Medicare Supplement Insurance Summit

Transcription

1 Medicare Supplement Insurance Summit How to Make Really Effective Use of Competitive Rate Information Miami, Florida May 18, 2012 Kenneth L. Clark, F.S.A., M.A.A.A. (312) Dawn E. Helwig, F.S.A., M.A.A.A. (312)

2 Introduction Rate sensitivity of Med Supp market Difficult and perhaps not prudent to be perfectly aligned with competition Who is the competition? Actuary consistently finds oneself in a position of defending rates A thorough yet focused dynamic competitive analysis unveils the big picture and potential areas of focus and revision 1 May 18, 2012

3 Altered Perceptions: Sales/Marketers vs. Actuary (Extreme examples Tongue in Cheek ) Sales Their rates are lower so they must be right That s too much money for these seniors to pay! Don t worry about losing money. We will make it up in volume I ll go broke trying to sell these rates Why can t our actuaries price as low as their actuaries? 2 May 18, 2012

4 Altered Perceptions: Sales/Marketers vs. Actuary (Extreme examples Tongue in Cheek ) Actuary Whew! Our rates are higher so we won t have to worry about losing the farm This isn t enough money to pay claims, expenses and those darn commissions! Man we are selling a ton of this stuff! We must be in trouble! Sales people should appreciate the actuarial soundness and conservatism that went into these rates Why don t I ever hear from them when the rates are too low 3 May 18, 2012

5 Two Separate Factors Determine Level of Competitiveness Overall rate level vs. rate relativities Overall rate level reflects the underlying pricing objectives and expectations Expected morbidity level (Claim Costs) Expected persistency Future rate increases Retention required for commissions, expenses and profit yes profit 4 May 18, 2012

6 Two Separate Factors Determine Level of Competitiveness Rate Relativities Reflect the Extent that Risk Variation is Reflected in the Rates Area rating Age slope Gender rating Non-smoker discounts Spouse discounts Preferred rating Being able to identify the primary source(s) of lack of competitiveness can go a long way to explain and identify alternatives 5 May 18, 2012

7 Typical Competitive Analysis Actuary gets a phone call from Sales that word on the street is that rates are too high. The reality could be a combination of actual vs. perceived issues Actual issues Priced at too low of a loss ratio and high margins due to any combination of expenses, commissions, and profit Reflects higher morbidity driven by company experience of inforce block Competition is aggressively priced. Overall rate level 6 May 18, 2012

8 Typical Competitive Analysis Perceived issues One sided emphasis where rates are high Emphasis on outliers Areas where underpriced are ignored 7 May 18, 2012

9 Sample of a Dynamic Competitive Analysis Process Choose a handful of rating cells (M65, F65, ) Assign weights to each rating cell Convert all rates to a common area (i.e. Zip code 606) to determine Base rate table of selected cells for each company rate schedule Determine a composite base rate and set of area factors (relative to the base of each company) For each area assignment compare all selected competitors ranked high to low by the composite rate but show also the comparison by rate cell 8 May 18, 2012

10 Relativity Analysis By normalizing to a common base (Zip code, age, etc.) you can isolate everything else and see where your competitors and industry as a whole is rating At the same time compare that to expected claim cost slopes The market doesn t necessarily follow the claim cost relativities Hope to strike a balance Area Factor Example 9 May 18, 2012

11 Steps to Optimize Rate Classification Access to Information Claims Cost/Experience Data Competitor Rates Market Volume Information 10 May 18, 2012

12 Steps to Optimize Rate Classification Claims Cost/Experience Data Market Volume Information Holes in areas of low volume or non-penetrated markets Credibility considerations Data must be processed to be meaningful PMPM basis Isolated for other factors (i.e. Plan, age/gender mix) Loss Ratio experience detail can be used in lieu of pmpm calculations Challenge is to normalize for differences due to other factors May not be readily available Experience Period 11 May 18, 2012

13 Steps to Optimize Rate Classification Competitor Rates Access to rate information may be sparse Challenge to obtain current rate levels Selective rate information may be biased toward non-competitive classes Commercial database provides comprehensive but costliest solution Need to organize in a meaningful format Avoid paralysis by analysis 12 May 18, 2012

