A client recently asked me to explain the differences between innovation, transformation and improvement and to suggest how they might drive innovation, transformation and improvement in their own business.

Although I’ve helped insurers with all three of these on multiple occasions, I’d never really taken the time to figure out the distinctions, or how the terms fit together. So I did some research and thought a bit and came up with an answer.

I certainly don’t think this is the only way of looking at the issue, but it certainly helped me once I’d figured out my version of the answer. And, having now clarified matters in my own mind, I thought it might be helpful to share.

Definitions

Having hunted around, it seems there are no universally accepted definitions of innovation, transformation or improvement within a business context.

For me, business innovation is the delivery of something new with the intention of improving business outcomes. And that "something" can cover a wide range of territory, including products, services, distribution channels, processes, operating models, technology, and culture – indeed, any aspect of the insurer’s business whatsoever.  

I then find it helpful to distinguish two broad types of business innovation (and I’m indebted to the Digital Insurer’s TDI Academy for this, as it’s a distinction we teach on their ADI Program):

• Business Transformation is the large-scale reinvention of the whole business or a material subset of the business (such as a business unit or function); whereas
• Business Improvement is focused on smaller-scale changes, typically in only one element of the value chain or within a single team. It is more about improving today than inventing tomorrow.

There is no hard-and-fast boundary between the two, but, given the definitions I’ve just offered, the primary differentiators are:

• The scope of the innovation; and
• The scale of the insurer’s ambition.

Approaches to Design and Delivery

If there’s no hard-and-fast boundary between business transformation and business improvement, then why bother to distinguish them at all?

Because, based on my experience of dozens of insurance innovation programs and projects, I believe insurers should use different approaches to the two different types of innovation.

See also: It’s Time for Next Phase of Innovation

Designing and Delivering Business Transformation

I’ve shared my tried-and-tested approach to business transformation before, within the context of digital transformation:

Given the broad scope and ambitious scale of this type of innovation, it’s not surprising that the approach is very much rooted in the needs of the customer and in the business’s overall strategy. Building on these critical foundations, the insurer then needs to:

• Paint a vision for what will be achieved;
• Drill that down to a deliverable design;
• Establish the capabilities required;
• Create a road map to bridge the gaps;
• Deliver what’s on the road map;
• Review achievements, reassessing as needed;
• Wrap the transformation with strong change management; and
• Apply good governance throughout.

Designing and Delivering Business Improvement

So how should the (somewhat less-ambitious, narrower scope) business improvement projects be handled?

As I considered all of the insurance improvement programs and projects I’ve been involved in over the years, I recalled multiple different methodologies that I’ve used at one time or another.

These methodologies typically varied according to the different problems they were trying to solve, or the different opportunities they were looking to pursue.

But as I thought about the different approaches I realized that, despite linguistic differences, they had many characteristics in common. Indeed, it was possible to see all of them as particular flavors of an overall approach that could fruitfully be used for any business improvement project.

For obvious reasons, I call it "5-I."

The 5-I model delivers, and sustains, the desired business improvement in five steps:

• Initiate: Frame the problem to be solved, or the opportunity to be pursued, and launch the project.
• Investigate: Analyze the problem or opportunity to understand it fully, including root causes and implications.
• Ideate: Generate possible solutions or take advantage of the opportunity. Then analyze the alternatives and agree on which one(s) will be taken forward to delivery.
• Implement: Deliver the solution(s) and manage the change(s) to ensure the improvement is embedded and sustainable.
• Inspect: Review what’s been done, asking whether the insurer has solved the problem or is realizing the expected benefits. If not, iterate as needed. Otherwise, close the project.

See also: Adversity Breeds Innovation

Granted, there will be nuances between projects at the next level down, but I’m struggling to come up with a business improvement project this approach doesn’t fit.

 

Traffic volume in the U.S. is often worse during the summer months, with sunny skies and warmer temperatures bringing people out of their homes and into their cars. This year, in particular, as pandemic restrictions ease across the country and Americans feel more comfortable traveling, we’re likely to see traffic volumes surge. In turn, this will mean increased potential for vehicle breakdowns and accidents. Auto insurers would be wise to prepare for a busier than normal season.

What will the surge look like?

While it can be difficult to pinpoint exactly how many vehicles will require assistance over the next few months, we have a few predictions:

• We’re already seeing breakdown volume bounce back from 2020 lows, and it is likely volume will increase beyond 2019 pre-pandemic figures. 
• Rather than growing in a spike that starts – and ends – quickly, summer volume will be consistently high beginning from late June through early September.  
• Fourth of July will represent a significant peak.

See also: Key to Transformation for Auto Claims

How can insurers prepare?

In many cases, roadside breakdowns or accidents are a policyholder’s first – perhaps only – interaction with their insurer. These events therefore constitute a tremendous opportunity for insurers to please and impress their customers. Ensuring these events go as smoothly and efficiently as possible, particularly if a driver is frustrated, worried or nervous, is important. Managing these events can always be complex, and factoring in the scale at which these events will be handled in summer 2021 can make for some additional difficulties. The best way to be prepared is for insurers to optimize processes and systems now: 

1. Offer seamless, self-service communications: Consumers increasingly want easy-to-use digital options to engage service – think requesting a Lyft or ordering pizza on the Dominos app. This interest is extending into breakdowns, disablement and accidents, where help request channels like buttons within a branded mobile app, mobile web pages or web applications can deliver a fast, convenient experience for those who want it (trained agents can also be available via toll-free number or an “out” provided within the digital channel). These capabilities also open up agent time, allowing them to support more complex cases. Mobile web and web app options can be easily stood up so that digital options are ready to go by the time volume really starts to pick up.

