Tag Archives: auto insurer

Traditional Insurance Is Dying

Finance. Taxis. Television. Medicine. What do these have in common?

They’re all on the long–and growing–list of industries being turned upside down by disruptive technology. 

The examples are legion. Once-sure-bet investments like taxicab medallions are at risk of going underwater. Bitcoin is giving consumers the power to bypass banks. Traditional television is at risk from online streaming.

Insurance Is No Different

In fact, innovative players have been disrupting the insurance market since before “disruption” was the buzzword it is today. 

Look at Esurance, which in 1999 rode the dot-com wave to success as the first insurance company to operate exclusively online. No forms, no policy mailers–it didn’t even mail paper bills.

By going paperless, Esurance told customers that it was the kind of company that cared about their preferences–and established itself as a unique player in an industry that places a premium on tradition. Insurance isn’t known for being innovative. 

Most insurance leaders operate under the assumption that if it ain’t broke, you shouldn’t fix it. And in a heavily regulated industry, that’s not totally unreasonable. 

But you only have to look at the scrappy start-ups that are taking down long-established players to understand what awaits the companies that aren’t willing to innovate.

Thinking Outside the Box

Take Time Warner–profit fell 7.2% last quarter as industry analysts foretold “the death of TV.” Meanwhile, Netflix’s profits are soaring beyond expectations–even as the risks it takes don’t always pan out. 

Remember the “Marco Polo” series that cost a reported $90 million? Neither does anyone else. But for every “Marco Polo” there’s an “Orange Is the New Black.” Highly successful programs on a subscription model show that Netflix’s willingness to take risks is carrying it past industry juggernauts.

The market is changing–and if you want to stay competitive, you need to use every weapon in your arsenal. Millennials aren’t buying insurance at the rate their parents did

To a consumer population weaned on technology like Uber and Venmo, the insurance industry seems positively antiquated. Facebook can advertise to you the brand of shoes you like–so your insurance company should be able to offer a product that you actually want.

The Information Importance

According to Accenture, “Regulated industries are especially vulnerable” to incumbents. When there are barriers to entry based on licensing requirements or fees, competition is lower. Decreased competition, in turn, leads to less incentive to innovate. This can leave regulated industries, such as insurance, healthcare and finance, in a highly vulnerable position when another company figures out a way to improve their offerings.

Other attributes that can make an industry vulnerable, per Accenture’s findings, can include:

  • Narrow focus: If a brand focuses entirely on cost savings, convenience or innovation, it isn’t effectively covering its bases. A disruptor that manages to offer two or three of these factors instead of just one has a near-immediate advantage.
  • Small scope or targets: Failing to expand offerings to all demographics can mean that industries or service providers aren’t able to replicate the broad reach of disruptors.
  • Failing to innovate: Disruptors don’t always get their product right on the very first try. Companies must innovate continuously and figure out ways to build continuous improvement into their business model.

Tech start-ups use information as an asset. How can you tell if information is a valuable weapon in the battle you’re fighting? 

“Big data” isn’t just a buzzword; industry analysts are calling it the wave of the future. At Citi, they’re talking about “the feed”: a real-time data stream that leverages the Internet of Things to reshape risk management. 

Auto insurers are turning to connected cars to let them reward safe drivers. Some life insurers are even offering discounts to customers who wear activity trackers.

It Can Happen to You

For most insurance companies, incorporating an unknown element into the way they operate is daunting. 

But talk to any cab driver, grocery store clerk or travel agent, and they’ll tell you that the only way to survive in a technology-driven world is to innovate.

Look at the insurance technology market to see what improvements you can incorporate into your organization, and think expansively about how you can use information: for agency management, to attract new customers and retain old ones, to expand your profit margins or to streamline operating costs. 

Your survival depends on it.

Is the Data Talking, or Your Biases?

In April, a large life insurer announced plans to use Fitbit data and other health data to award points to insureds, providing impressive life insurance discounts for those who participated in “wellness-like” behaviors. The assumption is that people who own a Fitbit and who walk should have lower mortality. That sounds logical. But we’re in insurance. In insurance, logic is less valuable than facts proven with data.

Biases can creep into the models we use to launch new products. Everyone comes to modeling with her own set of biases. In some conference room, there is probably something like this on a whiteboard: “If we can attract people who are 10% more active, in general, we will drive down our costs by 30%, allowing us to discount our product by 15%.”