14 Steps to Optimize Rate Classification Market Volume Information Potential market opportunities Medicare eligible populations Medicare Advantage penetration Provide indication of key competitors/players NAIC Annual Statement Data 13 May 18, 2012

15 Steps to Optimize Rate Classification Pull it all together Need flexible reporting process Ability to focus on specific areas Ability to analyze changes Keep an eye on overall rate adequacy Competitive Model Generate report for specified key age/gender cells Baseline rate structure to which every other competitor rate structure is compared By geographic region arranged around baseline area assignments Include market volume of competitors 14 May 18, 2012

16 Problem Presented High Loss Ratios/Low Profits Yet Rates are Not Competitive Isolate One State Example Texas Provided for demonstration purposes only 15 May 18, 2012

17 Step 1: Review Rate Structure Unisex rating Five-year band attained age rating No area rating one rate for entire state No nonsmoker discounts XYZ Insurance Company Medicare Supplement Plan F Rates Attained Age Annual Rate $2,050 2,100 2,425 2,650 2, May 18, 2012

18 Step 2: Review Loss Ratio Experience Detailed breakdown if possible Area Break state into regions based on expected claim cost levels (zip code detail too refined and less credible) Age Gender 17 May 18, 2012

19 Step 2: Review Loss Ratio Experience Total loss ratio experience: Medicare Supplement Experience Overall Current Rates ( Before ) EP IC EXP CC LR $22,123,055 $17,812,475 9,500 $ % 18 May 18, 2012

20 Step 2: Review Loss Ratio Experience Loss Ratios by Area: Medicare Supplement Experience by Area Current Rates ( Before ) Area EP IC EXP CC LR I $8,617,173 $7,320,336 3,700 $ % II 6,287,579 5,078,475 2,700 $ % III 6,752,650 5,117,259 2,900 $ % IV 465, , $ % All $22,123,055 $17,812,475 9,500 $ % 19 May 18, 2012

21 Step 2: Review Loss Ratio Experience Loss Ratios by Age: Medicare Supplement Experience by Age Current Rates ( Before ) Age EP IC EXP CC LR 65 $2,337,244 $1,512,190 1,140 $ % 67 2,337,244 1,554,189 1,140 $ % 72 5,187,541 4,277,161 2,470 $ % 77 4,607,980 3,883,822 1,900 $ % 82 4,028,420 3,441,289 1,520 $ % 85 3,624,628 3,143,824 1,330 $ % All $22,123,055 $17,812,475 9,500 $ % 20 May 18, 2012

22 Step 2: Review Loss Ratio Experience Loss Ratios by Gender: Medicare Supplement Experience by Gender Current Rates ( Before ) Gender EP IC EXP CC LR Male $11,061,528 $9,447,166 4,750 $ % Female 11,061,528 8,365,308 4,750 $ % All $22,123,055 $17,812,475 9,500 $ % 21 May 18, 2012

23 Step 3: Analyze Competition Focus on key competitors six key carriers (A-F) Generally use 3-4 areas All are generally individual age rated One unisex rated Three have tobacco discount Two have preferred rating By age Not as competitive at younger ages By gender Not as competitive for females By Preferred/Smoker Class Not as competitive for the better risks By area More competitive at the higher cost areas 22 May 18, 2012

24 Step 4: Review Business Mix Concentration Area I II III IV XYZ Insurance Company Inforce Distribution by Area Inforce 38.9% Age 65 Population 21.5% All 100.0% 100.0% Concentrated in high cost urban areas male/female split High average issue age 75 XYZ Insurance Company Inforce Distribution by Age/Gender Inforce Age Male Female % % All 50.0% 50.0% > 65 Population Age Male Female % % All 42.6% 57.4% 23 May 18, 2012

25 Step 5: Reach Conclusions Loss Ratio Problem Sales are concentrated in Competitive low premium segments Competitive Rate Problem Not competitive in many key segments 24 May 18, 2012

26 Step 6: Potential Solutions Need to change rate structure to better align with risks and competition Determine the legal/regulatory avenues available and consider the implications of each Prioritize restructuring of rate classes Develop rating factors Apply overall rate level adjustment to maintain rate level adequacy 25 May 18, 2012

