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Long Term Disability Benchmarking Report

Claim Analytics recently published the 2010 Long Term Disability Benchmarking Report

Executive Summary
Section 1: Report Overview and Methodology
Section 2: Study Results
Section 3: Extended Duration Claims
Section 4: Historical Trends
Section 5: Social Security Disability Benefits Approval Rates
Section 6: Claims Practices Survey Results
APPENDICES

Claim Analytics 2010 LTD Disabilty Benchmarking Report

Executive Summary

INTRODUCTION

The Claim Analytics 2010 Group LTD Benchmarking Study compares LTD recovery experience across several
insurance companies. The key objective is to aid companies in identifying areas where there is potential
to improve their claim practices and claims experience.

Performance is measured as the difference between a company’s expected 24-month recovery rate and the average of the expected 24-month recovery rates for all companies, as measured on a standardized portfolio of new Group LTD claims. Expected recovery rates were determined using predictive models that were calibrated using each company’s actual LTD claim experience.

RESULTS

Performance by Company
The above chart summarizes the overall performance of each participating company.

PARTICIPATING COMPANIES

There are 12 participating companies, up from 8 last year (in alphabetical order):

Aetna Life
Guardian Life
ING Employee Benefits
Met Life
Principal Financial
Sun Life Assurance Co

Assurant Employee Benefits
Hartford Life
Lincoln Financial
Mutual of Omaha
Prudential
Unum

Please note that to preserve anonymity each company have been randomly assigned a letter between A and L.

KEY FINDINGS

Performance and Diagnostic Category: The difference in expected recovery rates (performance) between the best and worst performers is 12% (up from 11% last year). The wide differences in performance by company exist for some, but not all, diagnostic categories. These are the diagnostic categories where claims management practices can have a significant impact on outcomes. Performance does not vary greatly by company for other diagnostic categories. These are the diagnostic categories where claims management practices have less impact on outcomes.As an example, consider the two most prevalent diagnostic categories: musculoskeletal and cancers.
- The first chart below shows that the range of performance by company for musculoskeletal claims is much wider than the range for overall results. This indicates that musculoskeletal claims are a diagnostic category where claims management practices can influence outcomes.
- The second chart below shows the range of performance by company for cancer claims is much narrower than the range for the overall results. This indicates that there is less opportunity for claims management practices to affect outcomes for cancer claims.
Extended Duration Claims: There are significant differences in the actual historic recovery rates during benefit months 24-48 between the 12 participating companies. The range is between 6-17% with an average of 10%. This suggests that some companies may be missing significant opportunities in their extended duration claims, either through lack of expertise or lack of resources.

Historic Recovery Rate by Comapany

Increasing Average Claimant Age: The average age of new claimants by benefit year has increased steadily from 2002-2008 by nearly two years from 47.0 to 48.9. In spite of this increase in the average claimant age, the distribution of claims by diagnostic category has been very stable. I.e., the cause of disability tends to vary by age; however, although the average claimant age has increased by two years between 2002 and 2008 the frequencies of various diagnoses has remained quite static.

Historic Claimant Age by Benefit Year
Historic Claims by Diagnosis

Variance in Claim Approval Rates: There is a much greater than expected variance in claim approval rates across companies. The rates range from a low of 60.0% to a high of 89.6%. The average claim approval rate is 78.3%. There is an expectation within the industry that performance should be greatest for companies with the highest approval rates, and vice-versa. Based on our survey of company approval rates, we do not see evidence of this pattern. The chart below shows the average performance of companies within each claim approval rate band.
Claim Scoring Usage: Performance is best for the companies that used claim scoring as a claims management tool for at least part of the study period. Claim scoring is a service provided by Claim Analytics which uses predictive modeling to score each claim on its likelihood of return to work. Claim scoring is applied as a decision support and resource optimization tool for managing disability claims.

Year of Claim Scoring Implementation
Performance by year of claim scoring Implementation

COMPANY SPECIFIC ANALYSIS

In addition to this, the main report, each participating company will receive a separate, individualized analysis which will reveal to them their company code and provide an analysis of their own performance.

BENCHMARKING USER GROUP

Claim Analytics will host a user group meeting for benchmarking participants in the fall of 2010. The user group meeting will provide companies an opportunity to share their claims management experiences and work toward developing industry-wide best practices.

