Tag Archives: pricing model

New, Troubling Healthcare Model

As physicians and hospitals compete for the “under 65” patient — whose payments are generally 150%-plus higher than for a Medicare patient — they have to determine their pricing model. The traditional choice is to offer a low price per service based on a higher volume, or a high price per service based on a lower volume. But some are charging a higher price with the goal of generating higher volume, and their number may increase.

Healthcare spending is already 17.5% of U.S. GDP and is expected to hit 20% by 2025. This high-price/high-volume approach could exacerbate the problem.

See also: Healthcare: Time for Independence  

Part of the reason for concern is a recent landmark decision, in which Cigna was ordered to pay an out-of-network provider more than $13 million to cover certain alleged underpaid claims and ERISA penalties. ERISA is the federal law governing large employers that self-insure their medical plans (generally those with more than 250 employees).

In the lawsuit, Cigna alleged that the supplier was failing to collect the patients’ deductibles and coinsurance. The carriers’ intent is for the supplier to collect the deductible and coinsurance to make patients aware of the supplier’s charges and of their shared responsibility for the bill. Cigna took the position that, if the healthcare supplier does not collect any payment from the patient, the provider is accepting as “payment in full” the amount processed by Cigna on behalf of the employer. Cigna argued that, if the patient’s portion under the Summary Plan Documents (the carrier’s contract with the employer and the employee) is waived, then the plan’s portion is waived, as well.

When suppliers “forgive the patient liability,” these healthcare providers often have a revenue model of very high prices with the goal of higher volumes. They’ll entice the patient to use their more expensive services because the patient does not have to pay anything — and the higher payment to the healthcare provider under the plan will more than cover the liability that the provider forgave for the patient, even though the average employee deductible is high, at $1,300.

Most patients have not grasped that healthcare suppliers are running a business and that prices vary by as much as 300% within a network. As a result, while employees (the patients) may save money when the provider waives their financial responsibility, they lose in the end. That’s because their employers’ costs increase, resulting in higher health insurance costs, with larger deductibles and payroll contributions for all employees.

The court’s decision to reject Cigna’s claims creates further risk to the affordability of healthcare for employers, as employees will be financially motivated to access care from suppliers with higher prices because the patient’s liability is forgiven. Can we expect other healthcare suppliers to implement a revenue model tied to high prices with no patient liability?

See also: AI: The Next Stage in Healthcare  

Conversations with a number of healthcare suppliers shows that many do not realize that most large employers self-insure their medical plans; the suppliers perceive that the insurance carriers covers the costs. The purchasers (the employers) have the opportunity to engage the healthcare suppliers (hospitals/physicians) in a discussion around supply chain management, quality and patient safety, so the providers fully comprehend that the one ultimately paying the bill is monitoring their performance.

We’ll look forward to sharing the results of this type of collaboration, now underway in a major market. It’s time for employer-driven healthcare.

The 2 New Realities Because of Big Data

I have some bad news. There are no longer any easy or obvious niches of sustained, guaranteed profits in insurance. In today’s environment of big data and analytics, all the easy wins are too quickly identified, targeted and brought back to par. If you’ve found a profitable niche, be aware that the rest of the industry is looking and will eventually find it, too.

Why? The industry has simply gotten very good at knowing what it insures and being able to effectively price to risk.

Once upon a time, it was sufficient to rely on basic historical data to identify profitable segments. Loss ratio is lower for small risks in Wisconsin? Let’s target those. Today, however, all of these “obvious” wins stand out like beacons in the darkness.

To win in a game where the players have access to big data and advanced analytics, carriers should consider two new realities:

  • You can’t count on finding easy opportunities down intuitive paths. If it’s easy and intuitive, you can bet that everyone else will eventually find it, too.
  • Sustainable opportunities lie in embracing the non-obvious and the counter-intuitive: finding multivariate relationships between variables, using data from novel sources and incorporating information from other coverages.

Just knowing what you insure is only the start. The big trick is putting new information to good use. How can carriers translate information on these new opportunities into action? In particular, how can carriers better price to risk?

We see two general strategies that carriers are using in pricing to risk:

  • Put risks into categories based on predicted profitability level
  • Put risks into categories based on predicted loss

The difference appears subtle at first glance. Which approach a given carrier will take is driven by its ability to employ flexible pricing. As we will now explore, it’s possible for carriers to implement risk-based pricing in both price-constrained and flexible-rate environments.

