The problem child of the life insurance industry is no longer a youngster. You could even say the problem child is now officially an adult. This problem child is "The Middle Market Coverage Gap." It has been with us for such a long time, I decided to give it proper noun status.

For many years now, industry trade publications and conferences have featured articles and presentations about the tens of millions of average American heads of households, parents and spouses who have little or no life insurance. 

Ten years ago, everyone (including yours truly) who predicted that the life insurance industry would put a major dent in the coverage gap by 2020 was wrong. Not only wrong but super wrong.

LIMRA's recent "2021 Barometer Presentation on Perceived Need Gap" tells us that ownership of life insurance has decreased from 63% in 2010 to 52% in 2020. The presentation also tells us that there are about 258 million adults in the U.S. So, over 28 million fewer people have life insurance compared with just 10 years ago. 

How is this even possible?

Writing policies on the lives of our middle market friends was supposed to become so much easier, faster and more cost-effective. We have been hearing about the increasing use of e-application forms, accelerated underwriting and digital signatures. We have heard how agents just need to "drop a ticket" and the carrier would take it from there. 

But the fact remains: There were 28 million more adults with life insurance 10 years ago than there are today.

One of the explanations is that the life insurance industry is slow to change. It is guarded and staid. And I wouldn't change that for the world. The life insurance industry has long been one of the underpinnings of our economy, and it needs to stay that way. 

The other important explanation is that the promised "straight-through process" has remained elusive.

I am fortunate to work with the biggest and the best life insurance sales and marketing organizations on a national scale, and they have told me:

"We've seen pieces of the total package necessary to do a fully automated and digital term life sale. But we don't have the whole nine yards yet. Our agents need to get from 'hello' to 'here's your in-force policy' in one conversation.

"It's not good enough to do an electronic quote, complete the e-application and then see everything go offline for medical exams, blood testing, urine testing and even sometimes sending out for an attending physician statement. This turns what could have been a 20- or 30-minute sale into 20 to 30 days or longer."

Twenty to 30 days or longer? Is that right? Unfortunately, it is. I've seen many times an attending physician statement take more than three months to obtain.

I'm celebrating my 40th anniversary in the life insurance industry this year. I can tell you that the life insurance buying process that has evolved into an expensive, tedious and time-consuming proposition for agents and customers alike is one of the primary reasons for the coverage gap.

See also: Life Insurance Is Ripe for Change in 2021

As the policy buying process and underwriting requirements became more tedious, the agent community backed away from calling on the middle market. Consumers, when they caught a glimpse of what was required to obtain a policy, backed away, as well. All that backing away resulted in our very own multitrillion-dollar coverage gap.

Keep in mind that the typical sale in the middle market is relatively small. The brokerage commission does not cover the processing of underwriting requirements, the scheduling and re-scheduling of medical exams or the sales calls that are often needed to "make the sale twice" because the customer became lost in the underwriting maze and wanted out.

With a true straight-through process, the small case size can make economic sense for the agent. Such a process also solves an enormous problem for the customers of the middle market. The average consumer is intimidated and confused by the typical life insurance buying process. But they need the coverage. We have learned from the COVID-19 pandemic that most families cannot weather a financial emergency very well. COVID-19 relief legislation has helped. But Congress doesn't provide life insurance. We do. And a 100% digital, big data solution that gets from "Hello" to "Here's your in-force policy" in 20 minutes is what we need.

In November 2016, I wrote an article for Insurance Thought Leadership titled, "This is Not Your Father's Life Insurance." The article not only spelled out what the real straight-through process for buying life insurance should look like, it also kinda sorta said that we would get there.

And now for the age-old question: Are we there yet?

Yes.

We are now seeing the fruits of our labors materialized into a genuine straight-through process for term life. Here is a look at what an agent can now accomplish in about 15 minutes with a term life applicant between the ages of 18 and 70 for up to $500,000 of coverage.

• First, we do an electronic term life quote. Easy. 
• Next comes the e-application. Thorough yet concise.
• Then, the applicant provides a payment method, which will only be used if the policy is approved.
• From there we move into the digital signature for the application and necessary authorizations. 
• At the moment the digital signature is complete, the proprietary underwriting rules engine queries six third-party big data providers. They are: Medical Information Bureau, Department of Motor Vehicles, Rx, criminal history, credit score and ID verification.
• The system combines the information that the proposed insured provided to the agent on the application form with the data retrieved from the third-party data bases. The system renders a real-time underwriting decision in about 60 seconds.
• The qualified applicant's policy is immediately issued, and the new policyholder receives an in-force policy via e-policy delivery.
• This is all accomplished without any person-to-person contact. A huge plus in the age of COVID.

