What could you do when you identify a concern? 45

47 Random hold-outs How to conduct random hold-outs 1). Pull ( hold out ) a certain percentage of applicants who would otherwise qualify for a best rate through the predictive model, and require them to go through traditional underwriting. 2). Closely monitor the number and type of adverse risks identified from traditional underwriting that would not have been caught by the model and the related program parameters. Benefits Incredibly valuable in learning lessons quickly Concerns Agents are frustrated by random hold-outs Expectation vs bonus 46

48 Post-issue APS Post-issue APS process For a certain percentage of insureds issued through the model/ program: obtain an APS after issue and attempt to evaluate the types of adverse risks not caught by the program parameters will likely require some additional authorizations adds extra expenses and underwriter involvement into a process specifically designed to reduce each What actions could you take if you found something really bad? Active rescissions requires an application question specifically related to the identified condition on which you would want to rescind 47

49 Cost Benefit Analysis 48

50 Cost-benefit analysis What: Comparison of underwriting expense savings against the additional expected costs from increased mortality resulting from predictive model. How: Review and understand differences that emerge by age, face amount, and gender. As face amount increases, expense savings may be limited, but expected mortality costs could increase significantly. Why: A good understanding of these details is recommended when setting maximum ages and amounts at which the predictive model will apply. 49

51 Example A: 10 year term product offered w/ no fluids (Non Med) at face amounts of $250,000 For ages below 55, Savings > Costs results in Net Savings For ages 55 and beyond, Costs > Savings results in Net Losses Assumptions: Mortality Data: LMS data on PV (Mortality Costs) based on Accelerated Underwriting parameters (i.e. No fluids, no APS, no PM/ MD) Cost Assumptions: Lab testing cost; Underwriter Efficiency cost; Lost Premiums estimate 50

52 Example B: 10 year term product offered using a predictive model at face amounts of $250,000 For all ages Savings > Costs, demonstrating the value of the Predictive Model Assumptions: Mortality Data: LMS data on PV (Mortality Costs) based on Accelerated Underwriting parameters (i.e. No fluids, no APS, no PM/ MD) Cost Assumptions: Lab testing cost; Underwriter Efficiency cost; Lost Premiums estimate 51

53 Smoker Prediction Model Protective value analysis Some Key Metrics (Out of a sample of 1,000 applicants) 20% (37) 54% (20) 7% (55) Initial misrepresented smokers Misrepresented smokers detected Total applicants put through testing (self-reported smokers not tested) 52

54 Summary Our success as an industry requires creating an improved experience for our customers. A non-invasive underwriting process and competitive products are a good start, but we need to be aware of other considerations as well. A successful accelerated underwriting program will have: Clear and specific goals and expectations for all parties involved in the process Active monitoring of the program to understand the quality of risks being accepted. New models and other data sources will continue to emerge in the near future Actuaries will have a significant responsibility to appropriately interpret this information and to ensure that our products incorporate those tools and remain priced appropriately. We will be confronted by many challenges, but we will find many exciting opportunities ahead too! 53

55 QUESTIONS? 54

56