Tag Archives: middle market

This Is Not Your Father’s Life Insurance

Soon-to-be published editions of dictionaries will list “InsureTech” as one of the newest words. We all own a piece of that new word and all that comes along with it. More than a new word, it is becoming a new world in the insurance industry. We’re on an InsureTech expedition.

Having spent decades of my life developing products, marketing programs and delivery systems in the life insurance vertical, I feel compelled to share some insights into the unique characteristics of the life insurance segment within the InsureTech movement. I will offer a recipe for an end-to-end digital system that bypasses legacy system quagmires and shifts digital life insurance sales into warp speed in both the consumer-direct and agent-broker categories.

But first a few words about what makes life insurance different from other types of insurance, along with some commentary on the state of affairs in today’s market.

Life Insurance Is Optional

Let’s think about the major types of insurance that consumers buy. Auto, home, health and life. We are required by law in all but two states to have auto insurance. If you have a mortgage on your home, you are required by the lender to have homeowners insurance. Federal law now requires that most of us must have a health insurance policy. These types of coverage are not optional. You don’t see articles about a trillion-dollar middle market coverage gap in the auto and homeowners insurance segments.

See also: What’s Next for Life Insurance Industry?  

But there is a trillion-dollar life insurance coverage gap in the middle-market today in the U.S. Why is that?

First, the process of obtaining a life insurance policy for typical middle-market needs is overwhelming, tedious, intimidating and mysterious for consumers. We’re talking about a basic term life policy with coverage of $250,000 or $500,000 or, OK, perhaps a million dollars of coverage in some cases in the middle market. Seems like it should be easy. But, even though we have seen price reductions across the board during the past 20 years for term life, individual life insurance ownership has actually decreased. The buying process is broken.

Second, combine the antiquated buying process with the fact that the purchase of life insurance is optional, and consumers repeatedly push the chore of buying life insurance to the bottom of their to-do lists. To make matters worse, because the fulfillment process for these smallish policies is so expensive for brokers and agents, they cannot make a profit focusing on the middle market. You end up with an unmotivated distribution system and a trillion-dollar coverage gap.

You Also End Up With a Trillion-Dollar Opportunity

I’ve taken it upon myself to write down the recipe for a digital process for capturing a sizable share of that opportunity.

This is what we need to mix together to end up with a complete system that is capable of starting with “Hello” and ending minutes later with a completed transaction: an “in-force” policy for the consumer.

  • User-friendly graphical user interface for both consumers and agents. (You would be surprised.)
  • Easy quote engine — provides all relevant price quotes so you don’t jump back and forth looking at one quote at a time. First thing I notice about most designs is that you have to keep re-entering inputs to see different quotes instead of being able to scan all of them on one screen.
  • Digital life insurance application process. Simple application language. Find just the right balance between just enough questions and not too many questions per screen.
  • Decision time. Consumer-direct or agent-assisted? Both models will become more numerous in the marketplace. Carriers need to understand that many consumers need and want some level of assistance. So, carriers need to be prepared to offer chat and over-the-phone assistance to complete the online process. Perhaps even full-blown call center agent “take over” of the application process when the applicant calls for help. Or some combination of these.
  • Collection of contact information from website visitors who are “just looking” so that carriers can conduct email and phone nurturing campaigns. Carriers need to understand and appreciate the tremendous dollar value of these campaigns and not leave a huge percentage of potential revenue on the table.
  • Compliance with Do Not Call and telecommunications statutes and CAN-SPAM. By the way, CAN-SPAM is widely misunderstood, and many marketers do not understand the generous powers it provides to contact potential customers via email. Email is still the “killer app” it was labeled as many years ago. Text messaging is a first cousin for certain market segments. Special language is needed on website(s) dealing with consumer permissions to use their mobile number.
  • Secure payment gateway to provide PCI-compliant credit card processing and deliver premium payments to the carrier. The ability to accept consumers’ checking or savings account numbers for payment is also necessary. Payment screens need to be seamless, transparent and simple.
  • Secure digital signature interface for consumer-direct and face-to-face sales as well as agent-assisted phone sales. All are slightly different. All are important. Again, seamless, transparent and simple.
  • Behind-the-scenes secure interfaces to the Medical Information Bureau (MIB), motor vehicle records (MVR) provider and pharmacy records (Rx) provider must be built to provide capability for real-time queries and retrieval of third-party data.
  • If the life insurance product being purchased does not require a medical exam (“non-medically underwritten,” which requires no blood or urine tests), then the process can proceed to the next step, which is the underwriting decision engine. If the design and pricing of the life insurance product do require blood and urine testing (“fully underwritten”), then the system will present a screen in the process for an appointment to be scheduled. Many designs are getting away from blood and urine testing, but, to be realistic, these tests will still be needed in many cases for years. This topic deserves to be considered in the system design sessions.
  • Underwriting decision engine that compiles all answers provided by the consumer on the digital application form with the MIB, MVR and Rx data. In real time, the underwriting engine then renders a decision on the application. Some straight-through systems are considering using third party software for this. Others have their own, proprietary engines that afford much faster adjustments to the underwriting engine rules and settings. Controlling the underwriting engine technology also can be the difference between a “go” or a “no go” answer when seeking to add features, change processes, edit code and take other similar actions, which are needed on a continuing basis, and sometimes quickly.
  • As applications are approved, the system must package the approved policy for the state of issue with all the necessary additional pages, such as HIPAA forms, Consent to Do Business Electronically forms and other pages, which can vary from state to state. This policy package must be provided to the new customer, the policyholder, in real time using a secure link for downloading.
  • All data pertaining to the new customer’s file must be transferred to the carrier’s administrative system in real time. A new customer is born.
  • Finally, a deep and broad suite of analytics must be baked into the system’s DNA and designed to manage the business being put on the books on a daily basis. Take this data in real time and reinforce what is working. Correct that which is not. Just this one necessary component alone could be the topic of an article several times the length of this one. We’ll get right on that.

