2013 Seminar for the Appointed Actuary Colloque pour l actuaire désigné 2013 Session/Séance: Session 4 (Life) Predictive modeling Uses in decision making Chris Stehno Speaker(s)/Conférencier(s):
The Evolution of Business Analytics In the past 10 years, business analytics has rapidly evolved from the status of back-room specialty to a core strategic capability with transformative potential. Today, big data and data visualization are key themes. 1960s-90s Business analytics is primarily a collection of siloed specialties such as market research and actuarial science. 2000s Widespread recognition that analytics helps both business-people and customers make better decisions. Today The explosion of data about nearly every aspect of our lives has become one of the major issues of the day.
What is Analytics? Analytics is using data to generate predictive insights to make smarter decisions that improve performance of businesses and drive strategy to outlast the competition Foresight Understand the signals being generated across your ecosystem to shape the future Insight Use data from within the organization to drive changes here and now Hindsight Conduct rearview mirror assessments based on data generated by operations Hindsight Insight Foresight Predictive and Prescriptive Descriptive Optimization Algorithms Simulation and modeling Quantitative analyses Advance forecasting Role-based performance metrics Exceptions and alerts Slice and dice queries and drill downs Management reporting Enterprise data management
Big Data: Digitization of Everyday Activities Many of these analytic solutions have relied on Big data, which generally refers to datasets of structured and unstructured data that are so large and complex that they create significant challenges for traditional data management and analysis tools in practical timeframes. Petabytes Terabytes Use Generated Content User Clickstream Web logs BIG DATA Signals Mobile Web Sentiment Social Network External Demographics Web Analytics A/B testing Big Data Characteristics: Variety Velocity Volume Complexity Business Data Feeds Gigabytes Offer history Dynamic Pricing Affiliate Networks Images/Audio/Video Megabytes Kilobytes Customer Analytics Financial / Operational / Risk Analytics Purchase detail Purchase record Payment record Segmentation Offer details Customer Touches Support Contacts Search marketing Behavioral Targeting Dynamic Funnels Speech to Text Product/Service Logs SMS/MMS Exploiting the big data opportunity requires aligning information capital, human capital, and organizational capital to build a culture of disciplined decision-making.
The Evolution of Insurance Analytics Advanced analytics and predictive modeling have become mainstream over the last 15 years in several industries within financial services. Property and Casualty insurers have been ahead of life and health insurers in the integration of advanced analytics into core operations. Today 1990s Credit Scoring -an early bellwether of the disruptive power of data in insurance. 2000s Predictive modeling transforms the P&C industry and actuarial profession. Analytics- powered underwriting, and claim triage, cross-sell. Analytics is viewed as a core strategic capability. Increasingly granular focus on the customer.
Banks and P&C Insurers are Driving Value through Predictive Analytics; Life and Retirement are Exploring Financial service firms have embraced Predictive Analytics for its ability to help predict the needs and behaviors of customers, forecast future business outcomes, and uncover fraud and financial risk Banks assess existing customer behavior to help refine customer segmentation strategies, drive efficient cross-sell, improve customer retention, and identify credit and fraud risks Property and Casualty Insurers use Predictive Analytics to more efficiently assess risks / underwrite, identify potential policy sales, and score claims to improve decision-making and resource deployment Life and Annuity Carriers are using Predictive Analytics to drive significant segmentation in the underwriting process, improve in-force retention, cross-sell and up-sell, and improve agent segmentation, targeting, and support Predictive Analytics in Financial Services Banking Property / Casualty Insurance Life Insurance & Annuities Retirement Projecting profitability Agent recruitment Application triage Roll-over Targeting underserved segments Credit risk analysis Card fraud protection Customer retention Product pricing Targeted marketing and segmentation Claims management Lead generation In-force management Targeted retention Cross-sell & up-sell Agent recruitment LTC claims management Retirement Wealth management Sales segmentation Product optimization FR Retention/Recruit Emerging
Current Applications of Advanced Analytics In Health Risks
An Example in Preferred Underwriting Which of the three applicants deserve Super Duper Preferred category?