Pieces of the process have been around for a while. But as the big insurance distributors have told us, the total package is what we need. All these capabilities working together seamlessly produce synergy that will be a vital force in bringing the Middle Market Coverage Gap under control.

On a stand-alone basis, none of the features of the straight-through process are really all that impressive. But when welded together into the whole? They are all that.

Insurance sector communities have invaluable expertise and resources to address society’s climate challenges, but that experience is not fully understood or harnessed into the mainstream climate, sustainable development and finance agenda. The United Nations' 26th conference on climate change, known as COP26, is a strategic opportunity to finally and comprehensively bridge this gap.

With COP26 drawing ever nearer, the insurance industry has a gilt-edged opportunity to recapture its historic role as a key commercial shepherd of social transition and gain a seat at the main table in Glasgow.

Not since the age of industrialization has global society faced a challenge on the scale of climate change, and the insurance sector is uniquely placed to play a leading role in forging a workable solution; in fact, it is a challenge we are duty-bound to accept. 

When the Paris accord was adopted by 196 nations in 2015, the annual COP meetings instantly became the focal point of global efforts to tackle climate change. While some of the signatory nations have since made progress in building economic resilience against the physical and financial impacts of climate change, the urgency to do more is escalating; the demand for risk mitigation and adaptation strategies is accelerating in parallel.

Like few others, the actuarial sciences have a track record of providing support for strategic social transition at scale; the role as an architect of the social insurance systems that have underpinned many national reconstructions is well-documented.

More modern insurance tools, such as national catastrophe modeling, also have obvious applications to the climate challenge and reinforce our industry’s unique ability to accurately price risk over the longer term.

It shouldn’t be surprising that an industry built on the mathematical and philosophical foundations of the Scottish and wider 18^th century Enlightenment is now well-placed to provide assistance in the quantification of climate-related risks and the evaluation of the related choices and trade-offs.

Since the early 1990s, the insurance industry has revolutionized its mainstream assessment of climate-related risks and integrated this into its core pricing, risk controls, regulatory disclosure and capital management. A decade ago, led by Munich Re and in concert with public and academic partners, the industry created a global facility to assess the seismic risks to properties, infrastructure and wider assets.

In creating the Global Earthquake Model Foundation, the aim was to support better planning, building codes, investment, insurance and disaster response to help save the millions of lives, livelihoods and assets that were at risk. We now have the opportunity to emulate that ambition and provide a program for building a global resilience model to support physical climate risk scenarios, stress testing and analysis for the communities, markets and assets that are exposed.

Because building climate resilience is the product of many factors, insurance is not a silver-bullet solution. But it is a necessary component because, when disaster strikes, the ability to rebuild lost homes, businesses, jobs and lives is central to any economic recovery.

Through insurance, communal risks can be shared across public, private and mutual systems, via premiums, taxation and hybrid systems. With sound scientific principles, economic sustainability and transparency as the foundations, costs, payouts and incentives can be designed to support affordability, risk signaling, resilience and wider solidarity.

By November, we should have set an objective to make access to basic climate-related insurance protection systems an essential component of a climate-resilient lifestyle. In conjunction with wider financial reforms and processes, we also need to ensure that companies and local and national governments have enough support to evaluate and formally manage their contingent climate risks and liabilities. 

Society’s history with physical, industrial and social transition has shown that changes need to occur at speed and across all economies. They will require the provision of public, private and mutual insurance (including hybrid approaches) to enable a financially, socially and politically viable process. This is not just about commercial insurance products and public services; it is about the adoption of "insurance thinking" with regard to risk assessment and the creation of economically sustainable risk pricing and risk-sharing mechanisms.

See also: Increasing Regulation on Climate Change

It is a mammoth task, but we don’t have to start from ground zero for insurance to play a role in achieving Net Zero. There are organizational vehicles already in place to help speed us along this journey.

For example, the Insurance Development Forum (IDF), launched at COP21 in Paris, was created in recognition of the critical role that risk management plays in the response to climate change. The Forum is a unique international institution that brings together private and public sectors to help countries to build the resilience they need to limit the physical, social and financial impacts of climate change.

The global challenge of closing the risk protection gap brought by climate change is at the heart of the IDF’s mandate, and the forum has already found success using its Tripartite Agreement project to support major sovereign and sub-sovereign programs.