See also: InsurTech Can Help Fix Drop in Life Insurance  

These are the many pieces that I truly believe are necessary to work together perfectly to achieve the kind of disruption that is so necessary. We’re already all over this one.

Experience Mod Is Losing Key Role

The insurance industry has a reputation for being slow to change, but the “big data” revolution is driving significant changes in workers’ compensation underwriting. The emerging use of “big data” analytics in underwriting is diminishing the purpose and value of the experience modification factor and beginning to affect middle-market agents and their clients.

Big data has already redefined industries like retail (Amazon), entertainment (Netflix) and content publishing (Facebook). Stock and mortgage brokers are well ahead of insurance with their own predictive models. Big data in insurance is still under the radar for many, but it’s beginning to affect pricing and how agents work with their middle-market clients.

The National Council of Compensation Insurance’s (NCCI) experience rating plan was created to adjust premium costs to reflect “the unique claims experience of each eligible individual employer relative to other employers within the same industry group.” The experience rating plan helps insurers charge the appropriate premium for an individual employer’s work comp policy. Or, as one actuary stated, “The experience mod is a predictive indicator of future losses.” Traditionally, a higher experience mod predicts that the employer will have greater than expected losses in the coming policy period, so the insurer needs additional premium for the risk.

Many experts would agree that the experience rating plan, created in the 1930s, has historically served the insurance industry well. However, we are entering a new era where individual insurers are building their own predictive analytics models because of:

  • Recent and swift explosion of huge databases;
  • Inexpensive computing power and data storage; and
  • Advances in data acquisition and aggregation from multiple sources.

Computer hardware and software advancements, along with smart people, now allow insurers to quickly process millions of calculations, analyze the data they produce and promptly validate their emerging predictive models. Prior to these technological advances, insurers relied on the rating bureaus, such as NCCI, to collect and manage the data.

In addition, there are significant inefficiencies in the rating system that data-savvy insurers can leverage to gain a competitive advantage. For example, they can analyze their own data instead of relying on the rating bureau’s broader, aggregate view to create a competitive advantage.

Let’s assume the rating bureau’s data indicates that claim costs are rising for plumbers in a given state. The rating bureau will likely increase advisory and expected loss rates for plumbers in the entire state. However, an individual insurer may analyze its own book of business and see a decrease in claims costs for that state’s plumbers. The carrier could set a lower premium for plumbers and capture greater market share from competitors that only use aggregated rating bureau data.