Enhancing Risk Selection Expanding the data elements from internal and external sources provides a more detailed view and opportunity for advanced analytics Beth Tom Sarah Traditional Data 45 years old Currently working for a large manufacturing employer No past notable medical history Allergy Rx Acceptable BMI Lives in Mid-West 44 years old Works for a national distributor No notable past medical history No Rx Acceptable BMI Lives in the South 44 years old Works for a regional retailer Knee surgery two years ago No Rx Acceptable BMI Lives in Northwest Lifestyle Based Data Set Length of residence 4 years Lived in same hometown for 15 years Currently renting Commuting distance 45 miles Works as admin assistant Divorced with no children Foreclosure/bankruptcy indicators Avid book reader Walks for health Purchases diet and weight loss equipment Interest in self improvement High television consumption indicators Fast food purchaser Low Regional Economic Growth 20 years work experience with same employer Manager level position Owns home Has lived in hometown as his life Commuting distance 2 miles Revolve large monthly balances Excellent financial indicators Married with 2 children age 16 & 14 Suburban Striver Psychographic Cluster Avid outdoor enthusiast Avid golfer High Regional Economic Growth Length of residence 2 years New to town Renting Commuting distance 1 mile Reading: design and foreign travel related magazines Urban Single Cluster Premium Bank Card Good financial indicators Active in civic/community Active lifestyle runner, biking, tennis, aerobics Healthy food choices Little to no television consumption Med. Regional Economic Growth Predictive models built from these and hundreds of other data elements can better quantify the likelihood and reasoning of future health and morbidity events.
Enhanced Predictive Analytics Informs Risk and Marketing Decisions Predictive Analytics use new and traditional sources of information to quantify the likelihood and reasoning behind future risk events Beth Tom Sarah Risk Assessment The pool of candidates who score similar to Beth will have increased medical claims of 18% Tom falls into the pool of candidates that are near expectations on morbidity assumptions The pool of candidates who score similar to Sarah will have reduced medical claims of 15% Top five Reasons Long commute Poor financial indicators Purchases tied to obesity indicators Lack of exercise Strong personal ties to community/location Avid outdoor enthusiast Avid golfer Average commute High activity indicators Good financial indicators Healthy food choices Little television consumption Foreign traveler Collect additional information and send to senior most underwriter No changes Actively pursue for new business and retention efforts Possible Actions Layering non-traditional data yields insights that cannot be captured from the use of only traditional data.
Vendors and Data Available for Predictive Analytics Companies who are succeeding in advanced analytic analysis are doing so by their commitment to exploring new data. This commitment has resulted in an approach that leverages the use of both internal and external data to achieve maximum segmentation. Disability Data US Hospital Directory Nursing Home Data Medical Provider Data Hosptial Visit Statistics Doctor Practice Data Health Interest Data Crime Statistics Hail Vector Data Storm Events DB Climate Data Geographic Mapping Firehouse Data Fire Incident Data Representative Data Categories National Indices Deloitte Disease States Purchase Behaviors Wage Data Wealth Indicators Unemployment Stats EEOC Complaints DB Ec. Freedom Index Aggregated IRS Data Occupational Codes 17 lifestyle diseases Including: Diabetes, Cancers, Cardiovascular, Depression/Mental, Hypertension, etc. Auto Data Carfax Vehicle History Motor Vehicle Reports Auto Injury / Loss Data Driver Device Usage Road Rage Survey VIN Decoding Data Fed. Case Law DB Florida Tax Records Lit. Trends Survey Lawsuit Climate Data DUI/DWI Laws CA/FL Lawyer Data Tort Liability Index Purchase Propensities Spend by Category DTC Spend by Retailer Brand Usage Statistics Retailer Trans Data Purchase Triggers Ailment & Discharge Automobile Lifestyle Clusters Lifestyle and Life Traits Working Mothers Active Seniors High-Tech Segments Life Stage Clustering Demo. Census Data Geographical Sets Judicial / Legal Commercial Data Bus. Hazard Grade Bus.Financial Statistics UCC Filings Small Bus. Data Bus. Credit Score OSHA Bus. Data Tax Liens & Bankruptcy Data Vendors Acxiom AM Best AMA American Housing Survey American Tort Reform Foundation Burueau of Labor Statistics Cap Index Carfax CDS Hail Database Census Point Choicepoint Corporate Research Board DataLister Directory of US Hospitals Dun & Bradstreet EASI Analytics EEOC Equifax ESRI Experian Fastcase Legal Research System Florida Tax Assessment Records Fulbright Lititgation Trends Survey Insurance Information Institute Insurance Institue for Highway Safety Internal Renvue Service Knowlege Based Marketing (KBM) Lawyer Data Florida & California LexisNexis Martindale/Hubble Attorney Listing MRI Purchasing Propensities NFIRS National Fire Reporting NHTSA OSHA US Census
Lifestyle Based Analytics for Morbidity and Mortality Assessment Third party marketing datasets are used to develop health-related algorithms. These datasets include over 1,000 fields of data and the match rate with a client s policyholders is typically around 95% based only on the individual s name and address. 3 rd Party Marketing Data Types Disease State Algorithms Survey Data Self-reported information collected over the last 18 months Contains many lifestyle elements Observed Data: Basic individual and household demographics Age, sex, number and ages of children, marital status Occupation categories, education level Financial information Income level, net worth, savings and investments Home value, mortgage value Lifestyle data Activity running, golf, tennis, biking, hiking, soccer, tri-athlete Inactivity TV, mail-order, computers, video games, casino gambling Diet, weight-loss, exercise, cooking, gardening, health foods, pets Small Area Characteristics: Matched to carrier route modeled data Reports average data for that route Approximately two city blocks Deloitte Consulting s Proprietary Disease State Algorithms Using only third-party data Deloitte Consulting has built algorithms to provide insights into individuals afflicted with 20 plus lifestyle diseases (e.g. diabetes, female cancer, tobacco related cancer, cardiovascular, depression, etc.) which impact morbidity.