2. Consider a digital-first accident management process: Accident management is typically an expensive process, but an early-response digital and cloud solution can improve efficiency and responsiveness. More importantly, digitization can enable insurers to remain nimble and agile, quickly and easily ramping up accident-related services and allowing them to focus more effort on other aspects of the business. When done right, digitization can save insurers millions of dollars in claims loss costs annually. An example would be an accident platform that can provide transparency to all authorized employees, allowing them to conduct self-service activities such as reporting, checking status, entering a new accident claim case, etc. The ability for agents to digitally request an accident tow, either a primary tow from the accident scene or a secondary tow from storage, will also greatly reduce reliance on oftentimes lengthy logistical calls with contact center agents. 

3. Leverage digital photo-enabled claims: The accumulated cost of vehicle storage fees, rental days and a secondary tow following an accident can add up to as much as $1,050. By using images of the vehicle captured at the scene of the accident, claims processers can identify damages earlier. This speeds the decision-making process on whether the disabled vehicle should be sent to a repair facility or a salvage yard, helping to avoiding costs. With the right tools, photos can be easily channeled into an insurer’s method of inspection for expedited appraisal. 

4. Streamline vehicle tow and storage release: If a vehicle involved in an accident is towed to a storage location, negotiating the release can require significant knowledge about local regulations, processes and market rates. As accident volume ramps up, insurers should consider augmentating their current platforms and workforce with a partner that can provide a dedicated team of agents – armed with robust data – that are experienced at identifying opportunities to negotiate and decrease vehicle storage fees. These agents should be able to manage every aspect of the release process, including reducing checkpoint and approval calls, conducting cost-trend analysis, handling payments directly with the storage yard and managing tow-out service. 

See also: Don’t Look Now, but Here Come Autonomous Trucks

A summer driving, breakdown and accident surge is right around the corner. Agero’s recent research shows that the way in which these disablement events are handled has a significant impact on policyholder loyalty. By taking steps now to update, optimize and digitize processes for maximum efficiency, insurers will be prepared to more effectively help their customers through this likely difficult time.

Alarmed by the results of the 2019 employee/manager FMLA Knowledge Gap survey with regard to the lack of understanding of leave regulations and the employee and compliance challenges due to it, we decided to follow up in 2020 in the hopes of seeing a significant improvement. Leave administration may not be a top of mind or a requirement detailed in most managers’ jobs, but nearly 50% of those surveyed indicated they are asked to educate their employees about leave options — specifically Family and Medical Leave Act (FMLA) and state-mandated family and medical leaves.  Additionally, managers are required to notify employees of their eligibility for these specific leave benefits. Easy enough, right?

Say you are a manager for a New York-based employer and have an employee whose father has suffered a stroke. The employee informs you they’ll need to be out of work for at least two weeks while they take care of him. You tell them that, unfortunately, they only have seven days of paid time off remaining this year, so they’ll need to return to work when their PTO is up.

Not so fast. You’ve failed to provide notice requirements to this employee relating to the other leave benefits available to them for taking time off work to care for a parent who has a serious health condition. As a New York state employee of a company with more than 50 employees, they’re entitled to job protection under the FMLA. Additionally, they are entitled to apply for New York Paid Family Leave that would provide wage replacement for their time out of work. By not advising this employee of their rights and responsibilities under these leave laws, you have inadvertently violated these laws, and as a manager you may be held personally liable.

Remember, you made him come back to work during a critical time in his father’s life. Perhaps he chose not to come back and took unpaid time? Perhaps you issued attendance points? You have now critically violated his FMLA rights.

And this scenario is just for one state. Each state has its own regulations, with state paid family leave becomingly increasingly common but, of course, still not available to employees in most states. With so many companies now moving to either permanent or hybrid models of work from home, employees can live and work from anywhere, complicating how you manage leaves of absence. 

An Alarming Knowledge Gap Among Managers

For the second straight year, we surveyed front-line managers to better understand how well they grasp the federal Family and Medical Leave Act (FMLA) as well as state paid family leave (PFL), provided they live in one of the states currently offering PFL. Though we slightly tweaked some of our questions from our initial research in 2019 — limiting the direct comparisons from 2020 to 2019 — the numbers continued to paint a bleak picture for managers’ understanding, creating operational and business risk for them and the company.

To be deemed “knowledgeable,” managers were required to answer our three questions on FMLA correctly. Of the 435 managers surveyed, just 11% answered all three basic questions correctly, reflecting a significant knowledge gap!

For PFL, we asked two questions to test managers’ knowledge, and just 17% of these managers were able to correctly identify the benefits eligible under PFL, including whether they live in a state currently offering PFL.

It would be one thing if these managers weren’t tasked with any responsibility with leave administration at their company, but that’s not the case. The graphic below shows that nearly 50% of managers said they are responsible for educating their employees on these leave benefits and notifying them of their eligibility.