Predicting Profitability: Triage Model

In the first strategy, carriers evaluate their ability to profitably write a risk using their current pricing structure. This strategy often prevails where there are constraints on pricing flexibility, such as regulatory constraints, and it allows a carrier to price to risk, even when the market-facing price on any given risk is fixed.

The most common application here is a true triage model: Use the predicted profitability on a single risk to determine appetite. Often, the carrier will translate a model score to a “red/yellow/green” score that the underwriter (or automated system) uses to guide her evaluation of whether the risk fits the appetite. The triage model is used to shut off the flow of unprofitable business by simply refusing to offer coverage at prices below the level of profitability.

A triage model can also be implemented as an agency-facing tool. When agents get an indication (red/yellow/green again), they start to learn what the carrier’s appetite will be and are more likely to send only business that fits the appetite. This approach has the added benefit of reducing underwriting time and expense for the carrier; the decline rate drops, and the bind/quote rate rises when the agents have more visibility into carrier appetite.

A final application carriers are using is in overall account evaluation. It may be that a carrier has little or no flexibility on workers’ compensation prices, but significant pricing flexibility on pricing for the business owners policy (BOP) cover. By knowing exactly how profitable (or unprofitable) the WC policy will be at current rates, the carrier can adjust price on the BOP side to bring the entire account to target profitability.

Predicting Loss: Pricing Model

If a carrier has pricing flexibility, pricing to risk is more straightforward: Simply adjust price on a per-risk basis. That said, there are still several viable approaches to individual risk pricing. Regardless of approach, one of the key problems these carriers must address is the disruption that inevitably follows any new approach to pricing, particularly on renewal business.

The first, and least disruptive, approach is to use a pricing model exclusively on new business opportunities. This allows the carrier to effectively act as a sniper and take over-priced business from competitors. This is the strategy employed by several of the big personal auto carriers in their “switch to us and save 12%” campaigns. Here we see “know what you insure” being played out in living color; carriers are betting that their models are better able to identify good risks, and offer better prices, than the pricing models employed by the rest of the market.

Second, carriers can price to risk by employing a more granular rate structure. This is sometimes referred to as “tiering” – the model helps define different levels of loss potential, and those varying levels are reflected in a multi-tiered rate plan. One key advantage here is that this might open some new markets and opportunities not in better risks, but in higher-risk categories. By offering coverage for these higher-cost risks, at higher rates, the carrier can still maintain profitability.

Finally, there is the most dramatic and potentially most disruptive strategy: pricing every piece of new and renewal business to risk. This is sometimes called re-underwriting the book. Here, the carrier is putting a lot of faith in the new model to correctly identify risk and identify the correct price for all risks. It’s very common in this scenario for the carrier to place caps on a single-year price change. For example, there may be renewals that are indicated at +35% rate, but annual change will be limited to +10%. Alternatively, carriers may not take price at all on renewal accounts, unless there are exposure changes or losses on the expiring policy.

Know What You Insure

Ultimately, the winners in the insurance space are the carriers that best know what they insure. Fortunately, in an environment where big data is becoming more available, and more advanced analytics are being employed, it’s now possible for most carriers to acquire this knowledge. Whether they’re using this knowledge in building strategy, smarter underwriting or pricing to risk, the results are the same: consistent profitability.

Sometimes there are pricing constraints that would, at first glance, make effectively pricing to risk challenging. As we have discussed, there are still some viable approaches for carriers facing price inflexibility. Even for carriers with unlimited price flexibility, pricing to risk isn’t as easy as simply applying a model rate to each account; insurers must take care to avoid unnecessary price disruption. We’ve discussed several approaches here, as well.

Effectively pricing to risk gives carriers the opportunity to win without relying on protecting a secret, profitable niche. In the end, this will give them the ability to profit in multiple markets and multiple niches across the entire spectrum of risk quality.

3 Key Steps for Predictive Analytics

The steady drumbeat about the dire need for data and predictive analytics integration has been there for several years now. Slowly, many carriers have started to wake up to the fact that predictive analytics for underwriting is here to stay. According to Valen Analytics’ 2015 Summit Survey, 45% of insurers who use analytics have started within the past two years, and, of those that don’t currently implement analytics, 56% recognize the urgency and plan to do so within a year. Although it used to be a competitive advantage in the sense that few were using predictive analytics, it can now be viewed as table stakes to protect your business from competitors.