REPORT FORMAT

Section 1 – Report Overview and Methodology: This section provides an overview of the study and discusses the study methodology.

Section 2 – Study Results: This section compares expected recovery rates across companies for the standardized portfolio of claims. The results show each company’s overall recovery performance as well as their performance by the various claim attributes that have been analyzed, such as diagnostic category and region.

Section 3 – Extended Duration Claims: This section shows the actual historical recovery rates between benefit months 24-48 for each company on their own block of claims. Readers are cautioned that a comparison of actual historical recovery rates between companies may not provide insight into the claims management performance of the companies because there are underlying differences in the profile of claims between companies.

Section 4 – Historical Trends: This section provides a comparison of the profile of new claimants by benefit year from 2002-2008 and highlights trends such as the average claimant age and the distribution of claims by diagnostic category by benefit year.

Section 5 – SSDI Approval Rates by Benefit Year: This section provides a comparison between companies of the proportion of claims that have been approved for SSDI benefits by benefit year. The results will help each company better understand how the proportion and timing of their claimants being approved for SSDI benefits compares to the other participating companies.

Section 6 – Claims Practices Survey Results: As part of this year’s benchmarking study each participant completed a survey about their claims management practices. The results of the survey allow companies to compare their claims management practices to those of the other participants and also to see how different practices correlate to performance levels.

SECTION 1 – Report Overview and Methodology

OVERVIEW

OBJECTIVE

Insurance companies can differ widely in their LTD claims management practices. However, until now Group LTD insurers have not had a reliable way to measure the effectiveness of their claims management practices relative to their peers.

The Claim Analytics benchmarking study compares LTD recovery experience across several participating insurance companies.

This is the second year of the study. Several enhancements have been added since last year:

Analysis of recovery rates for extended duration claims (Section 3)
Analysis of historical trends (Section 4)
Analysis of SSDI approval rates (Section 5)
Survey of participating companies on specific claims practices (Section 6)

PARTICIPATING COMPANIES

The participating insurance companies (in alphabetical order) were as follows:

Aetna Life
Assurant Employee Benefits
Guardian Life
Hartford Life
ING Employee Benefits
Lincoln Financial
Met Life
Mutual of Omaha
Principal Financial
Prudential
Sun Life Assurance Co.
Unum

Please note that in order to maintain anonymity, throughout this report each participating company has been assigned a letter between A and L based on the order of performance ranking.

Five of this year’s participants were not a part of last year’s benchmarking study: Aetna Life, Assurant Employee Benefits, Guardian Life, ING Employee Benefits and Prudential. Sun Life Health Insurance Co (former Genworth business) was treated as a unique entity for last year’s study but has been combined with Sun Life Assurance Co for this year’s study.

BENEFITS OF PREDICTIVE MODELING

A key feature of this study is that predictive modeling techniques are employed to normalize for differences in the business mix between companies. This allows for an “apples to apples” comparison of LTD recovery rates between companies.

An unadjusted comparison of LTD recovery rates between insurance companies may provide misleading results as it does not account for the underlying differences that exist in the business written by the companies. For example, it is well documented that LTD recovery rates decrease as elimination period increases. Thus, insurer X that writes a large proportion of 180-day business will, all else being equal, have lower recovery rates than insurer Y that writes a lot of 90-day business. However, it would be wrong to conclude based on this that insurer X is not as effective at claims management as insurer Y.

Predictive modeling allows us to uncover the patterns in an insurance company’s historical LTD claim data and quantify the relationships between claimant profile (age, elimination period, diagnosis, etc) and likelihood of recovery. The result is the ability to compare the expected recovery results for a standardized portfolio of claims across several insurance companies – both as a way to measure aggregate performance of each company and also as a way to identify for each company their areas of relative strength and weakness.

DEFINITIONS

Recovery: The termination of an approved claim for a reason consistent with a claimant returning to work, being able to return to work or failing to provide evidence that (s)he is unable to return to work.Examples of termination reasons that are considered to be recovery include: return to work, advance pay and close, not totally disabled for any occupation and failure to provide medical evidence. Examples of termination reasons that are not considered to be recovery include: death and attainment of maximum benefit.Note that within the benchmarking study we focus on 24-month recovery, meaning claimants that recover within 24 months of the benefit date (the date the elimination period is satisfied).
Expected Recovery Rate: The likelihood of 24-month recovery for a claim as estimated by a given company’s predictive model.Note that for any given claim, the expected recovery rate will be different for each company because the predictive models are specific to each company and their own, unique, claim experience.
Performance: The difference between the expected recovery rate for a company and the average of the expected recovery rates for all companies.
Standardized Portfolio of Claims: A portfolio of 200,000 claims constructed by randomly sampling, with replacement, all claims from the participating companies.