This model of shared success, augmented by inclusive insurance and mainstream market expansion across many territories, provides the ideas and facilities to support the countries looking to protect their people and assets from the dangers of climate change.

If we seize the opportunity, society may look back on COP 26 in Glasgow as the pivotal moment in climate-financial history in the same way we now refer to COP 21 in Paris for its influence on climate politics. November also may be remembered as the month the insurance sector, a sleeping giant, awakened to fulfill its potential to help quell today’s climate emergency.

As the providers of risk transfer solutions, we have always been "in the room" for discussions on climate change, but we have yet to fully take a seat at the main table where the historic solutions will be forged. 

Insurance sector communities have invaluable expertise and resources to address society’s climate challenges, but that experience is not fully understood or harnessed into the mainstream climate, sustainable development and finance agenda. COP26 is a strategic opportunity to finally and comprehensively bridge this gap.

While artificial intelligence can improve almost all of the insurance value chain, most insurers are still not leveraging AI at its full capacity.

Adopting AI and implementing hyper-automated systems can help insurance distribution, in particular, become more efficient, accurate and secure — a benefit that both companies and the end consumer will see. From improving risk analysis and fraud detection to providing more sophisticated pricing and better customer insights for faster, more personalized customer advice and services, there are many ways in which AI helps move insurance distribution forward.

Improving operational efficiency

With today’s low interest rates, insurers can no longer depend on the financial earnings of their assets and need to find new margins in their operating models. They have more pressure to increase revenues while cutting overall operational costs. 

Deploying AI to everyday back-office processes can reduce the number of manual tasks insurers face, freeing them to spend more time on tasks that support their bottom line. Insurers are able to get more done in less time and often with improved accuracy.

Enhancing insurance distribution

According to McKinsey, 80% of value driven by advanced AI in insurance will come from marketing and sales alone (versus only 10% from better risk management and 3% from gains in operational efficiency). Today, insurers face several distribution challenges — moving from physical to remote and hybrid sales networks, learning how to strike the balance between technology and human sales, adjusting multi-channel and omnichannel sales, etc.

AI can enable a more fluid and personalized experience for customers from initial lead prioritization through needs analysis and advice, to automated underwriting. Additionally, integrated computer vision technology automates the underwriting process while detecting fraudulent documents, which drastically reduces the policy underwriting time for the policyholder and the operation costs for the carrier — a benefit for both parties.

See also: Stop Being Scared of Artificial Intelligence

Providing a better customer experience

By creating consumer-specific predictive models, AI helps policy providers enrich their recommendations to both potential and current policyholders, resulting in better synchronization between needs and offers, and superior service across the entire sales chain. 

For example, speech recognition combined with natural language understanding can interpret essential information from customer inquiries in real time. The AI can then provide contextualized and transparent recommendations for both advisers and agents. The result? Advisers and agents can act faster and more accurately, increasing the number of cross-sell opportunities and the win ratio of quotes.

A look ahead

AI will play into several trends that the industry will start to see unfold in the next five years.

Insurance products and service offerings will become more and more complex

As consumers’ needs continue to develop, so will the products and services required to address them. Five years from now, insurers will offer more complex services and, in turn, will need to be able to better explain these offerings to policyholders. This is where AI technology will be vital. AI will help guide advisers, agents or self-care portals in recommending the most relevant products for each individual. We will also see more embedded insurance offerings, with AI helping to pick and pair the consumer’s best options.

Insurance companies will feel the threat of Big Tech

In five years, the insurance business will be even more intermediated through digital platforms and marketplaces. The list of examples is already growing — Airbnb offers renter insurance, Amazon is offering delivery guarantees, Booking.com proposes travelers’ insurance. 

As insurers continue to compete with Big Tech, they need to match the competition’s standards by offering immediate, simple and adaptive policies with AI. Without full process automation on key distribution activities, traditional insurers will struggle to exist in this tech-focused ecosystem and will be challenged by full-digital players.

The industries where the competition is stronger and the insurers are more primed for innovation include personal lines of insurance such as auto, property and casualty and health insurance. In the future, we will need to see these industries take off with artificial intelligence to stay in the game.

See also: Pressure to Innovate Shifts Priorities

At Zelros, we believe that AI-enabled solutions will empower insurance players to keep up with the rising expectations of their customers. AI will give them the acceleration needed to have the real-time experience that everyone now expects when engaging with a brand.

