69 PD Underwriting Issues for Group Life and Disability Insurance. Moderator: Peter A. Heinrichs, FSA, MAAA

Transcription

1 69 PD Underwriting Issues for Group Life and Disability Insurance Moderator: Peter A. Heinrichs, FSA, MAAA Presenters: Susan L. Ebertz Michael F. Vassar Mark R. Yoest, FSA, MAAA

2 Underwriting Trends in Group Insurance Strategic Considerations and Predictive Modeling SOA Spring 2014 Health Meeting Session #69 Mark Yoest, FSA, MAAA Deloitte Consulting LLP June 24, 2014

3 Table of Contents Acquisition Pricing Analytics as a Solution for Group Insurance Underwriting Analytics: Driving Data Maturity in Group Insurance Analytics: Driving Analysis Maturity in Group Insurance Book of Business Management Predictive Analytics - 2 -

4 Acquisition Pricing

5 Premium Acquisition Pricing Group Insurance pricing often involves discounting at the point of sale in order to acquire the group, followed by a series of increases in the premium charged upon renewal. The amount of initial discount and resultant renewal rate increases greatly impact lapse experience and the ability to maintain profitability over the life of the contract Actual premium charged Level target lifetime premium Initial Discount Policy Duration 4

6 Premium Discounting Decomposed The initial discount can be decomposed into two key components: Risk Assessment Adjustments Actual premium charged Initial Discount Strategic Business Adjustments Level target lifetime premium Policy Duration Risk Assessment Adjustments a) Determination of past experience b) Credibility of past experience c) Qualitative / subjective adjustments Strategic Business Adjustments a) Class of policies (e.g., all sales in a new region) b) Case-specific adjustments (e.g., sale to a targeted broker) 5

7 Price Price Economic Underpinnings of Acquisition Pricing Differences in customer purchasing behavior between new and renewal sales creates market dynamics that lead to acquisition pricing New Business Renewal Business Value unknown for non-incumbents High Low? High Low Value known by buyer upon renewal Low Other Dimensions High Low Other Dimensions High The customer buys not only on price, but on other dimensions Value of other dimensions unknown for all sellers except the incumbent Buyer tends to discount the value of other dimensions for non-incumbents, thereby focusing more on a lower price The buying decision contains significant information asymmetry many sellers exist whose prices and cost bases are typically unknown to other sellers The customer knows or can influence the value of other dimensions delivered by the incumbent Uncertainty of value of the other dimensions does not impact buyer s evaluation of price The buying decision is one of deciding whether to continue with a substantially unchanged product but at a different price The incumbent no longer has to account for inertia and switching costs in discounting its price For the above reasons, acquisition pricing is effectively the norm in Group Insurance 6

8 Complications with Acquisition Pricing in Today s Market Complication Breakdowns in underwriting authority levels Rate guarantee periods as a negotiation point Implication Either through lax controls or suspension of discipline, underwriting authority limits contemplated in pricing are ignored in execution Exercise of underwriting judgment is not consistently applied Underwriter judgment potentially influenced by sales representative or broker Little systematic enforcement of underwriter authority limits No tracking of justification of Strategic Business Adjustments for later evaluation Underwriting relation-to-manual ( RTM ) targets set actuarially assuming certain rate guarantee periods, while longer periods are sold Extending a new case rate guarantee period can reduce lifetime profitability, necessitate higher renewal rate increases Rate manual adjustments for long rate guarantee periods often ignore lifetime profitability impact Anecdotally, many carriers are noting lapses during the rate guarantee period Settling for blockwide RTM Balancing growth and profitability Underwriters can become complacent when a case is managed to block-wide target RTM levels Fundamental to acquisition pricing is that some cases need to be ultimately renewed above block-wide RTM targets Deliberate growth, even when coupled with fidelity to underwriting limits, brings weightedaverage RTM down, creating an appearance of flagging profitability Many carriers lack the tools to understand whether they are truly at targeted RTM Analytics can enable clarity in a world muddled by Acquisition Pricing constructs 7