The real competitive advantage, however, now comes from how you implement predictive analytics within your underwriting team and focus its potential on strategic business issues. New competitors and disruptors like Google won’t politely wait around for insurers to innovate. The window to play catch-up with the rest of tech-driven businesses is getting narrower every day, and it’s either do or die for the traditional insurance carrier.

All of this buzz about data and predictive analytics and its importance can be deafening in many ways. The most important starting point continues to center on where to get started. The most pertinent question is: What exactly are you trying to solve?

Using analytics because everyone is doing it will get you nowhere fast. You need to solve important, tangible business problems with data-driven and analytic strategies. Which analytic approach is best, and how is it possible to evaluate the effectiveness? Many insurers grapple with these questions, and it’s high time the issue is addressed head-on with tangible steps that apply to any insurer with any business problem. There are three key steps to follow.

First Step: You need senior-level commitment.

You consume data to gain insights that will solve particular problems and achieve specific objectives. Once you define the problem to solve, make sure that all the relevant stakeholders understand the business goals from the beginning and that you have secured executive commitment/sponsorship.

Next, get agreement up front on the metrics to measure success. Valen’s recent survey showed that loss ratio was the No. 1 one issue for underwriting analytics. Whether it’s loss ratio, pricing competitiveness, premium growth or something else, create a baseline so you can show before and after results with your analytics project.

Remember to start small and build on early wins; don’t boil the ocean right out of the gate. Pick a portion of your policies or a test group of underwriters and run a limited pilot project. That’s the best way to get something started sooner than later, prove you have the right process in place and scale as you see success.

Finally, consider your risk appetite for any particular initiative. What are the assumptions and sensitivities in your predictive model, and how will those affect projected results? Don’t forget to think through how to integrate the model within your existing workflow.

Second Step: Gain organizational buy-in.

It’s important to ask yourself: If you lead, will they follow? Data analytics can only be successful if developed and deployed in the right environment. You have to retool your people so that underwriters don’t feel that data analytics are a threat to their expertise, or actuaries to their tried-and-true pricing models.

Given the choice between leading a large-scale change management initiative and getting a root canal, you may be picking up the phone to call the dentist right now. However, it doesn’t have to be that way. Following a thoughtful and straightforward process that involves all stakeholders early goes a long way. Make sure to prepare the following:

  • A solid business case
  • Plan for cultural adoption
  • Clear, straightforward processes
  • A way to be transparent and share results (both good and bad)
  • Training and tech support
  • Ways to adjust – be open to feedback, evaluate it objectively and make necessary changes.

Third Step: Assess your organization’s capabilities and resources.

A predictive analytics engagement is done in-house or by a consultant or built and hosted by a modeling firm. Regardless of whether the data analytics project will be internally or externally developed, your assessment should be equally rigorous.

Data considerations. Do you have adequate data in-house to build a robust predictive model? If not, which external data sources will help you fill in the gaps?

Modeling best practices. Whether internal or external, do you have a solid approach to data custody, data partitioning, model validation and choosing the right type of model for your specific application?

IT resources. Ensure that scope is accurately defined and know when you will be able to implement the model. If you are swamped by an IT backlog of 18-24-plus months, you will lose competitive ground.

Reporting. If it can be measured, it can be managed. Reporting should include success metrics easily available to all stakeholders, along with real-time insights so that your underwriters can make changes to improve risk selection and pricing decisions.

Boiling this down, what’s critical is that you align a data analytics initiative to a strategic business priority. Once you do that, it will be far easier to garner the time and attention required across the organization. Remember, incorporating predictive analytics isn’t just about technology. Success is heavily dependent on people and process.

Make sure your first steps are doable and measurable; you can’t change an entire organization or even one department overnight. Define a small pilot project, test and learn and create early wins to gain momentum by involving all the relevant stakeholders along the way and find internal champions to share your progress.

Recognize that whether you are building a data analytics solution internally, hiring a solution provider or doing some of both, there are substantial costs involved. Having objective criteria to evaluate your options will help you make the right decisions and arm you with the necessary data to justify the investment down the road.