That is a product model. But that model was not likely based on tested data. It was likely a biased supposition pretending to be a model. Someone thought he used data, when all he did was to build a model to validate his assumptions.

Whoa.

That statement should make us all pause, because it is a common occurrence – not everything that appears to be valid data is necessarily portraying reality. Any data can be contorted to fit someone’s storyline and produce an impostor. The key is to know the difference between data cleansing/preparation and excessive manipulation. We continually have to ask if we are building models to fit a preconceived notion or if we are letting the data drive the business to where it leads us.

Biases hurt results. When I was a kid, my Superman costume didn’t make me Superman. It just let me collect some candy from the neighbors. Likewise, if insurers wish to enter into an alternate reality by using biased data, they shouldn’t expect results that match their expectations. Rose-colored glasses tend to make the world look rosy.

Here’s the exciting part, however. If we are careful with our assumptions, if we wisely use the new tools of predictive analytics and if we can restrain ourselves from jumping through our hypotheses and into the water too soon, objective data and analytics will transport us to new levels of reality! We will become hyper-knowledgeable instead of pseudo-hyper-knowledgeable.

Data, when it is used properly, is the key to new realms, the passport to new markets and to a secure source of future predictive understanding. First, however, we have to make it trustworthy.

Advocating good data stewardship and use.

In general, it should be easy to see when we’re placing new products ahead of market testing and analysis. When it comes to insurance, real math knows best. We’ve spent many decades perfecting actuarial science. We don’t want to toss out fact-based decisions now that we have even more complete, accurate data and better tools to analyze the data.

When we don’t use or properly understand data, weak assumptions begin to form. As more accurate data accumulates and we are forced to compare that data with our pre-conceived notions, we may be faced with the reality that our assumptions took us down the wrong path. A great example of this was long-term care insurance. Many companies rushed products to market, only later realizing that their pricing assumptions were flawed because of larger-than-expected claims. Some had to exit the business. The companies remaining in LTC made major price increases.

Auto insurers run into the same dangers (and more) with untested assumptions. For example, who receives discounts, and who should receive discounts? Recently, a popular auto insurer that was giving discounts to drivers with installed telematics, announced that it would begin increasing premiums on drivers who seemed to have risky driving habits. The company had assumed that those who chose to use telematics would be good drivers and that just having the telematics would cause them to drive more safely. The resulting data, however, proved that some discounts were unwarranted; just because someone was willing to be monitored didn’t mean she was a safe driver.

Now the company is basing pricing on actual data. It has also implemented a new pricing model by testing it in one state before rolling it out broadly – another step in the right direction.

When we either predict outcomes before analyzing the data or we use data improperly, we taint the model we’re trying to build. It’s easy to do. Biases and assumptions can be subtle, creeping silently into otherwise viable formulas.

Let’s say that I’m an auto insurer. Based on an analysis of the universe of auto claims, I decide to give 20% of my U.S. drivers (the ones with the lowest claims) a discount. I’m assuming that our mix of drivers is the same as the mix throughout the universe of drivers. After a year of experience, I find that I am having higher claims than I anticipated. When I apply my claims experience to my portfolio, I find that, actually, only the top 5% are a safe bet for a discount, based on a number of factors. Now I’ve given a discount to 15% more people than ought to have had it. Had I tested the product, I might have found that my top 20% of U.S. drivers were safe drivers but were also driving higher-priced vehicles – those with a generally higher cost per claim. The global experience didn’t match my regional reality.

Predictions based on actual historical experience, such as claims, will always give us a better picture than our “logical” forays into pricing and product development. In some ways, letting data drive your organizations decisions is much like the coming surge of autonomous vehicles. There will be a lot of testing, a little letting go (of the driver’s wheel) and then a wave of creativity surrounding how the vehicle can be used effectively. The result of letting the real data talk will be the profitability and longevity of superior models and a tidal wave of new uses. Decisions based on reality will be worth the wait.

Managed Care Isn’t Managed or Care

It is time to get angry. I plead with you all to recognize the king’s new clothes as he stands indignantly naked between the 300-pound gorilla and the elephant in this room called workers’ compensation. I am hereby calling out an industry that has turned “managed care” into “manipulated cost,” with a shameful lack of transparency and a churning mass of workers’ compensation claims.