9 Analytics as a Solution for Group Insurance Underwriting

10 Analysis Maturity Data Information Insight & Foresight A construct for discussing Analytics in Insurance Analytics as a term is used in many contexts. We define Analytics as the set of endeavors that render information in an interactive fashion and drive clarity in data and the analysis derived therefrom. Analytics Creates Value Informs Drives Competitive Advantage Creates Value Availability Management Application Data Maturity Key Considerations Leading insurers have aligned their Analytics strategy to key business objectives to drive profitable growth Establishment of a roadmap for capabilities helps insurers select opportunities that are aligned to the Analytics vision and overall business strategy It is neither practical nor feasible to amass a perfect data set for Analytics deployment Insurers often supplement their own data sets with industry and other third party data The availability and quality of data impacts the quality of insights Competitive advantage is created by applying data and analysis in a matter that competitors cannot match, e.g., by pricing with superior understanding of risk, by combining product offerings across business units, etc. 9

11 Predictive Analytics The pinnacle of Analysis Maturity Predictive Analytics the process of using a variety of statistical techniques from modeling, data mining, and game theory to analyze current and historical facts to make predictions, as well as assess risks and opportunities, about future events. Predictive Analytics are now being used in a wide variety of fields such as healthcare, pharmaceuticals, financial services, insurance, and telecommunications. 1 An Objective Approach to Analyze Risk Predictive Analytics Replaces subjective reasoning from underwriting operations with the use of algorithmic solutions based on mathematical and statistical techniques Leverages internal and external data to predict risk of individual customers Improves insights into risk characteristics that are predictive of future profitability A Tool to Allow Increased Efficiency and to Gain Insight Provides objective guidance for more efficient and consistent underwriting and marketing actions Opens the door for new insights into risk characteristics of cases Creates opportunities to enhance traditional underwriting and pricing A Means to an End Predictive Analytics itself only delivers the relative risk index for an insured The value obtained from Predictive Analytics comes from careful implementation of Predictive Analytics into business processes and systems 1 University of California, Predictive Analytics Center of Excellence, 10

12 Current applications of Analytics in Group Insurance In general, limited capabilities exist around comprehensive data management and advanced Analytics strategies for Group Insurers Tactical Use of Analytics Analytics are most commonly being used in claims and pricing Most Group carriers admit to having limited-to-no formal processes in place to govern and manage data Many methods used to pursue Analytics capabilities Vendors most frequently used in Claims In-house teams Leveraging corporate functionality Increased Use for Reporting Group Insurance executives consistently expressed frustration with their organization s level of data maturity Carriers report a growing interest in the use of Analytics for Analyzing profitability Monitoring customer retention Understanding agent performance Tracking claims operations Source: Deloitte Group Insurance Benchmarking Study

13 Underwriting Modernization through Analytics The Underwriting function and Actuarial, given the close linkage have become a central focus for many Group Insurers to update outdated capabilities Do you have a clearly articulated pricing strategy? 40% 13% 47% Do you have an underwriting dashboard? 27% 13% 60% Dated Underwriting Capabilities Hinder Success Acquisition pricing can cause increased lapses, decreased potential to raise rates at renewal A lack of reporting tools / dashboards can limit the ability to monitor Underwriting decisions Underwriter adjustments to calculated manual rates are commonly subjective in nature, not captured in a systematic fashion, and do not reference all relevant information Do you have an underwriting workbench? 20% 7% 73% Do you use straight-through-processing? 13% 13% 73% Underwriting Modernization is Critical Predictive Analytics could speed the risk assessment processes, yielding quicker delivery of case quotes while adding consistency to decision-making Predictive Analytics could provide Underwriters the ability to synthesize variables into risk measures Group Insurance lags other insurance sectors in the adoption of Underwriting straight-through-processing and other techniques to limit the use of high-cost personnel Source: Deloitte Group Insurance Benchmarking Study