Figure 1: Managers’ responsibilities in leave administration at their employer

Considering that 30% of managers in the study thought that FMLA provided insurance coverage to pay medical bills while on leave and 32% indicated FMLA provided replacement of lost salary/wage, it is easy to be concerned about having these managers responsible for educating employees on these leave options and benefits. Adding to the complexity and risk for the company is that having one of your managers treat their direct reports differently than another relating to the FMLA is also an FMLA violation. If one manager thinks FMLA can be taken for a family vacation, and another doesn’t, you have a significant compliance issue!

See also: The Key to Agency Management Systems

Figure 2: Employees’ and managers’ responses to the benefits provided by FMLA. Note: Respondents could select multiple answers.

We speculated after 2019’s report — in which we did not ask questions about whether managers had received FMLA training — that a lack of training was a major factor in the knowledge gap. Yet, after collecting training data from respondents in 2020, we found the knowledge gap among managers was virtually the same, regardless of whether the manager had formal training.

While this is yet another red flag, we did identify some benefits from training. Managers who had received FMLA training were significantly more likely to agree that as managers of employees, they could be sued for FMLA and ADA violations. They were also significantly more likely to agree they are responsible for helping with compliance at their organization.

What Role Does Technology Play in Educating Managers and Employees?

Customer portals are becoming commonplace for large employers as well as insurance companies — recent Majesco research found that insurers are heavily focused on customer self-service capabilities and portals, with 41% to 61% of companies saying they are implementing or have already implemented these.

While these portals can be used for a multitude of reasons, they can be a tremendous resource for employees and managers when it comes to tracking and reporting absences. Our research found that self-service company portals are employees’ preferred method of obtaining answers to the number of days they’ve been absent and the days they have remaining. But when it comes to more detailed questions, such as the type of state of federally offered leaves available, employees are more apt to seek out this answer by contacting their HR department.

Figure 3: Where do employees go for their leave questions?

The benefits of an effective and informative self-service portal are substantial. Having a devoted platform where employees and managers can access this information 24/7 could play a pivotal role in ensuring both employees and managers are well-educated in the state and federal leaves available to them. Paired with software that can automatically determine an employee’s eligibility for these leaves based on built-in regulations is the type of error-proof compliance solution needed in the market. Hence the rising demand in absence and accommodation management software that employers can use or insurance companies can provide as a “value added service” to their employer customers. 

Insurance companies that are providing this offering, or are enhancing their portal, are well-positioned to be a go-to solution for helping employers mitigate their compliance risk and improve their leave management offering.

How Should Employers Address This Knowledge Gap?

As you might expect, there’s no silver bullet for solving this knowledge gap. For some companies, the gap may not be as substantial. And the approach to address this gap for one employer may not work for another. Take employer size and location. An employer with more than 10,000 employees that has offices all across the U.S. is going to have a much more complicated workforce than a 200-person company operating in a single state will. And, as noted, the rising acceptance of work from home will accelerate this complexity. 

Balancing multiple state paid family leaves alongside other local, state and federal leave laws, and any corporate leaves, is a lot for anyone whose full-time job is absence management. While no one is expecting these managers to have the same knowledge as those individuals, it probably doesn’t come as a surprise that managers who have a substantial list of their own day-to-day duties are struggling to correctly identify FMLA and PFL details. Simply allowing these managers to continue to play a role in leave administration without proper education isn’t the answer, though.

While our study found the existing training structure isn’t effective for educating managers, we heard through interviews with them that these training sessions were infrequent and covered a wide range of topics — they weren’t specific to leaves such as FMLA. Evaluating whether the existing training at your company is focused enough and effective with your staff is a great starting point.

For some employers, though, part of the answer may be to look to an insurance carrier or third-party administrator for a new “value added” service. Although it doesn’t absolve your managers of responsibility, outsourcing leave administration to insurers or TPAs is becoming increasingly common among employers. Insurance companies and TPAs have the resources and technology to optimize the business process and ensure compliance with these laws.

For a complete picture of the FMLA and PFL Knowledge Gap in 2020, you can download the report here.

Don’t Look Now, but Here Come Autonomous Trucks Paul Carroll, Editor-in-Chief of ITL While the focus for years has been on autonomous cars and on what they’ll do for safety, for auto insurance, for our lifestyles and more, a disruption is taking shape in the nearer term: autonomous trucks. The fear factor has obscured that vision. While it is odd enough to drive down a street in Phoenix and see a Waymo minivan next to you without a driver, it’s hard to imagine anyone setting loose on a highway an 18-wheeler carrying 50,000 pounds without anyone at the wheel. But we’re close... continue reading > DIGITAL INSURANCE: THE INFLECTION POINT New Majesco research highlights how digital is shaping today’s competitive landscape, why insurers are prioritizing digital capabilities and how a 360 view of customers can create growth opportunities. Download Now SIX THINGS The Finish Line Keeps Moving by Mark Breading Improving customer experience in P&C was already a marathon--now, the pandemic and other events have changed the race in significant ways. Read More 4 Questions That Scare Salespeople by Kevin Trokey I can appreciate the job's difficulty. But I swear, at times, it seems like salespeople are intentionally making their job harder. Read More Elevating the Capability of Employees with AI based Fraud Detection Delivers Significant Financial Results sponsored by Daisy Intelligence AI done right will deliver significant cost savings in claims operations, satisfy customers and make the difficult job of fraud detection and claims processing easier.  Read More Life Insurance With Mortgage Protection by Jason Mandel Life insurance with mortgage protection allows families to shelter at home—to stay in their homes—rather than sheltering in place. Read More Building Healthy Workplaces by RJ Frasca Emerging HR technologies can attract and retain motivated talent, bolster culture, decrease spending and improve business operations. Read More What SPACS Mean for D&O Exposure by Jason Remsen Although many of the risk exposures remain the same as those in traditional M&A deals, lack of historical data has fueled uncertainty. Read More Vaping, Compliance and Rewarding Safety by Michael Shaw If insurers care about improving the safety of vaping, they should insist on the wholesale adoption of existing rules by the vaping industry. Read More MORE FROM ITL The Alarming Surge in Ransomware Attacks 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.  Watch Now Partner with ITL to create expert thought leadership content. Custom Content Promoted Content Display Advertising Custom Webinars Monthly Topic Sponsorships ITL Partner Packages and more Learn more and get the 2021 Media Kit GET INVOLVED Write for Us Our authors are what set Insurance Thought Leadership apart. Get Started SPREAD THE WORD Share Share Tweet Forward SUBSCRIBE TO SIX THINGS