27 Step 6: Potential Solutions Prioritization of restructuring 1. Expand area rating 2. Move to individual age rates 3. Apply nontobacco discount 4. Gender specific rates 5. Preferred rates 26 May 18, 2012

28 Step 6: Potential Solutions Choice of restructuring Items 1-4 Recommended rating factors can be developed in timely manner based on available information 27 May 18, 2012

29 Step 7: Rate Factor Development Claim Cost analysis Competition Actuarial Judgment 28 May 18, 2012

30 Step 7: Rate Factor Development Age rating Need a steeper rate slope (reductions at younger ages and increases at old ages) From 20% reduction at age 65 grading to 7.5% increase at age 85+ Area Rating Area I 1.05, Area II 1.00, Area III 0.95, Area IV 0.80 Gender Rating Male increase 6%, Female decrease 6% Tobacco Load Apply 15% load 29 May 18, 2012

31 Step 8: Overall Rate Level Adjustment Reflect overall rate level desired Implicitly accounts for overall rate increase Balancing Item Apply overall increase of 7.5% Meets loss ratio target of approximately 75% 30 May 18, 2012

32 Resulting Rate Schedule Attained Age XYZ Insurance Company Medicare Supplement Plan F Non-Tobacco Rates Male $1,869 1,898 1,927 2,082 2,238 2,393 2,423 2,453 2,543 2,632 2,722 2,811 2,901 2,955 3,009 3,063 3,117 3,171 3,227 3,282 3,338 Annual Rate Female 1,657 1,683 1,709 1,847 1,984 2,122 2,149 2,175 2,255 2,334 2,414 2,493 2,573 2,621 2,669 2,716 2,764 2,812 2,861 2,911 2,960 Area I II III IV Tobacco Load: 1.15 Factor May 18, 2012

33 Expected Impact on Experience Total Loss Ratio: Medicare Supplement Experience Overall Revised Rates ( After ) EP IC LR $23,733,470 $17,812, % 32 May 18, 2012

34 Expected Impact on Experience Loss Ratio by Area: Medicare Supplement Experience by Area Revised Rates ( After ) Area EP IC LR I $9,705,740 $7,320, % II 6,745,302 5,078, % III 6,882,706 5,117, % IV 399, , % All $23,733,470 $17,812, % 33 May 18, 2012

35 Expected Impact on Experience Loss Ratio by Age: Medicare Supplement Experience by Age Revised Rates ( Before ) Age EP IC LR 65 $2,009,820 $1,512, % 67 2,072,520 1,554, % 72 5,715,580 4,277, % 77 5,200,300 3,883, % 82 4,547,080 3,441, % 85 4,188,170 3,143, % All $23,733,470 $17,812, % 34 May 18, 2012

36 Expected Impact on Experience Loss Ratio by Gender: Medicare Supplement Experience by Gender Revised Rates ( After ) Gender EP IC LR Male $12,578,855 $9,447, % Female 11,154,615 8,365, % All $23,733,470 $17,812, % 35 May 18, 2012

37 Expected Impact on Competitive Level Much more in line with competition Actually less competitive for certain high loss ratio segments Profitability not as dependent on shifts in distribution 36 May 18, 2012

38 Issues that need to be addressed Ideal to address rate classification from the beginning Disruption of Inforce Business if applicable Unless filed as a change in rating methodology or sold under a different company, rate changes must apply to inforce, not just new issues Some insureds will have significant rate changes both up and down If current insureds remain on the old structure, your new structure will be competing with your old structure Regulatory (Filing) Issues May require a change in rating methodology filing depending on the extent of the changes Even if not, may still come up against some regulatory resistance Steeper rate slope Be prepared to provide background documentation to support rate table revision 37 May 18, 2012

39 Conclusion Optimal rate classification involves recognition of risk variation Promotes equity of premium rates Align with competition as much as possible within the constraints of profit objectives Minimize anti-selection and skewed distributions Uniform profitability protects against shifts in distribution from expectations 38 May 18, 2012