End to End Business Involvement for Actuaries in the Insurance Industry

Transcription

1 Joint Regional Seminar 2015 A Promising Profession and a Profession with Promises End to End Business Involvement for Actuaries in the Insurance Industry July 2015

2 How Much is a Big Mac? 2

3 $3.25 Cross-Price Elasticity $2.95 3

4 Airfares Pricing due to Demand Variation Higher Price Lower Price Time to Travel Closer to travel date Further out from travel date Time of booking Peak Hours (morning, lunch, late night) Non-peak Hours Location Suburbs with frequent travelers Extracting Consumer Surplus Suburbs with less frequent travelers Safari vs Internet Explorer Safari on Apple devices Internet Explorer on PC Note: For illustration only 4

5 Price Elasticity and Consumer Surplus Extracting Consumer Surplus Different Price Elasticity Near Burger King Optimal Prices No Competition 5

6 How Does this Relate to Insurance? 6

7 1. Is a person looking/need/willing to buy insurance? 2. How much is the person willing to pay for it? 3. What kind of product, benefits, offerings are needed? 4. How do we know this? 5. How can we ensure the portfolio is profitable? How do we measure profit? 7

8 Learn from Data (Trial and Error, pilot programs) Data Analytics Propensity Modelling, Claims Modelling Response Rates, Lapse Rates, Customer Lifetime Value Develop Customized Strategies based on Predictive Results 8

9 Willingness to Buy - Life Events Data Analytics Customer Lifetime Value 9

10 Data Extraction, Analytics, Pricing, Underwriting, Sales Data Collection Data Creation Product Development Pilot Programs Data Cleansing & Data Correlation Big Data concept Risk Modelling Claims, Trends, Incidence, Severity, Perils Elasticity Modelling Lapses, Response Rate, Optimal Price Point Business Insights Product Design, Distribution, Upsell, Customer Lifetime Point of Sales System + Online Platform Optimization Insurance Sales Feedback Loop via Monitoring 10

11 Point of Sale [Lead Generator ++] Data Origination Supermarket customer data Expenditure data (bank, credit card) Pharmaceutical data Medical data (clinics, hospitals) Internet usage data Location data (GPS, travel cards, phone) Frequent traveler data Supermarket Bank Pharmacy Point of Sale Initiation Policy Low-cost Guaranteed issued product using predictive UW and customer elasticity analytics from data Products include Hospital cash, ROP, PA, Travel PA, Low SA term life and/or cancer Upsell/Cross-Sell Follow-up policy, higher value, more detailed underwriting (but partially done by original customer data, and emerging experience from initiation policy) Utilize customer analytics for successful upsell strategy For example premium price point, value of money, perceived value benefits End-to-end customer experience Brand affinity Customer loyalty Maximize customer lifetime value Ultimate Goal 11

12 Point of Sale [Lead Generator ++] Conglomerate Bank + Retail Relationships Loyalty Card Supermarket Rewards Pharmacy Household Electronics Bank Customers Credit card Insurance Company Utilities Telecommunications Wealth management Insurance 12

13 Point of Sale International Examples International Examples >200 kiosks in Walmart stores in South Carolina and Georgia, then Pennsylvania 1 in 5 willing to purchase life insurance from a retail outlet like Costco or Walmart How it works customers can bring the life insurance in a box a prepaid Discover card to the checkout. To activate, the buyer must call MetLife and answer six questions. If there are no yes answers, the one-year term policy is activated. If you have one yes answer, you can get a one-year accidental death policy or your money back. The person hasn t technically bought the insurance until the call. when people call, they are offered a quote for level term, and 20% end up going with level term instead of the one-year, non-renewable policy. Coles is one of the two largest supermarket chains in Australia Metlife launched Life insurance product sales through Coles in May 2014 with an aim of offering cost effective products: Consumers overestimate cost of life insurance by almost double >50% of Australians don t have life insurance because they think its too expensive Metlife aims to reach out to everyday Australians ABS statistics show that underinsurance is AUD800bn Product features: Premiums from $1 per week Low sum insured of 50k up to 1m Death cover with terminal illness, funeral & optional children s cover Quote in 60 seconds No medical or blood tests Largest online store in China part of one of the largest global IT firms Taikang teamed up in 2012 and is currently selling simple CI (female cancer, juvenile CI) and PA products Simplified or no Underwriting 13