REPORT FORMAT

The report includes the following sections:

Section 1 – Report Overview and Methodology: This section provides an overview of the study and discusses the study methodology.

Appendix A – provides details on how States were classified into Regions.

Appendix B – provides details on how ICD9 codes were classified into Diagnostic Categories.

Appendix C – provides details on how SIC codes were classified into Industries.

DATA CONSIDERATIONS

MODEL DATA

The study was based on claims data provided by the twelve participating companies and included all approved claims, both open and closed with dates of benefit commencement on or after January 1, 2003.

DATA CONSIDERATIONS

Not all claims were used in the study:

Claims that were denied, claims that did not satisfy the elimination period and claims where the outcome was not clearly coded were not used.
Only claims with a benefit commencement date on or prior to December 31, 2006 were used as we focused on recoveries within the first 24 months of benefit – for claims with later benefit commencement dates, the 24-month status would not have been determined by December 31, 2008, which was effectively the study end-date.

In total, we analyzed 404,497 claims, of which 209,408 (51.77%) recovered within 24 months of benefit commencement.

DATA FIELDS ANALYZED

With minor exceptions, we limited the analysis to data fields that were provided by all companies as follows:

• Age at Disability	• Elimination period
• Gender	• Primary diagnosis (ICD9)
• Monthly benefit	• Benefit percentage
• Region	• Industry (SIC)

The appendices provide details on the definitions of regions, diagnostic categories and industries.

STUDY METHODOLOGY

The steps involved in developing the study results are discussed below.

MODEL EACH COMPANY’S CLAIM HISTORY

A predictive model was built specific to each company based on their own historical claim data, to predict the likelihood of new claims recovering within 24 months of benefit commencement.

CREATING A PORTFOLIO OF CLAIMS

A portfolio of 200,000 claims was created to be used as our standard for measurement. This portfolio was constructed by random sampling, with replacement, from all 404,497 claims reported by the twelve participating companies.

PREDICT OUTCOMES FOR THE PORTFOLIO OF CLAIMS

The portfolio of claims was run through each company’s unique model. This provided, for each company, a prediction for each claim of its likelihood of recovering based on that company’s specific claim management practices.

AGGREGATION OF RESULTS

For each company the predicted claim recovery results were aggregated over all of the claims to arrive at its overall expected recovery rate.

Results were then analyzed by various claim attributes including age, elimination period, gender, region, diagnostic category, industry and monthly benefit amount.

SECTION 2 – STUDY RESULTS

OVERALL RESULTS

Expected Recovery Rate by Company

Expected recovery rates were calculated for each company on the standardized portfolio of claims.

For each company, performance was then measured as the difference between its expected recovery rate and the average expected recovery rates across all companies.

The participants were assigned company codes based on their performance relative to benchmark. The participant with the highest performance has been coded as Company A, the participant with the second highest performance has been coded as Company B, and so on, through to the participant with the lowest performance which has been coded as Company L.

Assigning codes in this manner will be helpful to readers of this report. When looking at the performance charts for any segment of claims the reader will be able to remember that the companies with highest aggregate performance are at the left-hand side of the chart and those with lowest aggregate performance are at the right-hand side of the chart.

The remainder of this section will discuss the key findings and then analyze expected recovery rates across several key claimant attributes, such as age, elimination period, gender and diagnostic category.

KEY FINDINGS

There are three key findings: wide range of performance between companies, performance by diagnostic category and performance by monthly benefit.

Wide Range of Performance: The difference in expected 24-month recovery rates (performance) between the best and worst performing companies is 12%. Again, the expected recovery rates are for a standardized portfolio of claims so that performance for each company is being measured on a like basis.The chart below, which is the same as the chart from the previous page, shows that Company A, the best performing company, would be expected to achieve a recovery rate of 8% greater than average for the standardized portfolio of claims. Company L, the worst performer, would be expected to achieve a recovery rate of 4% less than average.