14 Analytics: Driving Data Maturity in Group Insurance

15 Driving Data Maturity in Group Insurance From identification of an opportunity through the underwriting process and to the installation of a case, opportunities abound to capture and manage data that drives value for Group Insurers Sales & Underwriting Process Universe of Cases Marketing Efforts Opportunities Identified DTQ Logic Cases Quoted Negotiation Cases Sold Policy Install / Maintenance DTQ - Data Commonly Missing Detail on cases declined-to-quote (DTQ) Are DTQ statistics (e.g., DTQ rates by broker, sales rep, underwriter) in line with strategy? Do DTQ statistics imply operational deficiencies? Iterative detail on negotiation process DTQ - Potential Analyses Negotiation - Data Commonly Missing To what extent is the initial rate offer accepted? How can negotiations best be managed? Areas for Maturation of Data Underwriter risk assessment Negotiation - Potential Analyses Which underwriters appropriately assess risk, regardless of sold premium? What business value was achieved in making strategic business adjustments? Policy Maintenance - Data Commonly Missing Manual premium - at time of sale, current manual premium Policy Maintenance - Potential Analyses Real-time evaluation of book of business profitability 14

16 Analytics: Driving Analysis Maturity in Group Insurance Book of Business Management

17 Premium Book of Business Management Barring the appropriate Analytics, acquisition pricing could lead insurers to inappropriately gauge the profitability of its current product portfolio Region A Region B Region C Sold Premium and RTM by Duration Total Premium ,180 RTM 75% 85% 95% 105% 115% 125% 98% Premium RTM 75% 85% 95% 105% 115% 125% 88% Premium ,000 RTM 75% 82% 88% 95% 100% 110% 100% Target RTM by Policy Duration % 85% 95% 105% 115% 125% Which Region produced the best UW results? Which Region adhered most closely to target UW guidance? Initial Discount Policy Duration 16

18 Book of Business Management Building the right dashboard Transitioning from ad hoc reporting to functioning dashboards can greatly reduce waste Define key constituents Define metrics to be tracked, key decisions to enable Book of Business Management Dashboard Design What is the audience for the dashboard? Who will maintain the dashboard on an ongoing basis? In collaboration with key constituents, define the below Key metrics: RTM, loss ratio, rate action, persistency, etc. Parameters: Product, issue year, broker, producer, underwriter, authority level, etc. Key decisions to enable: Modifications to sales / renewal strategies, performance management, workforce planning Define metric targets Define dashboard frequency Defining targets enables systematic evaluation of the metrics against those targets and the ability to take action more quickly and with more conviction Optimally dashboards are able to query management information in real-time Throughout the dashboard development process, constantly validate that necessary input data is available 17

19 Analytics: Driving Analysis Maturity in Group Insurance Predictive Analytics

20 Predictive Analytics Key Components Innovative Data Sources Segmentation Business Implementation Traditional internal data sources Non-traditional external individual or household-level data sources Algorithmic Solution Data Aggregation & Data Cleansing Resulting Programs: Underwriting Pricing Application Data Customer Loss History Medical Data Consumer Data Financial Data Disease States Lifestyle Data Household Data Evaluate and Create Data Variables Develop Predictive Analytics Develop Reason Codes & Business Rules Agency Management Target Marketing Claims Sales Effectiveness Market Expansion and Product Design Customer Retention Non-traditional data unlock new insights into customer and employee populations Building and deploying Predictive Analytics requires a specialized combination of skills covering data management, data cleansing, data mart construction, actuarial and statistical analysis, and insurance operations Predictive Analytics are used to align underwriting, pricing, agency management, marketing with overall business strategies 19