The word "transformation" is overused, and yet here in the auto insurance claims industry there is no better word for a process that is being changed so dramatically from beginning to end, and at every step in between. 

But real transformation, while claimed by many, is in reality only enabled by the exceptional few. That is because transformation occurs through a collective, inclusive effort, not a silver bullet technology. And complete transformation requires the active participation of the end-user, to ensure higher levels of acceptance and satisfaction. Transformation must be good for the business and the customer, or it will likely not take hold at all. 

Foundational to Success

Digital transformation is the essential driver behind how companies will add value and deliver services to their modern customer, a customer who expects and trusts digital interactions. AI is critical to processing and assessing all inputs and removing friction. Yet AI alone cannot deliver transformation. 

Let me explain.

We know data availability is increasing rapidly across multiple dimensions – volume, velocity and variety. In the last two years, more data was created than in the entirety of human history. This is a fraction of the data that will be available in the near future as connections continue to multiply, becoming increasingly bi-directional and informing virtually everything. Right now, there are more than 50 billion connected devices in the world, and connected cars are emerging as an important digital platform.

Artificial intelligence is the only way businesses can leverage the tremendous amounts of data available. AI synthesizes all of this data. This is positive and necessary. But AI output is often delivered to humans, reviewed offline and paused before actions occur. Companies have to eliminate this pause and disconnect in the process to transform their operations. AI decision-making must be digitally connected to operating systems or consumer interfaces or both to drive action and to create a truly elevated, digital experience.  

See also: Transforming Auto Claims Appraisals

Relevant mobile technologies, network connection management and industry-specific workflow applications are required to activate AI, automating tasks based on that data to speed up and simplify lengthy and complex processes. The auto insurance claims process is an ideal candidate for such transformation. Our industry needs to connect AI to technologies that drive action. 

Here’s an example of how a transformed auto claim experience can look to your policyholder when AI gets put into action with mobile and network technologies:

• Pat enters his vehicle in the morning. The app on his phone activates and begins tracking his trip so that his auto insurance policy premium is calculated for only the time he is in transit, based on the policy he selected upon enrolling. 
• On arrival at his employer’s office parking lot, Pat accidentally scrapes the side of his vehicle on a pillar. 
• His vehicle and app automatically detect the incident and offer Pat the opportunity to submit the incident to his insurer to determine if a physical damage claim should be opened.
• Pat decides to proceed and immediately receives a text link with instructions about how to take a few smartphone images of the damaged area and text them to his carrier. 
• Pat is immediately notified by text that the damage is minor and that the car can be safely driven but that the repair cost likely exceeds his policy deductible by at least $500. 
• Pat decides to file the claim and receives a text with a list of nearby repair facilities, including consumer ratings, shop certifications or specialties and availability. 
• He taps a few links and schedules the repair, and once he arrives a pre-arranged temporary rental car will be waiting for him. 
• Pat continues to receive status updates from his insurer until he is advised what time his vehicle will be ready for pickup or delivery, as preferred.  

Note that the steps described above begin and continue with AI-enabled decision-making and workflow management. Out of view of the policyholder, AI and digital connections are powering the parts ordering process, and the repair facility is digitally paid by the insurer within hours of the vehicle being delivered. Without these enabled technologies, a digital end-to-end experience would not be possible. But when combined with the other elements, the result is transformative, completely digital.  

Sourcing the Data That Powers AI and Drives Decisions

The relationship between the ability to reliably predict outcomes and the absolute volume of historical claims data leveraged to train the software is directly proportionate – the greater the amount of relevant data used, the better the outcome. We frequently hear from our insurance clients of all sizes that the volume of data needed to develop reliable algorithms is greater than even the largest insurers have available. CCC has processed more than $1 trillion of claims-related data, which we put to work to develop hundreds of actionable AI models. And while data relevancy is essential, another key difference in AI efficacy is the use of a combination of AI disciplines. Deep and machine learning and business rules combine to deliver the most reliably predictive, comprehensive results for faster, smarter resolutions. 