Expected Recovery Rate by Company

Diagnostic Category: There are wide differences in performance by company for some, but not all, diagnostic categories. These are the diagnostic categories where claims management practices can have a significant impact on outcomes. Performance does not vary greatly by company for other diagnostic categories. These are the diagnostic categories where claims management practices have less impact on outcomes. The benchmarking report will help each company to identify the diagnostic categories for which their experience is relatively strong or weak.Diagnostic category is explored in greater detail later in this section of the report. For now, as an example, consider the two most prevalent diagnostic categories: musculoskeletal and cancers:
- Musculoskeletal claims account for 28% of all claims analyzed. The first chart below shows that the range of performance by company for musculoskeletal claims is much wider than the range for overall results. This indicates that musculoskeletal claims are a diagnostic category where claims management practices can influence results.
- Cancer claims make up 15% of all claims analyzed. The second chart below shows the range of expected outcomes by company for cancer claims is much narrower than the range for the overall results. This indicates that there is less opportunity for claims management practices to affect outcomes for cancer claims.
Monthly Benefit: Most of the differences in performance by company are attributable to claimants with monthly benefits of less than $2,500. This category includes 76% of all claims.The first chart below shows that performance for claimants with monthly benefits of less than $2,500 is very similar to overall performance.The second chart below shows that performance for claimants with monthly benefits from $2,500 – $3,999 has a similar pattern to overall performance, but to a lesser magnitude.The third chart below shows that nearly all companies perform equally well for claimants with monthly benefits of $4,000 or greater.

Expected Recovery Rate by diagnosis: $0 - 2499

Expected Recovery Rate by diagnosis: $2500-3999

AGE

This chart shows the average expected recovery rate by age band, across all companies.

Expected Recovery Rate by Age

As expected, recovery rates decline steeply as claimant age increases.

The charts on the next two pages show the expected recovery rates by age band for each company. The performance of the companies by age band is very consistent with the overall performance results.

ELIMINATION PERIOD

This chart shows the average expected recovery rate by elimination period.

As expected, recovery rates decline as elimination period increases.

The charts on the next two pages show the expected recovery rates by elimination period for each company. The performance of the companies for each elimination period band is consistent with the overall performance results; however, the range is performance increases as elimination period gets longer.

Note that there is some variation for the 0-89 day elimination period band because of the small number of claims in that group.

GENDER

This chart shows the average expected recovery rate by gender.

Female claimants exhibit much better 24-month recovery rates than male claimants. The higher recovery rates for females are explained by a few factors:

Very high recovery rates for pregnancy claims
Female claimants are, on average, 3.1 years younger than male claimants
The average elimination period of female claimants is 10.0 days shorter than for male claimants
The average monthly benefit of female claimants is $469 less than for male claimants

The charts on the next page show the expected recovery rates by gender for each company. The performance of the companies by gender is very consistent with the overall performance results.

REGION

This chart shows the average expected recovery rate by region.

Recovery rates are lower in the South and Southwest than in the rest of the country.

The charts on the next four pages show the expected recovery rates by region for each company.

The performance of the companies by region is reasonably consistent with the overall performance results.

DIAGNOSTIC CATEGORY

This chart shows the average expected recovery rate by diagnostic category.

Recovery rates are highest for claimants on disability due to pregnancy-related illnesses, followed by injuries, digestive illnesses and musculoskeletal ailments. Recovery rates are lowest for claimants suffering from respiratory illnesses, ailments of the nervous system, endocrine disorders, circulatory diseases and cancers.

The charts on the next thirteen pages show the expected recovery rates by diagnostic category for each company.

There is very little variation between companies in expected recovery rates for cancer claims, implying that companies have similar experience with these types of claims.

The performance of companies for managing circulatory claims is similar to their overall performance results.

The performance of companies in managing digestive system claims is somewhat similar to their overall performance results.

The companies with highest aggregate performance – Companies A, B and C – also perform well for digestive system claims. And the companies with the lowest aggregate performance – Companies I, J, K and L – also underperform for digestive system claims.

However, for the companies with average aggregate performance – Companies D, E, F, G and H – there is a moderate amount of variation in results for digestive system claims.

This may be a diagnostic category where case management practices and outcomes vary significantly amongst companies.

Performance by company for claimants with diseases of the endocrine system is similar to performance for diseases of the digestive system.

Companies with strong overall performance also perform well for this diagnostic category while companies with weak overall performance also underperform for this diagnostic category. For companies with average overall performance there is variation in the results.