While insurance has been steadily improving communications with customers through gradual adoption of chatbots, Microsoft just put another big item on the industry's technology to-do list: speech recognition.

Microsoft's announcement on Monday that it is buying speech-recognition firm Nuance for $16 billion means that insurers will have to confront the technology -- likely sooner than they had expected. Big Tech has already been getting consumers accustomed to having their speech understood by devices, mostly via Siri and Alexa, and the Microsoft purchase of Nuance will push speech recognition into many business transactions. All industries, including insurance, will have to react as Big Tech again raises the bar for what constitutes a reasonable customer experience.

So, it's worth spending a minute thinking about what speech recognition will -- and won't -- change in insurance.

My bet, having followed the development of a host of fundamental changes in technology for decades now, is that speech recognition mostly will mean the end of the sorts of decision trees that customers now have to go through to get to the right spot in a call center or a corporation.

At the moment, such automated answering systems generally ask callers to respond to a series of options by saying a number or pressing a key. The systems may then ask callers to repeat the process, maybe even multiple times, as a decision tree gradually narrows down the options and determines where to direct the call.

With a system based on speech recognition, customers will simply begin a conversation by saying something like, "I'm calling to check on a payment," or, "I'd like to check on the status of my claim." The artificial intelligence may be able to respond immediately, if it can match the caller's phone number with the appropriate records. If not, the AI can then ask a question or two and respond to simple questions on its own or transfer the call to the right human representative for a more extended conversation.

If a caller wants to speak Spanish, he'll just start talking in Spanish rather than having to oprima numero dos.

Doing away with these automated menus won't materially change any caller's life, but they are enough of an annoyance that insurers and big agencies will need to get rid of them as soon as speech recognition allows. As the world continues to move toward self-service, the industry will need to keep expanding the capabilities of the speech-recognition systems to handle more complex queries and more extended conversations -- along the lines of the progression occurring with chatbots.

The change to speech recognition will be a heavy lift. It not only requires mastering the speech recognition technology but tying it into back-end computer systems and integrating voice queries with customer interactions via text message and via the website or app. Training and staffing of agents will need to change, too.

The shift won't have to happen right away. Nuance (which developed the initial speech-recognition technology for Siri) has a heavy focus on healthcare, so Microsoft won't immediately be raising customer expectations across all industries. But the change to speech recognition will take long enough and be disruptive enough that insurance companies should develop road maps soon.

Now, I've seen some project even more sweeping changes because of speech recognition, but the claims are overwrought. Yes, speaking is often more convenient than typing, but speech has its limitations. If I'm traveling alone and looking for a hotel or a place to eat, I might ask Siri to give me some options, but I'm going to pull off to the side of the road to scroll through them and investigate. And if I'm going to need to read about such relatively simple options, imagine how much more important reading is for all but the simplest queries related to insurance.

Speech won't become the primary interface for the internet any time soon, despite what some have written and despite great improvement in the technology.

But speech recognition still marks a significant change, and Big Tech is once again setting rules for customer experience that the rest of us will have to abide by.

Stay safe.

Paul

P.S. Here are the six articles I'd like to highlight from the past week:

The Future of AI in Insurance

Organizations hoping to deploy artificial intelligence have to know what problems they’re solving — no vague questions allowed.

10 Ways to Prepare for the Hard Market

In soft markets, differentiation can be challenging. But hard markets present an opportunity for the best insurance professionals to stand apart.

How to Deliver the ROI From AI

A technology has emerged that can harness AI across all departments of a business like never before. It's called a feature store.

Benchmarks, Analytics Post-COVID

The pandemic introduced several variables that question the validity of actuarial models and benchmarks.

The Key to the Future of Mobility

Telematics can help solve some of the insurance industry's oldest problems, but, first, insurers must win the client's trust.

Time to Start Over on Secondary Towing

The current system for secondary towing is excruciating. The only reasonable solution is to start over from scratch.

Immediately following an accident, undrivable cars are typically towed away from the scene to temporary holding locations such as an impound lot or storage facility. There they remain until an insurance adjuster evaluates the vehicle and declares it a total loss or requests to have it towed to another facility to be repaired. This process could take several days, and, each day, storage costs are adding up. Moving the vehicle to a repair facility — whether a DRP (direct repair program) facility or somewhere else — is referred to as a "secondary tow," and, as any claims adjuster in the auto insurance business will tell you, the secondary tow process is ugly. The current system is outdated, chaotic and excruciatingly inefficient. In fact, it’s so awful that the only reasonable solution is to rethink the secondary tow process altogether and start over from scratch. 