Here’s how: 

Deep learning is an AI method that uses historical data to inform which action is likely to lead to which outcome. Let’s take photo-estimating as an example. To train an AI model that can review smartphone images from a collision and predict whether a vehicle is repairable versus a total loss, the AI model needs to learn from historical data: photos of other car crashes, as well as the claims data that accompanies those photos regarding the parts, labor, cycle time and medical assistance needed for each claim. The question is: Does the AI model have enough historical data to make that prediction actionable? A few hundred images are helpful, but decades’ worth of wrecked car images and related metrics make the AI model far smarter. Another question: Can the AI model sort out the anomalies from the requisite data set? Can it learn from them?

Another key discipline is machine learning, which allows historical data to be influenced by behavioral or pattern changes that might make recent actions more likely to occur again. Let’s say you have been a Facebook visitor every day for the last five years, but more recently you’re only visiting Instagram. In this case, the majority of data would say you’re going to visit Facebook again, but recent activity would suggest Instagram is a better prediction. Why does this matter? Vehicles and parts are not static. New cars and parts are introduced continuously; if an AI solution is going to be effective, it needs to base predictions on data that can account for recent behaviors, not just historical data. 

A less sophisticated, yet foundation disciple, includes the use of rules. A rules-based approach can offer helpful predictions when historical data is not available or recent activity is not accurate enough to ensure a reliable prediction. Suppose that an inbound technical support email contains the word "urgent" in the body. A rule is triggered, and that email is forwarded to someone who can immediately act on it. These types of rules can get into extremely complex decision points, leading to hundreds of potential rules, some of which may even conflict with each other. This is why rules-based AI is an incomplete approach that can fall short in accuracy and reliability. Yet, because data and domain experience aren’t required to create rules-based AI, it is a helpful starting point that can assist companies to begin the journey of automating complex workflows such as auto insurance claims.  

See also: Auto Claims: Future May Belong to Bots

When It All Comes Together – A Reimagined Insurance Experience

When the claims experience is working in harmony as a result of automated, AI-enabled decisions and all the needed inter-company, inter-industry integrations, not only will the insurer’s customer’s experience be maximized but real hyper-personalization can be achieved, meaning that each insurer’s individual customer will enjoy an exemplary service experience in the manner and method that they expect and prefer. 

Industry transforming technology is here and ready to be combined in time to meet consumers’ evolving expectations. Insurers are in a position to connect AI, mobile and network to transform what’s possible.

JUNE 2021 FOCUS OF THE MONTH
Workers' Compensation

 

FROM THE EDITOR

 

The world of work turned upside-down and inside-out beginning 15 months ago, as the pandemic shut down offices and forced so very many of us to work from home. That transition worked far better than expected -- but now we're beginning to reverse it as the pandemic recedes in many countries. Will the move back into offices work as well, including for those that insure workers?

The transition could well be more complicated this time. The shutdown stressed schedules and technology capabilities but at least was clear-cut: Everybody had to stay home for those first weeks. Over time, the situation became more complicated based on federal guidelines and those from the individual states, and workers deemed "essential" wound up being much more exposed to the virus than the rest of us. But, now, every company will pretty much make up its own rules, and each employee will react and adapt in his or her own way.

Some employees will return to the office full-time, some occasionally. Some won't ever have to leave home. Business travel will rebound. But how much? Many companies will give up office space. Many others will reconfigure what they have. Factories and other non-office settings may change less, but many will still adapt.

And insurers will have to figure out what rates to charge in these new environments, without benefit of the normal historical data. Insurers will also have to sort through all sorts of new issues. Here's one: When is the place where a worker works a "workplace," and when is it not?

Welcome to the new world of workers' comp.

- Paul Carroll, ITL's Editor-in-Chief

 

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WHAT TO WATCH

A Conversation on Workers' Comp, with Kimberly George and Mark Walls We sat down with two of ITL's thought leaders, Kimberly George and Mark Walls, to explore the new world of workers' comp. Watch Now

Optimizing Care with AI in Workers Comp Claims In workers’ compensation, we’ve all seen seemingly basic claims morph into catastrophic claims. Artificial intelligence and machine learning have held out some hope of heading off problems, but the industry has been cautious about exploring these possibilities. This webinar, sponsored by CLARA analytics, lays out a tangible solution that realizes the promise of AI. Watch Now

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WHAT TO READ

	Workers Comp Trends for Technology in 2021 An efficient workflow passes 60% to 70% of medical bills straight through; workers' comp has a long way to go. Read More
	How Social Inflation Affects Liability Costs The industry is probably looking at several more years of accident year combined ratios above 100%. Read More
	How AI Can Tackle Claims Staffing Gap A job description with “acquire AI superpowers” might appeal to millennials more than “study policy footnotes and calculate claim reserves.” Read More
	Case Study on Using AI in Workers’ Comp Taking in extra data points and thinking in a different way has let us make better decisions about how to route claims, and more. Read More
	Commercial Claim’s Journey With AI AI is still very new to insurance, and claims teams are only scratching the surface on how it can be applied for the betterment of all constituents. Read More
	COVID: Chance to Rethink Work Comp As insurers worry that the pandemic is depressing premiums, here is a way to rethink workers' comp -- and two entirely new product ideas. Read More

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WHO TO KNOW

Get to know this month's FOCUS article authors:

Thomas Ash	David Bacon	Karlyn Carnahan
Kimberly George	Shahin Hatamian	Ji Li
Rebecca Morgan	Mark Walls

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Learn More about ITL Focus

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Interested in sponsoring ITL Focus or learning about other promotional opportunities? Contact us

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Have you ever entered a store when the automatic door wasn’t working? Or, have you ever come to an escalator that is simply stopped, and you have to walk up or down? It feels strange when common automations don’t work. If you are like me, you might even experience that disoriented, woozy feeling when your mind thinks it is stepping onto something that is supposed to move.