21 Loss Ratio Relativity Predictive Analytics for Underwriting: Scoring Predictive Analytics increases the number of variables available to the underwriter to stratify risks, allowing for more precise risk segmentation Scoring Predictive Analytics algorithmic solutions from internal and external sources are identified and aggregated to generate case-specific scoring: Sample Lift Curve Cases are then grouped into deciles based on their model score. The lift curve graph visualizes the degree to which a case represents a favorable risk to the insurer x n+1 (3 Year Loss Ratio) + x n+2 (Broker Distance) + x n+3 (Bill History) + x n+4 (Percentage of Females) + x n+5 (Insured Age) + x n+6 (Industry Group 1) +... x n+k (other variables) Model Score = Y 40% 30% 20% 10% 0% -10% % -30% -40% Model Score Deciles Better Than Average Average Worse Than Average 20

22 Loss Ratio Relativity Group Insurers can derive at least the following benefits through adoption of underwriting Predictive Analytics models Business benefits can be derived by building Predictive Analytics models to segment risk and manage underwriting based on that segmentation: 1. Management of in force block by non-renewing those cases anticipated to be the worst risks. 2. Management of in force block by allocating focused rate actions where indicated. 3. Improvement in the targeting and selection of new business by declining to quote cases anticipated to be poor risks. 4. Easing the acquisition pricing cycle by increasing the allocation of new business discounting to the better risks. 5. Increased Underwriting functional efficiency, including faster close rates, potential for straight through processing, etc. Sample Lift Curve & Associated Business Actions DTQ / Non-renew 40% Modify discounting / rate action 20% 0% -20% % Decile Better Than Average Average Worse Than Average 21

23 Rock and Profiterole Premium Premium Oak Hill Cookie Co. Premium Premium Q Street Bakers Premium Premium Acquisition Pricing through the lens of Predictive Analytics Predictive Analytics allow the insurer to stratify risks in order to make more intelligent acquisition pricing decisions. Consider three cases with identical manual premium rating variables (x 1,. x n ), but different Predictive Analytics model variables (x n+1,. x n+k ) Before Predictive Analytics Manual Premium = f(x 1, x 2, x 3,. x n ) After Predictive Analytics Predictive Analytics Model Premium = f(x 1, x 2, x 3,. x n, x n+1,. x n+k ) Policy Duration Policy Duration Policy Duration Policy Duration Policy Duration Policy Duration Manual Premium Predictive Analytics Model Premium 22 Sold Premium

24 Predictive Analytics - Business Implementation Five distinct design components work in concert to translate Predictive Analytics model results into corporate and underwriting strategy and execution Decile Management Strategy, Pricing Rules, Business Rules, Reason Messages, and Performance Metrics Business Implementation Components 1 Decile management, the segmentation strategy when executed, enables underwriting strategies Decile Management Pricing rules guide application of discretionary pricing and tiering to enhance pricing precision Business rules help attain operational efficiencies Reason messages provide risk insight to aid U/W decision making Performance Management Target State Pricing Rules 5 Performance management metrics measure results across the U/W operation using new metrics and objective criteria based on results of the Scoring Engine Reason Messages Business Rules Technology Enablement and Change Management 23

25 Potential organizational impacts of transitioning to Predictive Analytics Potential Organizational Impacts Changes in Job Roles Job functions may change significantly Roles likely to change in Actuarial and IT as individuals take on new responsibilities related to analysis, maintenance, and IT management New Core Competencies Strong analytical and communication skills required to describe and explain information and related decisions Process Change Changes to processes such as new business and renewal underwriting, customer service, reporting, internal audit, and agency management Could impact workflows across the entire policy issuance process Organizational Design Design includes organizational charts, reporting relationships, and job descriptions Likely occurs in the Underwriting, Broker, IT, or Actuarial areas Performance Metrics and Rewards Performance metrics and rewards may need to be revised to better align with Predictive Analytics activities Based on performance against any Analytics-related performance metrics (e.g., adherence to Predictive Analytics model indications) 24

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