Let’s take a look at the process as it stands today. Following the accident, the claims adjuster often has no idea where the damaged vehicle is for several days. The tow was likely called for by local authorities on the scene, and the tow operator may bring it to any local lot. Sometimes, the carrier has to rely on the motorist to learn of the vehicle’s location.

Because there’s no standard procedure once the location is identified, the adjuster must determine how to get the vehicle released. Every lot is different, and, with adjusters working hundreds of simultaneous claims, it takes quite a bit of time to determine what signatures are required, what information is needed for forms, how to transfer money and so on for each one. Each claim requires a lot of phone calls and extensive paperwork to resolve.

The adjuster then has to order a tow to transport the vehicle to a repair facility or a salvage location, if it’s totaled. By and large, tow providers don’t like doing secondary tows, so finding a willing provider may take some time, and, even then, the provider is not likely to prioritize the job, which will cause additional delays. 

Tow Operators — Caught in the Middle

It’s hard to blame tow providers for their reluctance to perform secondary tows. They often find themselves completing a lot of paperwork and have to pay for fees out of pocket. Reimbursement for the fees and payment for the job can take 30 days or more, and, even worse, the provider often doesn’t know how much the network will pay until the check arrives.

Typically, it takes about three days to get a vehicle released from a tow yard, and, throughout the entire process, the adjuster has zero visibility into the status of the vehicle. Most of the time, an adjuster will only know where a vehicle is when the repair facility notifies the adjuster that they’ve received it. All the while, the insurer is racking up storage fees and rental car costs. If the motorist calls for an update on the claim and the vehicle, the insurer has no information to provide.

Requirements for a New Secondary Tow Process

The industry needs a new, transparent system for secondary tows, because the current one benefits no one, including the impound facilities. After all, they want to move vehicles through their lot and get paid for storage services. The longer that vehicles remain in the lot, on average, the harder it is to identify vehicles that will never be picked up, which is a poor return for the business. Certainly, the impound facility can send abandoned vehicles for salvage, but the facility rarely recoups costs.

Here’s what a new, more efficient and transparent secondary tow process needs to do:

• Ensure tow providers are paid fairly and quickly for the secondary tow: If tow providers know they’ll be paid a reasonable rate within 24 hours after the job is done, they’ll take these jobs and complete them quickly. 
• Provide adjusters the transparency they need: Adjusters need regular updates on the status of the vehicle to optimize their workflow and to provide the vehicle owner with updates. Receiving real-time updates from an online dashboard is preferred.
• Create a more standardized process for vehicle release: This is a longer-term goal, as this industry is highly fragmented, with many “mom and pop” operations. Nevertheless, the industry needs to organize around some standard procedures for vehicle release to speed the process and reduce confusion. Yard owners will get paid faster if adjusters know in advance the information required and the basic outline of the process they’ll need to follow. And motorists will get their vehicles back faster if it takes less time to release it from the yard and transport it to a repair facility. Everyone benefits.

See also: Transforming Auto Claims Appraisals

Much of this new process can be accomplished through digital technologies. By automating the process of authorizing and paying for a vehicle’s release, tow operators can focus on the task they do best: transporting vehicles. Mobile technologies can make it simple for tow operators to keep adjusters informed, often without having to do anything beyond their regular transport tasks. Systems exist today that can send alerts when the tow operator picks up and drops off the vehicle. GPS can even track the vehicle as it’s towed to the repair yard.

Secondary towing is broken, but it can be fixed. With the application of new technologies and the will for all the players to benefit together, it is possible for the industry to build a secondary tow process that works for insurers, tow companies, yard owners and motorists alike.

Insurers can help clients protect themselves - but preventive approaches aren't yet widely implemented, leaving the door open for unscrupulous hackers

Ransomware and business email compromise (BEC) attacks are soaring, and ransom demands have gone from an average of $10,000 to well north of $100,000 – demands sometimes reach the tens of millions of dollars. In this interview, we discuss what is causing the surge – and what businesses can do to protect themselves. 