The performance of companies in managing genitourinary illness claims is different than their overall performance results. In particular, Companies E, F and I fare much better and Company A fares much worse for genitourinary claims than in aggregate.

This may be a diagnostic category where case management practices differ significantly amongst companies.

The performance of companies in managing infectious and parasitic disease claims differs from overall performance results.

The companies with the strongest aggregate results, Companies A, B and C, all perform very well for this diagnostic category. For the other nine companies, there is not a strong correlation between aggregate performance and performance for infectious and parasitic disease claims. In particular, Companies D and J perform much better for this diagnostic category than in aggregate and Companies H and L perform much worse for this diagnostic category than in aggregate.

In general, the companies’ performance in managing injury and poisoning claims has a similar pattern to their overall level of performance. However, the magnitude of results is greater than overall – meaning the degree of over / underperformance for this diagnostic category is more pronounced that aggregate over / underperformance.

Injury and poisoning may be a diagnostic category where claims management practices have a significant impact on claim outcomes.

The performance of companies in managing mental disorder claims is significantly different from their overall performance results. In particular, Companies E and H perform significantly better and Companies B, D, G and L perform significantly worse for these claims.

For most companies, performance in managing musculoskeletal claims is similar to their overall level of performance. For Companies A, B, J and K, performance for musculoskeletal claims is similar to overall performance but much more pronounced. Outcomes for musculoskeletal claims may be sensitive to claims management practices.

In general, the companies’ performance in managing nervous system claims is similar to their overall level of performance, but to a lesser magnitude.

Recovery rates for pregnancy claims are consistently high across companies. As the average expected recovery rate is 96% for these claims, there is no room for any company to achieve a significant excess recovery rate.

Performance by company for respiratory claims is comparable to aggregate performance for most companies.

In general, the companies’ success in managing “other” claims is similar to their overall level of performance but magnified.

For Companies A, B and C, the three companies with aggregate performance better than average, excess expected recoveries are even higher for this diagnostic group.

Similarly, for Companies I, J, K and L, the four companies with aggregate performance worse than average, excess expected recoveries tend to be lower for this diagnostic group.

INDUSTRY

This chart shows the average expected recovery rate by Industry.

Expected recovery rates are consistent across most industries, with two exceptions.

Expected recovery rates are much higher than average for the Education industry. The high expected recovery rates result from a high proportion of pregnancy claims (19% for Education vs 7% in total) and a short average elimination period (95 days for Education vs 135 days in total).
Expected recovery rates are much lower than average for the Manufacturing industry. The low expected recovery rates result from a low proportion of pregnancy claims (4% for Manufacturing vs 7% in total) and an older average claimant (48.5 years for Manufacturing vs 47.4 years in total).

The charts on the next eight pages show the expected recovery rates by Industry category for each company.

Across industries, the performance for the companies does not vary greatly from their overall performance.

In general, the companies’ performance in managing Education claims is similar to their overall level of performance.

Company D performs better for Education claims than overall while Company G performs worse.

In general, the companies’ performance in managing Finance claims is similar to their overall level of performance.

Company C performs better for Finance claims than overall while Company L performs worse.

For the Health industry, performance for most companies is similar to overall levels.

Company L has better results for Health claims than its overall performance.

Performance by company for the Manufacturing industry is consistent with overall performance levels.

Company C has worse results for Manufacturing than its overall performance

For most companies, performance for Public Administration claims is similar to overall performance.

For Company H, results for Public Administration claims are better than overall performance. For Company D and Company F, results for Public Administration claims are worse than overall performance.

Performance by company for the Services industry is consistent with overall performance results.

Performance by company for the Trade industry exhibits a similar pattern to overall performance results, but the magnitudes of over / underperformance are greater.

Performance by company for “Other” industries is consistent with the overall performance results.

MONTHLY BENEFIT AMOUNT

This chart shows the average expected recovery rate by monthly benefit amount

Recovery rates decrease as monthly benefit amounts increase. Some of decrease in recovery rates is a result of higher earners typically being older and having more severe ailments but part of the decrease is truly a result of the high monthly earnings and benefit amounts.

For claimants with monthly benefits less than $1,000, performance by company tends to be in the same direction as overall performance, but to a greater degree.

For claimants with monthly benefits between $1,000 and $2,499, performance by company is very similar to overall performance.

