Predictive Analytics for Life Insurance: How Data and Advanced Analytics are Changing the Business of Life Insurance Seminar May 23, 2012

Transcription

1 Predictive Analytics for Life Insurance: How and Advanced Analytics are Changing the Business of Life Insurance Seminar May 23, 2012 Session 1 Overview of Predictive Analytics for Life Insurance Presenter Doug Welch

2 2012 Society of Actuaries Conference Predictive Analytics for Life Insurance Doug Welch Deloitte Consulting Deloitte Consulting LLP May 2012 Emerging Marketplace Significance Visibility into analytics can assist business leaders in making decisions more accurately, objectively and economically a rapidly developing consensus in business, education, law, medicine and even professional sports. There has been significant recent attention to the increasing use of analytics across disciplines: Recent Business Publications # 1 National bestseller Moneyball: The Art of Winning an Unfair Game Michael Lewis, 2005 NY Times Bestseller Freakonomics: A Rogue Economist Explores the Hidden Side of Everything Steven D. Levitt, Stephen J. Dubner, 2009 Competing on Analytics: The New Science of Winning Thomas H. Davenport, Jeanne G. Harris, 2007 Super Crunchers: Why Thinking-By-Numbers is the New Way To Be Smart Ian Ayres, 2008 The Numerati Stephen Baker, 2008 Business Week, Managing by the numbers: How IBM improves productivity by tracking employees every move, Stephen Baker, September 8, 2008 The Economist,, data, everywhere A special report on managing information, February 27, 2010 The Wall Street Journal, Insurers Test Profiles to Identify y Clients, November 19, 2010 Many insurers may be missing the opportunity to drive value through analytics, and those who explore these capabilities could gain a competitive advantage. -1- Copyright 2012 Deloitte Development LLC. All rights reserved. 1

3 ic Solutions Development and Deployment While an insurance-based ic Solution approach has many applications, today we will focus on New Business Triage. Below is a high-level overview of the process. Using advanced statistical analysis, organizations are better able to segment cohorts of people or policy holders. New Business Potential Sources Segmentation Analysis Triage Traditional internal data sources Customer History Medical Non-traditional data sources Consumer Financial Synthetic Variables Lifestyle Household Analytical Solution aggregation and data cleansing Evaluate variables for correlation to target variable Develop ic Solution applicants Eliminate timeconsuming and physically invasive tests for certain applicants Streamline application review process Improve ease of doing business Important Note: may be subject to various laws and regulations (e.g., FCRA, state privacy laws). Deloitte Consulting does not provide legal advice. Clients must consult their legal counsel to determine whether particular data may be obtained and / or used in a particular context. -2- Copyright 2012 Deloitte Development LLC. All rights reserved. Possible for Life Insurance s An insurance-based ic Solution approach starts with all of the traditional data that can be captured within the first 48 hours. This information can then generally be supplemented 1 with a variety of external datasets. This approach can assist in segmenting those who may otherwise appear indistinguishable from one another. category Basic demographics Medical history Family history Paramedical examination Fluids Height/weight Other medical/interview Telephone interview APS/medical records Treadmill Test EKG MIB (Medical Information Board data) Rx (prescription data) Traditional Underwriting New Business Triage Target Marketing / Cross- Selling s Driving record (MVR) Traffic conviction history Auto accident history Existing policy data? 3rd party data 1 Timing depends on MVR delivery (slower in some states) and arrangements with third-party data providers -3- Copyright 2012 Deloitte Development LLC. All rights reserved. 2

