Tag Archives: actuarial science

data symmetry

Competing in an Age of Data Symmetry

For centuries, people have lived in a world where data was largely proprietary, creating asymmetry. Some had it. Others did not. Information was a currency. Some organizations held it, and profited from it. We are now entering an era of tremendous data balance — a period of data symmetry that will rewrite how companies differentiate themselves.

The factors that move the world toward data symmetry are time, markets, investment and disruption.

Consider maps and the data they contained. Not long ago, paper maps, travel books and documentaries offered the very best views of geographic locations. Today, Google allows us to cruise nearly any street in America and get a 360° view of homes, businesses and scenery. Electronic devices guide us along the roadways and calculate our ETA. A long-established map company such as Rand McNally now has to compete with GPS up-and-comers, selling “simple apps” with the same information. They all have access to the same data. When it comes to the symmetry of geographic data, the Earth is once again flat.

Data symmetry is rewriting business rules across industries and markets every day. Insurance is just one industry where it is on the rise. For insurers to overcome the new equality of data access, they will need to understand both how data is becoming symmetrical and how they can re-envision their uniqueness in the market.

It will be helpful to first understand how data is moving from asymmetrical to symmetrical.

Let’s use claims as an example. Until now, the insurer’s best claims data was found in its own stockpile of claims history and demographics. An insurer that was adept at managing this data and applied actuarial science would find itself in a better position to assess risk. Competitively, it could rise to the top of the pack by pricing appropriately and acquiring appropriately.

Today, all of that information is still very relevant. However, in the absence of that information, an insurer could also rely upon a flood of data streams coming from other sources. Risk assessment is no longer confined to historical data, nor is it confined to answers to questions and personal reports. Risk data can be found in areas as simple as cell phone location data — an example of digital exhaust.

Digital exhaust as a source of symmetry

Digital exhaust is the data trail that all of us leave on the digital landscape. Recently, the New York City Housing Authority wished to determine if the “named” renter was the one actually living in a rent-controlled apartment. A search of cell phone tower location records, cross-referenced to a renter’s information, was able to establish the validity of renter occupation. That is just one example of digital exhaust data being used as a verification tool.

Another example can be found in Google’s Waze app. Because I use Waze, Google now holds my complete driving history — a telematics treasure trove of locations, habits, schedules and preferences. The permissions language allows Waze to access my calendars and contacts. With all of this, in conjunction with other Google data sets, Google can create a fairly complete picture of me. This, too, is digital exhaust. As auto insurers are proving each day, cell phone data may be more informative to proper pricing than previous claims history. How long is it until auto insurers begin to look at location risk, such as too much time spent in a bar or frequent driving through high-crime ZIP codes? If ZIP codes matter for where a car is parked each night, why wouldn’t they matter for where it spends the day?

Data aggregators as a source of symmetry

In addition to digital exhaust, data aggregators and scoring are also flattening the market and bringing data symmetry to markets. Mortgage lenders are a good example from outside the industry. Most mortgage lenders pay far more attention to comprehensive credit scores than an individual’s performance within their own lending operation. The outside data matters more than the inside data, because the outside data gives a more complete picture of the risk, compiled from a greater number of sources.

Within insurance, we can find a dozen or more ways that data acquisition, consolidation and scoring is bringing data symmetry to the industry. Quest Diagnostics supplies scored medical histories and pharmaceutical data to life insurers — any of whom wish to pay for it. RMS, AIR Worldwide, EQECAT and others turn meteorological and geographical data into shared risk models for P&C insurers.

That kind of data transformation can happen in nearly any stream of data. Motor vehicle records are scored by several agencies. Health data streams could also be scored for life and health insurers. Combined scores could be automatically evaluated and placed into overall scores. Insurers could simply dial up or dial down their acceptance based on their risk tolerance and pricing. Data doesn’t seem to stay hidden. It has value. It wants to be collected, sold and used.

Consider all the data sources I will soon be able to tap into without asking any questions. (This assumes I have permissions, and barring changes in regulation.)

  • Real-time driving behavior.
  • Travel information.
  • Retail purchases and preferences.
  • Mobile statistics.
  • Exercise or motion metrics.
  • Household or company (internal) data coming from connected devices.
  • Household or company (external) data coming from geographic databases.

These data doors, once opened, will be opened for all. They are opening on personal lines first, but they will open on commercial lines, as well.