This webinar will discuss:

Presenters:

Michael Palotay

Chief Underwriting Officer
Tokio Marine HCC - Cyber & Professional Lines Group

Michael Palotay started his career at AIG in 2006 as a Tech E&O and Cyber Liability Underwriter.  In 2009, he joined NAS Insurance to lead their new Tech/Cyber underwriting facility. Over the next 10 years, his team grew to over 36 underwriters, writing over $130M in premium and consistently delivered impressive underwriting profitability.  When Tokio Marine HCC acquired NAS Insurance, Michael was the Chief Underwriting Officer, focusing on maximizing underwriting profitability, product development and overall business development.  He has continued in this role within the Cyber & Professional Lines group at Tokio Marine HCC.

Paul Carroll

Editor-in-Chief
Insurance Thought Leadership

Paul is the co-author of “The New Killer Apps: How Large Companies Can Out-Innovate Start-Ups” and “Billion Dollar Lessons: What You Can Learn From the Most Inexcusable Business Failures of the Last 25 Years” and the author of “Big Blues: The Unmaking of IBM”, a major best-seller published in 1993. Paul spent 17 years at the Wall Street Journal as an editor and reporter. The paper nominated him twice for Pulitzer Prizes. In 1996, he founded Context, a thought-leadership magazine on the strategic importance of information technology that was a finalist for the National Magazine Award for General Excellence. He is a co-founder of the Devil’s Advocate Group consulting firm.

AI, machine learning, deep learning, natural language understanding, robotic process automation, intelligent process automation: Insurance has a lot of FUN (Frequently Used Neologisms).

The more terms are created and used, the less their meaning is clear, and this is exactly what is happening around AI. 

AI, or, better, machine intelligence, is a set of different technologies and techniques aimed at mimicking human behaviors. Not all are suitable to automate and optimize knowledge-based process such as claims or underwriting, where there are a lot of dense and complicated documents such as medical reports. So, how to scale and automate knowledge-based processes?

To define where to start, please consider:

• Strategic Plan: Each and every AI initiative must be 100% aligned with the company strategy. All initiatives should be part of an internal ecosystem, to share knowledge and lessons learned and to redefine priorities.
• Quick Wins: Begin where the use cases are not too complicated. Quick wins will fuel new wins and initiatives.
• Measure, Measure and Measure: Make sure there is clarity about criteria for success.
• The Right Skills: Pay attention to the teams and the right balance between outsourcing and internal staff, strengths and weaknesses. This is never a pure build-or-buy journey, but rather build-and-buy, where you can learn a lot from trusted advisers and providers.
• Do, Learn and Adapt: Agility and flexibility are extremely important when it comes to projects involving technology and innovation. Mitigate the risk by understanding immediately when it’s time to adapt.
• Organizational Impact: Do not underestimate the impact on the organization and the indirect variables that can influence the outcome of any initiative.
• Internal and External Visibility: Make sure the initiative is well-represented both internally and externally. Doing so you will help attract the right people.

See also: Crucial Technologies for P&C During COVID

Technology adoption is no longer a choice. It is a must-have. Carriers that will not act immediately will be left behind, victims of the AI divide.

When we think about how to prepare for a hard market, we immediately think of our end customers – the policyholders paying the premiums. While it’s true that we need to prepare them, the first step in doing so involves comprehensive training of our teams. It’s easy to forget that many of our employees have never lived through a hard market. They don’t know just how painful it’s going to be … for them and their customers.

Getting employees prepared and their messaging fully on point is like training for a battle. It requires conditioning, endurance and a tough dose of reality. Here are my top 10 tips:

1. Name the Training. When you put a name on something, you place a stake in the ground. People know it matters, and it’s going to involve a process. This isn’t a one-and-done. It counts. Call it whatever you want – Hard Market Bootcamp, Rate & Capacity Awareness or 2021 Reality Check – just give it a title and make it official.
2. Plan a Kickoff. Get everyone in your company on a call and introduce the topic. Show rate trends for your lines of business and have agents give firsthand accounts of situations they’re encountering. If you have veteran team members who were selling in the last hard market, have them tell the team what they remember about the last cycle. Share your historical retention rates and set a retention rate goal for the hard market. Use an infographic like this to paint the picture for your team.
3. Convey the Urgency. When customers are hit with rate increases, they shop, which means a massive amount of shopping is happening right now. How your team navigates the hard market will influence your success for the next decade. You can be the agency that uses it to capture new customers, or you can be the agency that loses customers and scrambles to stay even. Team members’ raises, bonuses and advancement opportunities will be affected by what they do right now. Do they know what’s at stake?
4. Bring the Numbers to Life. Use rate trends to illustrate the average financial impact on your customers. If a commercial auto client paid X to insure their fleet in 2019 and they have weathered two years of rate increases, how much premium will they pay at their 2021 renewal? How much will that affect their bottom line? 
5. Make Scarcity Real. It’s hard to envision something you’ve never experienced. We’ve all grown to expect an on-demand environment, where you buy anything, any time. Team members won’t comprehend that, in some cases, customers will simply not be able to buy the coverage they want and need. Capacity will not exist. Use the very timely and relatable metaphor of COVID to make it real. Just 18 months ago, we could not conceive of a time when you could not go to a restaurant or attend school in person. Lack of insurance capacity is like that. Access will be cut off in some cases.
6. Help Team Members Step Into Customers’ Shoes. It may seem obvious, but empathy is on the decline. Some people on your team won’t intuitively think about the hard market from your customers’ perspectives. Help them imagine it.
7. Give Them the Words. Don’t assume your employees know what to say. Strategically consider the best way to frame your hard market conversations and write down scripts for common scenarios that arise. Make sure everyone on your team is sticking to the script and communicating the same message. Have your account teams practice and role play.
8. Allow Time for Best Practice Sharing. Establish time each week for account teams to share what they encountered, talk about how they overcame challenges and support one another. This is a battle for coverage, and your team must be mentally focused and supported.
9. Challenge Your Team to Set Up Preemptive Communication Processes. The absolute worst thing we can do is surprise our customers. Our job is to help them navigate risk, and a big spike in expenses is a major risk. We need to let them know what to expect and let them know that we’re shopping the market and advocating on their behalf. If they know we’re on their side, they will be less likely to shop. Talk about the hardening market in your blog and in your customer newsletter. Start sending emails and letters six months in advance of renewal, letting clients know what’s happening in the market and what you’re doing about it. Call them 90 days in advance. Spending the extra time up front could raise your renewal rate a few percentage points.
10. Revise Your Game Plan Every Month. Hold a monthly training meeting for your entire team to compare notes, share best practices and review procedures. In every meeting, ask your front-line workers to share their experiences. Give key people notice that they will be asked to speak, so they come prepared. After a couple of months, people will know the drill and it will happen automatically, but you might need to engineer the experience the first couple of times. This should be collaborative and engaging – not a call where you talk and they listen. Track your monthly retention rates and compare them with the same month last year on each call, so your team has metrics to gauge their success.

See also: The Cost of Uncivil Discourse

As an industry, we always say that insurance is not a commodity, but in soft markets differentiation can be challenging. On the other hand, hard markets present an opportunity for the best insurance professionals to stand apart. This is our chance to demonstrate our value, make a difference and earn customers for life. Let’s do this!

Artificial intelligence (AI) and machine learning have come a long way, both in terms of adoption across the broader technology landscape and in the insurance industry specifically. That said, there is still much more territory to cover, helping integral employees like claims adjusters do their jobs better, faster and easier.

Data science is currently being used to uncover insights that claims representatives wouldn’t have found otherwise, which can be extremely valuable. Data science steps in to identify patterns within massive amounts of data that are too large for humans to comprehend on their own; machines can alert users to relevant, actionable insights that improve claim outcomes and facilitate operational efficiency.

Even at this basic level, organizations have to compile clean, complete datasets, which is easier said than done. They must ask sharp questions — questions formulated by knowing what the organization truly, explicitly wants to accomplish with AI and what users of AI systems are trying to find in existing data to get value. This means organizations have to know what problems they’re solving — no vague questions allowed. Additionally, companies must take a good look at the types of data they have access to, the quality of that data and how an AI system might improve it. Expect this process to continue to be refined as companies attain a greater understanding of AI and what it can do.

AI is already being applied to help modernize and automate many claims-related tasks, which to this point have been done largely on paper or scanned PDFs. Data science will push the insurance industry toward better digitization and improved methods of collecting and maintaining data. Insurtech will continue to mature, opening up numerous possibilities on what can be done with data.

Let’s look at some of the ways AI systems will evolve to move the insurance industry forward.

Models Will Undergo Continuous Monitoring to Eliminate Data Bias

AI will continue to advance as people become more attuned to issues of bias and explainability.

Organizations need to develop the means (or hire the right third-party vendor) to conduct continuous monitoring for bias that could creep into an AI system. When data scientists train a model, it can seem like it’s all going very well, but they might not realize the model is picking up on some bad signals, which later becomes a problem. When the environment inevitably changes, that problem gets laid bare. By putting some form of continuous monitoring in place with an idea of what to expect, a system can catch potential problems before they become an issue for customers.