4 ic Solutions Business s Analytics Enabled Underwriting Applying a New Business Triage ic Solution using application data, MIB, MVR, Rx and other 3 rd party data, together with underwriting rules established by the insurer Obtain / Design & Market to / Assess Submit & Retain Sales Develop Identify Client Needs Process Force Products Clients / / Illustrate force Disburse ic Solutions-Enabled Triage Process completed Expedited ILLUSTRATIVE Tele-Interview completed if required) Raw Insurer s Underwriting Rules Medical tests not required Policy issued Processing time - several days Traditional Additional Sources: Obtain and analyze MIB Rx MVR 3rd Party Marketing medical test results Policy issued or denied Processing time - several weeks -4- Copyright 2012 Deloitte Development LLC. All rights reserved. ic Solutions Business s Analytics Enabled Underwriting 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. Obtain / Design & Market to / Assess Submit & Retain Sales Develop Identify Client Needs Process Force Products Clients / / Illustrate force Disburse ic Solutions vs. Traditional Underwriting Results 10X tality Rate XMort Pop. Avg. Low Model Continue to fully underwrite application Apply insurer s underwriting rules to reduce requirements and processing time High Model Expected Mortality Fully Underwritten Pricing Expected Mortality -5- Copyright 2012 Deloitte Development LLC. All rights reserved. 3

5 ic Solutions Business s Agent Success and Retention An additional high-value area where analytics might provide competitive advantage is in the area of retaining and enabling agents. Obtain / Design & Market to / Assess Submit & Retain Retain Sales Develop Identify Client Needs Process Sales Force Force Products Clients / Illustrate force Disburse Agent Analysis 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 s score weighted factors and can provide ongoing monitoring for at-risk producers and enable more targeted coaching / assistance Ironically, the most common obstacle is the failure to methodically capture key data rm agent success Chance of longer te Lower Scoring Sales Force 40% Low ILLUSTRATIVE < 20% chance of meeting company s definition of a Successful Agent Illustrative Results Higher Scoring Sales Force 60% 2.5 X more likely to meet company s definition of a Successful Agent High Pop. Avg. -6- Copyright 2012 Deloitte Development LLC. All rights reserved. ic Solutions Business s Targeted Client Marketing ic Solutions can also provide opportunities to better target solutions to consumers, including the common challenge of cross-selling to existing customers. Obtain / Design & Market to / Assess Submit & Retain Sales Develop Identify Client Needs Process Force Products Clients / Illustrate force Disburse Mass-marketing via Customer response packet via mail and Underwriting Percent of Decile 45% 40% 35% 30% 25% 20% 15% 10% 5% 0% 15% 25% 11% 12% Market to target customers to limit sunk costs down the road, underwriting declines and not-takens 8% 6% 5% 5% 5% 4% 8% 7% 4% 6% 5% 4% 3% 3% Analyzed Worst s Decline Substandard 3% 1% Analyzed Best s -7- Copyright 2012 Deloitte Development LLC. All rights reserved. 4

6 ic Solutions Business s In-Force Policyholder Analytics Some companies could enhance the management of their substantial in-force block, where analytics are typically focused on business losses and lapses rather than actually improving the business. Obtain / Design & Market to / Assess Submit & Retain Sales Develop Identify Client Needs Process Force Products Clients / Illustrate force Disburse Inforce business Nontraditional data appended Retention Low High Health High Low Focus retention rerouces on the healthy customers most likely to surrender Continue current retention processes Continue current retention processes Spend fewer retention resources where they will have the least effect -8- Copyright 2012 Deloitte Development LLC. All rights reserved. ic Solutions Outlook The life insurance industry has historically leveraged some form of analytics in various areas, including market research The analytics arms race is intensifying, driven in part by financial pressures, consumer expectations, competitive actions, and information availability The potential analytics transformation for life insurance could be similar to what has been experienced in other industries including: Retail: Individual consumer analysis Distribution: Logistics sophistication Property & Casualty: Underwriting models On-line Services: Ease of use analytics Each life insurance organization will be faced with important questions and tough choices Retaining the analytics status quo is essentially a competitive choice QR Code to access Deloitte Consulting article on analytics and changing expectations in life insurance -9- Copyright 2012 Deloitte Development LLC. All rights reserved. 5

7 Doug Welch (312) Deloitte Consulting, LLP 111 South Wacker Chicago, IL Copyright 2011 Deloitte Development LLC. All rights reserved. 6