It’s no surprise that large global actuarial and consulting firms are working with insurers to develop and enhance predictive models. Insurers already possess a treasure trove of data just waiting for those, affectionately known as “data nerds,” to spin it into gold. As one actuary from a well-known consulting firm said at a recent industry conference, “Underwriters have been using about six to eight data points to determine acceptability and pricing of a risk. We can build them a model with 400 to 600 data points.”

Big data brings big opportunities to insurers and agents; however, as with any collision of old-world and new-world methodologies, there will be some challenges and casualties. For example, let’s assume an underwriter receives an application for a workers’ compensation renewal, and the experience modification factor is renewing lower than the prior year. And the governing class code advisory rate is lower, as well.

However, the insurer’s predictive model indicates an increase in pricing is needed. As a result, the underwriter removes the scheduled credit and adds a scheduled debit to the pricing. Now, the agent has to explain an unexpected higher premium to the client.

Or, worse, the underwriter cannot even make an offer because the maximum allowed scheduled debit will not provide the pricing needed, according to the predictive model. In this case, an applicant’s reduced experience modification factor actually prevented the employer from getting a renewal offer from its current or preferred insurer. This may seem crazy, but when you add more and new data to a pricing model, you often get a different indicator.

Enhanced data analytics can turn traditional rating and pricing upside down. The purpose of the rating bureau’s experience rating plans is to assist the insurers appropriately set a price for the risk. However, with advanced analytics and regulations mandating the use of the experience mod, employers may find themselves in the residual market because the insurer was unable to make an offer at their price.

Workers’ compensation experience rating and experience modification factors are not going away any time soon; they are enmeshed into each state’s regulatory and statutory framework. And not all insurers will create and use their own predictive models, so some will continue to rely on the rating bureaus. However, you’re probably beginning to see anomalies between the old world of “predictive indicators of future losses” and the new world of insurance-specific predictive analytics.

Agents must not only be aware of these underwriting changes but must educate their clients and prospects. The brightest future belongs to employers that can move the loss data in the right direction over the long term. The agent’s role is to help them establish processes to make that happen.

As with most leadership challenges, agents need to start with a new conversation and dialog. Questions might include:

  • Are you aware of how the “big data” revolution is affecting your insurance program and pricing?
  • Has anyone shared with you how the insurance company’s underwriting process is going through its most dramatic change in more than 50 years?
  • Have you taken steps to adapt and align your business objectives and risk management practices to leverage this new approach?

Agents often say they want a way to differentiate in a crowded and noisy marketplace.  This underwriting revolution presents a sustainable competitive advantage to those willing to invest in gaining knowledge and expertise.

3 Analytics Strategies for the Middle Market

As if there isn’t enough pressure on middle market carriers today, with the big players combining to get even bigger and with the rolling up of supply chains — the carriers are now faced with a strategic imperative: Make sense of their data through analytics.

Meeting that imperative comes with a new competitive issue: fighting the war for talent to recruit and retain data scientists.

The demand for data scientists is spiking at a time when it can’t be met by supply. The largest organizations have enough scale to fund and attract a team of analysts, but what is the middle market insurer to do?

There are some straightforward strategies:

Count on partners

Many of the business demands for analytics will be met with software tools. The vendors for these solutions will be more than happy to have some data science types participate in your implementation and help to sort out your data. The same is true of marketing campaign vendors. They will have in their circles the experts needed to slice and segment targets, just like the large insurers can do on their own.

Services vendors

The services vendors are investing and building muscle in big data and analytics. Just as insurers augment their in-house actuarial talent when needed, we see the ecosystem of services vendors maturing nicely. You may pay more per hour than if you hired someone, but you only pay for what you need and you get a team that has “been there and done that.”

Decide not to decide

We talk to a lot of middle market companies that are looking at big data analytics. Some are saying that they aren’t seeing the demand for it from the business areas. They know this may mean that people aren’t doing enough to evangelize about analytics within the business, but analytics have no value if they don’t meet some kind of demand. If there’s no demand, push analytics out on the road map — but keep it on the road map. That allows you to revisit the subject when the labor market for data science talent is less frothy.

As is often the case, the reality is that most of the companies we see are doing some combination of these three strategies. They are engaging tool vendors for particular complementary needs, reaching into the service companies when that makes sense and putting the investment in their full-time staff until resources are more available.

At the end of the day, we see the middle market reacting creatively and nimbly to the challenge. But, hey, that’s what they do with all of the challenges they face, so why would this time be any different?