Lifestyle-Based Analytics and Improved Morbidity Risk Evaluation Lifestyle-based analytics ( LBA ) focuses on identifying increased morbidity and mortality risks for lifestyle based diseases. According to the US Surgeon General, lifestyle based diseases account for over 70% of US of healthcare expenses and subsequent deaths. Lorenz Curve for Neoplasm Female Sample Examples of lifestyle-based diseases include: diabetes, cardiovascular, cancer, and respiratory. This chart demonstrates LBA s ability to provide insights into future cancer claims in a healthy female population. The blue arrow points to LBA s ability to provide insights into future cancer claims in this same population. In this case, 20% of LBA s highest risk members accounted for almost 60% of the future cancer claims. The red arrow points to traditional underwritings identification of cancer claims in this healthy population. In this case, 20% of the highest risk members accounted for 30% of the future cancer claims. The black arrow points to a random distribution. In this case, 20% of the people will have 20% of the future cancer claims.
Representative Results Close Approximation of Traditional Underwriting Results for Best Risks Applying an Application Triage Algorithmic Solution using application data, MIB, MVR, Rx and other 3 rd party data, together with underwriting rules established by the insurer, may provide results that are similar to fully underwritten decisions for a significant portion of the business predominantly the higher scoring segments. The graph below is illustrative of results based on our experiences but actual results will vary. Algorithmic Solutions vs. Traditional Underwriting Requirements Results ILLUSTRATIVE 10X Mortality Rate Continue to use traditional underwriting requirements Apply insurer s underwriting rules to reduce requirements and processing time Pop. Avg. X Low Algorithmic Solution Score High Algorithmic Solution Score Algorithmic Solution Expected Mortality Traditional Underwriting Requirements Expected Mortality
Review of Algorithmic Solution Results After running the validation sample through the Algorithmic Solution, a detailed review of the results against the actual underwriting class is conducted. During the review process disconnects (i.e., where the Algorithmic Solution puts an applicant in a different underwriting class then the case was actually issued) Deloitte Consulting works closely with the client to: review these disconnects identify the cause of the discrepancy categorize these difference make adjustments to the Algorithmic Solution or potentially add underwriting rules to eliminate such disconnects.. On past projects, this review of underwriting has shown us that differences can be attributable to a variety of reasons: ILLUSTRATIVE 10% Underwriting exception was made to issue the policy at a better class. 55% 15% 20% Underwriting based on results of blood, urine, or other post application requirements. Underwriter error Data input translation error of paper to electronic format
Current Applications of Advanced Analytics In Life Insurance
Predictive Analytics Applications Across the Insurance Lifecycle The need for Predictive Analytics in life insurance is increasing, driven in part by data availability, competitive pressures, financial pressures, and consumer expectations. Deloitte Consulting s Analytics Enabled applications can create value to our clients across the entire insurance policy lifecycle. Simplified Insurance Lifecycle Obtain Agent / Recruitment Retain Sales / Retention Force Design & Develop Products Marketing Campaigns Assess Client Needs / Illustrate Submit & Process Application Underwriting Requirement In-Force Management Claims Management Agent Recruitment / Retention Improves efficiency of Agent recruiting Improves effectiveness of Agent retention Reduces costs by reducing agent turnover Target Market (Direct to Consumer) / Lead Generation Enhanced customer segmentation IP (Likely to Qualify) More efficient use of budget / resources Optimizes new Accounting DAC Guidelines Application Triage Eliminates timeconsuming and physically invasive tests for certain applicants Streamlines application review process (1 3 days) Reduces medical expenses Improves ease of doing business Improves customer experience Enhances underwriter productivity In-Force Management Enhanced client segmentation IP (Likely to Qualify) Identifies those most at risk of lapsing and those most qualified (spend resource / budget to retain through pro active education / marketing programs) More efficient and effective Cross-Sell / Up-Sell programs Optimizes new Accounting DAC Guidelines Claims Management Improves fraud detection Improves exposure analysis The value propositions offer benefits and process improvements across the Policy Lifecycle.