Right now, people are just doing basic QA, but it won’t be long before we see them harness sophisticated tools that let them do more on an end-to-end development cycle. These tools will help data scientists look for bias in models when they’re first developing them, making models more accurate and therefore more valuable over time.

Domain Expertise Will Matter Even More

In creating these monitoring systems, they can become sensitive to disproportionate results. Therefore, organizations must introduce some kind of domain knowledge of what is expected to determine if results are valid based on real experience. A machine is never going to be able to do everything on its own. Organizations will have to say, for example, “We don’t expect many claims to head to litigation based on this type of injury in a particular demographic.” Yes, AI can drill down to that level of specificity. Data scientists will have to be ready to look for cases where things start to go askew. To do that, systems — and even the best off-the-shelf toolkits — have to be adapted to a domain problem.

Data scientists are generally aware of what technology options are available to them. They may not be aware of the myriad factors that go into a claim, however. So, at most companies, the issue becomes: Can the data scientists understand whether the technologies they know and have access to are appropriate for the specific problems they’re trying to solve? Generally, the challenge that organizations face when implementing data science solutions is the difference between what the technology offers and what the organization needs to learn.

Statistical methods, on which all of this is based, have their limitations. That’s why domain knowledge must be applied. I watched a conference presentation recently that perfectly illustrated this issue. The speaker said that if you train a deep learning system on a bunch of text and then you ask it the question, “What color are sheep?” it will tell you that sheep are black. The reason is that, even though we know as humans that most sheep are white, it’s not something we talk about. It is implicit in our knowledge. So, we can’t extract that kind of implicit knowledge from text, at least not without a lot of sophistication. There’s always going to have to be a human in the loop to correct these kinds of life biases to close that gap between what you can learn from data and what we actually know about the world. This happens by inviting domain expertise into the data science creation process.

We’re getting better and better at democratizing access to AI systems, but there will always be an art to implementing them — where the data scientists have to be close to the subject matter experts to understand the underlying data issues, what the outcome is supposed to be and what the motivations are for those outcomes.

Unstructured Data Will Become More Important

There is so much data at insurance companies’ disposal, but we have only tapped into a small percentage — and we’ve yet to cultivate some of the most significant assets. The integration and analysis of unstructured data will enable this to happen as it becomes more accessible.

Case in point: Natural language processing continues to mature. This means that, instead of pulling information from structured fields, like a yes/no surgery flag that could be interpreted pretty quickly by reading claim notes, adjusters could receive a more holistic view of the claim, going beyond the structured data and finding more and more signals that would have otherwise escaped the adjuster’s attention.

Images also provide all types of exciting and insightful unstructured data. The interpretation of scanned documents is a necessary part of claims. Advanced AI systems that can handle unstructured data would be able to read them and incorporate relevant data into outputs for evaluation. Theoretically, even further in the future, adjusters could look at pictures from car accidents to ascertain the next steps and cost estimates.

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Systems that can interpret unstructured data also will be able to extract information in terms of drugs, treatments and comorbidities from medical records. In claim notes, sentiment analysis will seek out patterns from across many claims to identify the ones that yield the most negative interactions with claimants so that early interventions can occur to influence claim outcomes. We are just scratching the surface on unstructured data, but it won’t be long before it makes a profound impact on insurtech.

Feedback Loops Will Improve

Ideally, good machine learning systems involve feedback loops. Human interaction with the machine should always improve the machine’s performance in some way. New situations will perpetually arise, requiring a smooth and unobtrusive way for humans to interact with machines.

For example, claims adjusters may review data outputs and determine that possibly this sentiment wasn’t actually negative, or they might learn that they missed extracting a drug. By letting the machine know what happens on the “real world” side of things, machines learn and improve — and so do claims adjusters! To reach this level and to be able to continually improve data analysis and its applications, undergoing a continuous improvement loop, is where AI will ultimately shine. It empowers adjusters with rich, accurate knowledge, and, with each interaction, the adjuster can inject a bit more “humanness” into the machine for even better results the next time.

Companies are putting systems in place to do that today, but it will still take a while to achieve results in a meaningful way. Not a lot of organizations have reached this level of improvement at scale — except for perhaps the Googles of the world — but progress in the insurance industry is being made each day. AI systems, with increasing human input, are becoming more integral all the time. Within the next five to 10 years, expect AI to transform how claims are settled. It’s a fascinating time, and I for one look forward to this data-rich future!

As first published in Data Science Central.