People are wired to gravitate toward ease of use. They naturally exhibit streamlined behaviors, like making shortcuts in the grass between two sidewalks, and they quickly adopt automations that will make life easier. Just look at how rapidly we adopted smart phones.

How must insurers, right now, prepare for an even more automated and streamlined future, with the shortcuts that people will create, whether we want them or not?

Streamlining and innovation happens with partnerships.

The restaurant industry offers an example, as it adapted to the radical changes in dining behavior that the pandemic forced. Daddy’s Chicken Shack, for example, in Old Pasadena, California, is using facial recognition kiosks to provide a completely touchless payment and service experience. Kiosks were already on the rise as a sort of automation that could reduce lines and front-line work for restaurants such as McDonald’s that struggled to find workers. Kiosks may also improve sales and customer engagement. But the facial ID features from PopID took the kiosk from “data entry point” to “data security and touchless payment,” while streamlining the process for customers at the same time that it was keeping them safe.

Automated payments and touchless technology are the next escalators of our time. But they require effective partnering. It takes more than a kiosk to automate a payment. In this case, it takes the facial recognition technology vendor (PopID), a card reading technology (Ingenico) and a payments hub vendor (Datacap), plus a dozen more payment-related companies that help to create an omni-channel payment ecosystem. This is not only streamlining through partnerships but innovating, as well.

See also: Insurance Outlook for 2021

Streamlined partnerships rely on connections

No process can become automated without connections. Innovative organizations will find the transactional hurdles and prepare their systems to accept two-way connections, then use those connections to make life easier for the customer.   

Google Maps, for example, can now connect commuters with automated parking location and payment via two integrated apps, Passport and ParkMobile. Google Waze is testing touchless fuel payment at Exxon and Shell gas stations.

Connections are made more efficient through ecosystems

Insurance may not fit cleanly into every retail experience, but it is absolutely ripe for fitting into customers’ lives and businesses. The key for insurers is to think beyond a single transaction and be “partnership-ready,” which also means becoming “ecosystem-ready” -- whether for mobility (well beyond auto insurance) or for a combined life, health, wealth and wellness experience. Companies that can achieve an early entry into this space have a tremendous opportunity to create and grow a loyal base of customers.

Given the nature of ecosystems, insurers can assume multiple roles, from owner of the unifying platform, to orchestrator of the products and services or provider of products and services. What insurers achieve will depend on their ability to enter the market while it is still an uncrowded “white space.” Of course, moving early requires leadership with an appetite for taking informed risk, ability to move quickly and capacity to build partnerships within and outside of insurance.

Insurers also need strong technology capabilities, including next-generation solutions that are cloud-native, digital-first and ecosystem-ready. The solutions need to break the software down into thousands of consumable application programming interfaces (APIs), offering ready-to-use insurance apps as well as a network of third-party plug-and-play services and apps.

In my blog last year on ecosystems and engagement, we identified three key ecosystems that every insurer needs to consider: the mobility ecosystem, the lifestyle ecosystem and the financial ecosystem. And more are emerging all the time.

Insurance already touches life in so many moments of the day, but how should insurers look at their future capabilities in light of fitting into these key ecosystems? Where can insurers place new products that fit seamlessly into these life streams, and how can they develop and maintain a framework that allows for quick reactions to customer trends?

Insurers will find, as they prepare, that every connection they create makes the organization a little more resilient. Channels will grow organically. Products will be launched with less delay. Customers will be served in automated ways that suit them, with relevance and accuracy. Learning, through greater data exchange, will yield improved experiences in claims-related risk.

But insurers must prepare. Humans are wired to use automation and streamline experiences. So, how can we further automate payments? How can we automatically begin providing insurance when people walk or drive or fly into situations where they need insurance? Streamlining and innovation are made possible through partnerships. Partnerships are enabled by ecosystem-ready insurance processes.

An innovation strategy around big data and artificial intelligence will uncover insights that allow smart carriers to acquire the most profitable clients and avoid the worst. Companies that develop the best portfolios of risks will ultimately enjoy a flight to quality while those left behind will compete for the scraps of insurability.

Insurers are also trying to individualize underwriting rather than use the traditional underwriting of risk categories.

As such, the insurance industry finds itself in a data arms race. Insurance carriers are leveraging their datasets and engaging with insurtechs that can help.

For the underwriter, big data analytics promise the ability to make better decisions with respect to risk selection and pricing. Underwriters have thought too many times that if they had just understood a particular area of risk better they would have charged a lower price and won the business; or had they had that little extra piece of information they would not have written an account that turned out to be unprofitable. Most certainly, underwriters would assert that with better information they would have charged a more appropriate price for a risk and most definitely would not have lost money.