Algorithmic Solutions Broader Business Applications Agent Recruitment and Retention An additional high-value area where Predictive Analytics might provide competitive advantage is in the area of recruiting and retaining and agents. Simplified Insurance Lifecycle Agent Recruitment / Retention Design & Develop Products Marketing Campaigns Assess Client Needs / Illustrate Submit & Process Application Underwriting Requirement In-force Management Claims Management Agent Analysis Illustrative Results The analysis can be based upon internally available information: Not-in-Good-Order Field Underwriting Business Quality Requirements Turnaround Call Center Questions / Calls Cycle Times Sales Patterns Algorithms score weighted factors and can provide on-going monitoring for at-risk producers and enable more targeted coaching / assistance Ironically, the most common obstacle is the failure to methodically capture key data Chance of longer term agent success Lower Scoring Sales Force 40% ILLUSTRATIVE < 20% chance of meeting company s definition of a Successful Agent Higher Scoring Sales Force 60% 2.5 X more likely to meet company s definition of a Successful Agent Pop. Avg. Benefits Rules based candidate prioritization Expand and diversify recruiting pool Retention indicators Low Score High Score
Marketing Campaigns Target Marketing, Lead Generation, Cross-Sell, Up-Sell Predictive Analytics can also provide opportunities to more efficiently and effectively target solutions to consumers and customers. Using Likely to Buy in tandem with Likely to Qualify (new to the market) not only helps you segment those who are likely to buy a policy, but are also likely to qualify for that policy. Simplified Life Insurance Cycle Obtain / Retain Sales Force Design & Develop Products Market to / Marketing Identify Campaigns Clients Assess Client Needs / Illustrate Submit & Process Application Underwriting Requirement Manage and Service Inforce Process Claims & Disburse Example Advanced Likely to Buy Lift Curve 100% 80% 60% 40% Unlikely to Buy More sophisticated models including multiple models by product feature and population demographics can expand the cohort of unlikely to buy 20% 0% -20% 1 2 3 4 5 6 7 8 9 10-40% -60% -80% -100% Benefits Deeper segmentation of consumers and customers 60% 40% Example Likely to Qualify Lift Curve More cost effective and productive marketing campaigns 20% 0% -20% -40% -60% 1 2 3 4 5 6 7 8 9 10 Unlikely to Qualify Approximately 85% less likely than average to Qualify Better agent and customer experience -80% -100%
Application Triage Application Triage simplifies the application process for a large percentage of applicants (usually 30% to 60% Simplified Insurance Lifecycle Obtain Agent / Recruitment Retain Sales / Retention Force Design & Develop Products Market to / Marketing Identify Campaigns Clients Assess Client Needs / Illustrate Submit & Process Application Underwriting Underwrite Requirement Risk In-Force Management Process Claims Claims & Management Disburse Application completed Tele-Interview completed if required) Additional Data Sources: MIB Rx Predictive Analytics Enabled Application Triage Process MVR 3rd Party Marketing Algorithm Raw Score Insurer s Underwriting Rules Expedited ILLUSTRATIVE Medical tests not required Policy issued Processing time - several days Traditional Obtain and analyze medical test results Policy issued or denied Processing time - several weeks Benefits Eliminate time-consuming, expensive and physically invasive tests for certain applicants Streamline application review process Improve ease of doing business
Algorithmic Solutions Broader Business Applications In-Force Management Some companies could enhance the management of their substantial in-force block, where Predictive Analytics are typically focused on business losses and lapses rather than actually improving the business. Simplified Insurance Lifecycle Agent Recruitment / Retention Design & Develop Products Marketing Campaigns Assess Client Needs / Illustrate Submit & Process Application Underwriting Requirement Manage and In-force Service Inforce Management Claims Management Inforce business Algorithm Score Benefits Non-traditional data appended Likely to Lapse Low Score High Score Identify compounding components of at riskcustomers Develop, deploy data driven pro-active intervention strategies Health Risk Algorithm Score High Score Focus retention rerouces on the most qualified customers most likely to lapse Continue current retention processes Improved mortality by focusing retention efforts on best risks Low Score Continue current retention processes Spend fewer retention resources where they will have the least effect
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