Now that we have established that data symmetry is real, and we see how it will place pressure upon insurers, it makes sense to look at how insurers will use data and other devices to differentiate themselves. In Part 2 of this blog, we’ll look at how this shift in data symmetry is forcing insurers to ask new questions. Are there ways they can expand their use of current data? Are there additional data streams that may be untapped? What does the organization have or do that is unique? The goal is for insurers to innovate around areas of differentiation. This will help them rise above the symmetry, embracing data’s availability to re-envision their uniqueness.

Healthcare Exchanges: Round 2

Most of the dust has now settled around the State Exchanges. Last week the New York rates were finalized and with most of the other states, rates came in lower than anticipated. The Department of Health and Human Services (HHS) released an analysis1 suggesting that rates were 18% lower than anticipated. The national press has been in a frenzy as the public is trying to determine what all of this means. This article will discuss several of the issues and try to put them in perspective.

So What Are The Facts?
Are the rates actually lower? The HHS article demonstrates that yes, rates are coming in lower than previously projected rates even by the Congressional Budget Office (CBO). However, the situation is not quite the same as alluded to. For example, the study compared the “lowest rate” with the projected or forecasted rate. In the recently released rates for the State of New York, rates for the silver plan in New York City ranged from a low of about $350 to a high of nearly $700, a wide range. The HHS article compared the lowest rate in a plan type and compared that to the CBO projection. If the low in New York was $350 and the high $700, one might assume an average rate of $525 (i.e., (350 + 750)/2 = $525). Comparing $350 to the projection gets a different answer than comparing $525 to the projection. For example, if $350 is 18% lower than the projection, $525 is 123% of the projection, a much different story than presented.

Why Do The Rates Vary So Much?
Rates are based upon a large number of specific and sometimes hard to define actuarial assumptions. Some of the health plans used different assumptions than others resulting in different rate levels. Some of the key assumptions are:

  • Health care provider discounts and also average cost of those providers in the network
  • Care management approach and effectiveness
  • Required expense and margin loads
  • Assumed health status of population to be covered
  • Assumed health care inflation and/or trend assumption
  • Specific plan design
  • Prior experience with individuals and small groups

There are other assumptions that are included, but the above list describes most of the important ones. If a particular health plan has better than average discounts with providers it is likely that their premium would be lower than a plan with lesser discounts. If the providers included in the network have a lower average cost than a competitor's network, the premium would likely be lower than the competitor. If a health plan had more effective care management practices than their competitor their rates might be lower. The list goes on. In addition to actual measured performance, some of these differences might be based upon perceived value and/or differences.

Although actuarial science is an objective science, different actuaries might have different opinions on the same issue and could apply different judgment when the data is incomplete or questionable. As a result, rate differences might occur as a result of different actuarial opinion.

The nature of a specific health plan can also lead to differences. One example of this is the rate development in one of the states our company was working on. One of the major players in the market attempted to negotiate more favorable contracts with its provider network. The best attempt at negotiating with a highly desirable health system resulted in a contract that paid that provider about 115% of Medicare payment rates, an improvement from their current contract. A competitive health plan in that same marketplace contracting with that same provider was able to negotiate a contract at close to Medicaid rates, considerably less than what the other carrier had achieved. A very surprising result that we had to investigate further to understand.

The first carrier was a major commercial health plan. The second was a health plan that served Medicaid beneficiaries. Their current contract paid close to Medicaid rates, and since the Exchange was going to attract Medicaid-like enrollees they were able to negotiate a rate close to their current rates, but a little higher. The large commercial carrier at the same time was not able to negotiate anywhere near that rate discount but was pleased to be able to get an improvement. This reimbursement difference alone would contribute to at least a 35% – 50% rate differential. Examples such as this have occurred through many of the exchanges and have led to many rate differences.

In addition, some plans have proposed “narrow networks” where providers agreeing to significant discounts and which have demonstrated performance advantage are included in the network. This has resulted in favorable rates in many situations. Some plans have used “broad networks” where almost any provider is included in the network. The exchange has no requirement about breadth of network. Individuals signing up for coverage in the exchange are going to have to carefully assess what providers are included in the networks. The lower premium rates might be the results of narrower networks with limited access.

In summary, the news has been more encouraging than expected, at least by many; however, there are many idiosyncrasies that need to be considered before making a final judgment regarding the Affordable Care Act and the exchanges. Rates will be available October 1, assuming no further delays, and then we will be able to make final assessments.

1 ASPE Issue Brief: Market Competition Works: Proposed Silver Premiums in the 2014 Individual and Small Group Markets Are Nearly 20% Lower than Expected.