One solution has been developing predictive underwriting risk selection and pricing models. By leveraging datasets previously unavailable, or in formats too unstructured to use, algorithmic models can better categorize and rank risks, allowing an underwriter to select and assign the most appropriate price that rewards better risks and surcharges those that are riskier. Better risks might be those that are simply less inherently risky than others (e.g., a widget manufacturer vs. an explosives manufacturer with respect to product liability or property coverage), or those whose behaviors and actions are more cautious. Through a predictive, data-driven
model, underwriters will be able to build profitable and sustainable portfolios of risks, allowing them to expand their writings to a broader customer base, pass along cost savings from automation to their clients, provide insights into means by which their insureds can reduce risk or identify new areas of coverage and product and bring more value to customers.

With this win-win situation at hand, the insurance industry has charged forward in data mining the decade’s worth of their own internal information, as well as accessing public databases, leveraging data brokers and partnering with insurtechs that have their own data lakes they can access. Algorithmic models then are being fine-tuned by actuaries, statisticians and behaviorists to find causation links and correlations between seemingly disparate data points with the intention of divining future loss outcomes. In this digital frenzy, what gets lost, however, is that there can be social costs from the methods by which all this data is used.

See also: 11 Keys to Predictive Analytics in 2021

Balancing Social Good With Social Cost

It is not false altruism to reward good risks, build resiliency in portfolios or discover insights that lead to new products and services. However, underwriters must recognize that they are inherently in the business of bias. While it is acceptable to be discerning between a safe driver and reckless one, it is unacceptable to build into underwriting decision a bias toward race and religion and many times gender or health conditions. It is therefore essential that underwriters, and the actuaries and data scientists who support them, act responsibly and be accountable for any social failures of the algorithmic models they employ.

With our predictive risk selection model in mind, consider some of the available data that could be processed:

--decades of workers' compensation claims data

--policyholder names, addresses and other personally identifiable information (PII)

--DMV records

--Credit scores and reports

--Social media posts

--Telematics

--Wearable tech data

--Biometric data

--Genetic and genealogy information

--Credit card and purchasing history

Consult algorithmic accountability experts like law professor Frank Pasquale, and they will provide you with additional data sets you might not even know existed. Professor Pasquale described the availability of databases of everything from the seemingly innocuous (wine enthusiasts) to those that shock the conscience (victims of rape). With the myriad of data available and so much of it highly personal in nature, underwriters must recognize they have a responsibility to a new set of stakeholders beyond their company, clients, shareholders and regulators -- namely, digital identities.

The next risk of social harm is in how that data is used. Predictive models seek to identify correlations between new points of data to predict loss potential. If correlations are wrong, not only could it jeopardize the underwriter’s ability to properly price a risk, but the correlations could result in an illegal practice like red-lining. This situation could occur accidentally, but a dataset could be used nefariously to circumvent a statute prohibiting use of certain information in decision making.

In California, there is a prohibition on using credit scores in underwriting certain risks. Perhaps a modeler for a personal lines insurance product draws information from a database of locations of check cashing stores or pawn shops and codes into the algorithm that anyone with an address in the same ZIP code is assumed to have bad credit. You would hope this would not happen, but insurance companies use outsourced talent, over which they have less control. Maybe a modeler works outside the U.S. and is innocently unfamiliar with our social norms as well as our regulatory statutes.

There are also social risks related to speed and complexity of predictive models. Dozens of datasets might be accessed, with different coded correlations and computations processed that are then weighted and ranked until a final series of recommendations or decisions are presented to the user. Transparency is difficult to attain.

If there is something ethically or statutorily wrong with a model, the speed at which processing can occur and the opaqueness of the algorithms can prolong any social harm.

Don’t Throw the Baby Out With the Bathwater

While regulation of big data analytics is not well-established, there are governance steps that insurance companies can take. Insurance companies can start by aligning their predictive models with their corporate values. Senior leadership should insist that decision-making technology adhere to all laws and regulations, but more generally will be fair. Fairness should apply to the process and to the rendered decisions. Standards should be established, customers treated with respect, professional obligations fulfilled and products represented accurately.

Insurance companies should audit their models and data to ensure a causation linkage to underwriting loss. Any data that does not support causation should be removed. Parallel processes employing traditional and artificial intelligence techniques should also be run to confirm that an appropriate confidence level of actuarial equivalence is met. Data should be scrubbed to anonymize personally identifiable information (PII) as much as necessary to support privacy expectations and statutes. To remove biases, audits should identify and require exclusion of information that acts as a proxy for statutorily disallowed data.

In essence, the models should be run through a filter of protected class categories to eliminate any illegal red-lining. Because models are developed by humans, who are inherently flawed, modelers should attempt to program their machine learning innovations to identify biases within code and self-correct for them.

From a base of fairness, carriers can take steps to promote transparency. By starting with an explanation of the model’s purpose, insurers can move toward outlining the decision-making logic, followed by subjecting the model to independent certification and finally by making the findings of the outside auditor available for review.

Insurers can look to trade associations and regulatory bodies for governance best practices, such as those the National Association of Insurance Commissioners (NAIC) announced in August 2020. The five tenets of the AI guidelines promote ethics, accountability, compliance, transparency and traceability.

See also: Our Big Problem With ‘Noise’

One regulation that could be developed would be imposing rate bands. Predictive engines would still reward superior risks and surcharge poorer-performing accounts, but rate bands would temper the extremes. This regulation would provide a balance between the necessity for mutualization of risk and individualization of pricing that could lead to unaffordability in certain cases.