14 International Online Life Insurance Examples Transformation of distribution model to sell Direct to customer Quick turnaround vs historical 2-week paper application process Shortage of underwriters in market Uses an online UW system Underwrite applications online and/or tele-underwriting No human underwriters employed Fully underwritten lengthy questionnaire with scope to reduce application time No medical tests or body fluid tests Flexible offer made - loadings, exclusions, alternatives e.g. accidental death Cover is issued immediately Product offering up to AUD1.5m for Life/TPD, AUD0.5m for CI Almost 130M of in-force revenue after 5 years of operations AUD 70M new business in 2011 Big brand, lots of advertising, lots of distribution Model transformation Awareness Funeral to both funeral and term Aggregator site with real product positioned strongly More than 150,000 customers, 50% first-time buyers 25% <30yo; 75% <40yo Big board advertising television and Tokyo subway Uses social media eg twitter to raise awareness and build company brand Online application process Attractive site great usability and customer experience Products include Term life, Whole life, Hospital cash, Disability Income Almost 400M of in-force revenue after 15 years of operations AUD 65M new business in 2011 Big brand, lots of advertising, lots of distribution Model transformation Awareness to brand Funeral to term Direct response to affinity 9 players, targeting affluent Indians Big brand, strong marketing and advertising, creates awareness Products being sold: Simple, attractive and well priced products: Term life, Hospital Cash, CI starting Process varies significantly among players (wet signatures not required) Moving from online customer acquisition/sales to true e-business Policy Bazaar (web aggregator) is driving more than 1/3 of all sales now 14

15 How can Actuaries help? 15

16 Currently, Actuaries provide: Product ideas and Product Development Experience studies, Pricing basis Profit testing, ALM, VNB/MCEV analysis 16

17 Actuaries can incrementally add-value by providing: 1.Data cleansing and Predictive Modelling 2.Predict Response Rates; Customer Retention; Pricing from Customer Demand/Willingness-to-Pay; Lifetime Product Up-sell etc 3.Produce valuable business insights to maximize return on marketing investment, optimizing sales, enhance customer lifetime value and portfolio profits 4.Develop point-of-sale systems; online platforms; Customized Distribution and Sales Strategies 17

18 Predictive Modelling Step by Step 1. Extract Sample of Customers 2. Split sample into Estimation Sample and Hold-out Sample Estimation sample: Perform statistical analysis Hold-out sample: Investigate predictive power of the model 3. Estimate Response Model using Estimation Sample 4. Predict Response probabilities for the Hold-out sample 5. Evaluate the model Compare predicted response probabilities in hold-out sample to actual response behaviour Lift/gains tables, Charts 6. Develop call/mail/push marketing strategies based on predicted results 18

19 Data Modelling, Regression 19

20 Evaluate Model Predictive Power, Improved Probability of Success 20

21 Scoring, Gains Charts, Business Insights Business Insight: Marketing cut-off point 21

22 Conclusion Current Actuarial Practice Product Development Experience Analysis, Pricing Basis Profit Testing, ALM VNB/MCEV Continue to perform traditional actuarial pricing Value Adding Data Analytics Data Extraction, Data Cleansing Pilot tests, Trial and Error Predictive Modelling, Regression Evaluate models, sampling Lift and Gains Enhance value to the business via Predictive Modelling to gain added insights from customer behaviour Business Insights, Strategy Customized products to the target customers at various life stages Optimal Upsell timing Pricing based on customer elasticity Enticing discounts based on individual health improvements and lifestyle changes Portfolio management to ensure top line growth and maximize profitability 22