For claimants with monthly benefits between $2,500 and $3,999, performance by company tends to be in the same direction as overall performance, but to a lesser degree.

For claimants with monthly benefits of $4,000 or more, there is very little variation in performance between companies.

The charts on the next three pages show the expected recovery rates by monthly benefit amount for each company.

For monthly benefit of both $0-$999 and $1,000-$2,499, the performance by company is similar to the overall performance results. The degree of over / underperformance is more pronounced at the $0-$999 benefit level than for the $1,000-$2,499 benefit level.

For claimants with monthly benefits between $2,500 and $3,999, performance by company tends to be in the same direction as overall performance, but to a lesser degree.

For monthly benefits of $4,000 or greater, there is little variation in performance between companies.

Company H and Company B perform better than average for claimants with monthly benefits of $4,000 or higher. Company E has the lowest expected recovery rates for claimants with monthly benefits of $4,000 or higher.

BENEFIT RATIO

This chart shows the average expected recovery rate by benefit ratio.

Expected recovery rates are, by far, lowest for claimants with benefit ratios of 70% or greater.

For benefit ratios up to 69.9%, performance is generally consistent with overall performance.

For benefit ratios of 70% or more, performance is not correlated to overall performance.

The charts on the next two pages show the expected recovery rates by monthly benefit amount level for each company and relative to the average for all companies combined.

Companies C, E and H perform much worse than their overall levels for claimants with benefit of 70% or more. Companies F, H, I, J, K and L all perform much better.

TIME TO RECOVERY

Time until recovery is an important consideration when measuring claims management performance. For this section of the report, the analysis is not based on predictive modeling and a standardized portfolio of claims. Rather, the results presented in this section of the report are based on each company’s actual historical results. Therefore, the findings in the section are not a perfect comparison; nonetheless, the findings will aid each company in better understanding their claims management results.

The chart above shows for each company the distribution of claims that did recover within 24 months by time to recovery (from the benefit date).

The companies with the best overall performance, Companies A, B and C, have a lower than average proportion of recoveries in the first three months. Their strong performance appears to result from success in resolving claims at later durations.

For the companies with the lowest overall performance there is no consistent finding:

For Company I, the distribution of recoveries by time is very similar to average. This may indicate that under performance results from a few opportunities missed at each duration.

Companies J and K achieve a lower than average proportion of recoveries within the first 6 months. The extension in duration of expected short duration claims may exacerbate under performance by tying up resources that would otherwise be available to work with more complex claims.

Company L is showing a very high proportion of recoveries occurring within the first 6 months. This company appears to have success in resolving claims that are able to recover quickly. The opportunities for improvement are in achieving more recoveries between 6-24 months (i.e., claims that are likely more complex but still have some likelihood of recovery).

The chart below shows each company’s actual historic recovery rate broken down by time horizon. Actual recovery rates may be quite different than the predicted recovery rates used for benchmarking because of differences in mix of business for some companies.

SECTION 3 – EXTENDED DURATION CLAIMS
HISTORICAL EXPERIENCE FOR RECOVERY BETWEEN 24 – 48 MONTHS

AGGREGATE HISTORICAL EXPERIENCE FOR 24- 48 MONTH RECOVERY

This section of the report focuses on claims that are still open after 24 months of benefits and measures their historical recovery rates during the next 24 months of benefits. There are very few claimants that do recover between 24-48 months and so we could not build credible predictive models, especially for some of the smaller companies. Therefore, we chose to show historical recovery rates rather than predicted recovery rates for this time horizon.

The historical recovery rates discussed in this section are the actual recovery rates experienced by each company for their own block of claims. This is very different than the expected recovery rates analyzed in the previous section of this report which are the recovery rates a company would be expected to achieve for the standardized portfolio of claims. Since the profile of historical claims differs by company, the historical recovery rates do not necessarily provide a consistent measure of claims management performance.

The chart below shows actual 24-48 month recovery rates for each company, compared to the average across companies. There is a wide range of recovery rates across companies – experience varies from 6% to 17%.

The remainder of this section shows historical recovery rates across several key claimant attributes.

AGE

Recovery rates for extended duration claims decrease as age increases.

The charts on the next two pages show the recovery rates by company for each age band. The variance in 24-48 month recovery rates is much greater for claimants aged less than 50 than for claimants aged 50 or more.