Decades ago, the cottage industry of bill review provided a legitimate and needed service directly between healthcare provider and payer. Fast forward to the scheme that has evolved, with some healthcare networks and other consortiums that purchase services conspiring with some insurers and third-party administrators (TPAs). These arrangements may set up shadowy deals where medical goods and services are marked up by intermediary agents and where perpetrators split the profits — at the expense of unknowing employers.

Sadly, there is little outrage from employers. Perhaps the amounts seem negligible while the methods seem to be an acceptable cost of doing business. Risk managers have so much else to think about. Maybe managed care is considered to be essential, so no one in their right mind would question it.

I believe the employer-buyer holds a false sense of security that we need to get beyond. The drip-drip-drip of money siphoned from actual employee care needs to finally strike a collective nerve so that real change can happen. To that end, I want to list some considerations that I hope will spark outrage and lead to change.

Quick Tip: Managed Care Food for Thought; Indigestion Guaranteed:

Cognitive Dissonance: Claims providers justify bill-review fee schemes based on the notion that there are no controls from provider-sources… but wait a minute… they also take network fees because they apparently can control bills from provider sources! Anyone feel mildly conflicted trying to reconcile this logic? How can both facts exist?

Deliberate Mystery: Bill review as founded decades ago should no longer be necessary. Today’s technology obviates the premise that all WC bills need to be hand-checked against a fee schedule or that licenses for CD-ROMs holding schedules or “reasonable and customary” data are valuable. When it comes to group health, providers make it their business to know what deductibles and reimbursements apply while you stand at the window. Why is WC a deliberate mystery?

Technology Hypocrisy: The WC claims industry purports to have data and IT capability that can predict and fast-track claims, allowing ever higher (arguably untenable) adjuster caseloads, yet when it comes to monitoring fees pretends we are still in the 1990s. I submit that today’s technology can lock healthcare providers into correct billing. Aggregate provider monitoring/auditing by the adjusting entity can support accuracy. High automation and negligible cost should make fee oversight an included aspect of claim service, with network affiliation requiring providers to contribute to the technology. As our national provider base moves into more hospital-centered conglomerates, there are far more IT resources available on the provider end.

Core Responsibility: TPAs and insurers are paid to adjust benefits per state statutes. But charging a percentage of savings for medical care in a state with a fee schedule is as ridiculous as would be charging a percentage of “savings” for reducing an average weekly wage to the statutory comp-rate. What is the difference when it comes to core claim adjusting obligations?

No Repeat Value Added: We can agree that complicated in-patient bills are worthy of review and that fees for that review are justified. But there routinely are recurring bills from the same providers on the same file. After the initial adjustment, why should claim payers charge a fee for adjusting the exact same bill repetitive times? Seriously… there is no value added in charging for a task already performed. Here is an exercise: Look at your claim payment register to see the same $1.15 bill review fee and $5.32 network fee charged on the same $48 physical therapy bill over and over and over. Maddening, isn’t it?

How Would You Feel?: Let’s make it personal. What if your homeowners or auto insurer mandated your use of a repair-provider-network? Your damaged car gets fixed, and you are presented with a bill marked up by the network — not knowing what the actual body shop charged. You pay your deductible, leaving “profit” for your insurer and network to split… Hmm…

Low-Quality Reality: Network discounts have come to roost, leaving healthcare provider frustration high and quality of care low. I submit that higher quality deserves higher fees.

New Network Charge: Efforts to define, seek and sustain quality should be at the forefront of network effort. Can you imagine providers competing based on quality to join exclusive, well-paying networks? Astute, unbundled and self-administered employers that seek medical quality often pay more with confidence and get better results. Today’s bundled programs beget providers willing to work cheap and approach profit on a volume-of-treatment basis. More visits means more weeks/months/years open equals more money for the entities otherwise trusted to resolve claims. Isn’t this reality the opposite of what should be managed care?

Employers… Get angry: Start asking questions and making demands. Let’s start with disclosure of end-provider fees. Ask your legislators to crack this issue open and make corrections accordingly. I call upon some major broker to take the lead and create a “Managed Care Bill Of Rights.” Wouldn’t that be a great distinction?

Bottom Line: The insurance/claims/managed care industry cannot beg for the trust of those served while skimming treatment dollars. Fix the problem.