Finally, insurance companies should recognize the importance of engaging with regulators early in the development of their AI strategies. A patchwork of regulation exists today, and insurance companies could find regulatory gaps that they might be tempted to exploit, but the law will catch up with the technology, and carriers should build trust with regulators from the onset, not after a market conduct exam identifies issues. Regulators do not wish to stifle innovation, but they do strive to protect consumers.

Once regulators are comfortable that models and rating plans will not unfairly discriminate nor jeopardize the solvency of the carrier, they can help enable technology advancements, especially if AI initiatives facilitate an expansion of the market through more capacity or new products, lowers overall market costs or provides insights that helps customers improve their risk profile.

In the data arms race that carriers are engaged in with each other, better risk selection and more accurate pricing are without question competitive advantages. Another, often-overlooked competitive advantage is an effective risk management program. Robust management of a company’s AI risks will reduce volatility in a portfolio and promote resiliency. With this foundation, a carrier can deftly outmaneuver competition and should be an additional strategy that is prioritized.

While the focus for years has been on autonomous cars and on what they'll do for safety, for auto insurance, for our lifestyles and more, a disruption is taking shape in the nearer term: autonomous trucks.

The fear factor has obscured that vision. While it is odd enough to drive down a street in Phoenix and see a Waymo minivan next to you without a driver, it's hard to imagine anyone setting loose on a highway an 18-wheeler carrying 50,000 pounds without anyone at the wheel.

But we're close.

If you can get the image of a hulking semi out of your mind, highway driving makes perfect sense. The issues that have slowed deployment of autonomous cars all relate to the vagaries of us humans. The technical problems related to snow, rain, fog, etc. have all been solved. But is that driver going to shove his way into the intersection even though he doesn't have the right of way, or will he wait for the AV? What about that pedestrian walking against traffic? That bicyclist who seems confused? Does the ball that rolled into the street mean a little kid is about to follow it from behind the double-parked van? But highway driving takes away a huge number of the human variables -- no pedestrians, no cyclists, far less merging with other vehicles.

Autonomous trucks can basically just get on a freeway and go straight until they need to get off. They solve a real problem, too. By law, truck drivers can only drive 11 out of every 24 hours. That means trucks, with valuable cargo, sit 13 out of every 24 hours. It also means that trucking companies are always short of drivers. But autonomous trucks would be able to go 24/7, cutting many trips in half and making trucking much more efficient.

The change would have broad implications, including for insurers that cover truck fleets and their cargo and for those that cover workers -- some driver jobs would disappear, while others would morph to handle changes in loading and unloading, refueling and more. For instance, some drivers might become specialists in the "first mile" or "last mile," taking an autonomous rig through complex city traffic out to the freeway or picking a rig up on the freeway and navigating it to its final destination, much as captains who know a harbor have long done with ocean-going cargo ships.

Change won't happen overnight. Trucks still have to overcome that first-mile/last-mile problem -- a high-profile startup shut down last year after trying to have drivers use virtual reality to take control of trucks whenever necessary. Autonomous trucks will also be more complicated mechanically than cars for the foreseeable future. While autonomous cars will all be fully electric, the batteries necessary to run trucks are so heavy that they cut into mileage too greatly, so autonomous trucks will not only need to have enough battery power to run all the sensors and computers but will still require an internal combustion engine.

Still, workarounds are developing, and autonomous trucks are making great progress. For instance, Locomation offers what it calls autonomous relay convoys, which combine two human drivers and two autonomous vehicles. Each human drives a rig to the freeway, where one then takes charge of driving. The other's rig switches to autonomous mode and follows the lead rig, while the human in the trailing vehicle rests. When the first driver's 11 hours are up, the rested driver takes over. Whenever the trucks need to split up to go their individual destinations, the respective drivers simply take control.

These sorts of convoys would barely make a dent in the potential of autonomous vehicles, but they do solve a real problem, and they provide a start for the long adoption curve ahead of us.

While the idea of having 50,000 pounds barreling down the freeway without a human at the wheel may still be intimidating, think of it this way: Truck drivers are up so high up that you rarely notice them, unless they've done something aggressive and you're looking for the driver because he's ticked you off. And AVs are by nature so cautious that you'd almost never try to stare down their driver. So you may not even notice the transition to automated trucks.

Cheers,

Paul

Ask yourself these simple questions: Do I walk/run at least 5 km (3 miles) a day? Do I drive or take public transport everywhere I need to go? Do I drink more water than alcohol when having dinner at a restaurant with friends? At my lunch break, do I take 45 minutes to enjoy a healthy meal or eat fast food?

While some of us may have adopted very healthy lifestyles, many more have not. But what if technology and behavioral science could work together to help all of us live healthier, longer lives? The impact could be transformative. For example, the World Health Organization (WHO) found that the proportion of total global deaths due to chronic lifestyle diseases is expected to increase to 70% of the global burden of disease by 2030, up from 56% in 2015. A basic understanding of behavioral science, combined with advances in wearable and personalized technologies, could begin to make a tangible difference in risk assessment.

There are reasons, rooted in human psychology and behavior, that cause many of us to fail to act as we know we should. Cognitive biases, and the pressures of modern life, can defeat even the best intentions. For example, if you prefer red wine over water at dinner, you may fall into the "present bias" trap, or an aversion to delayed gratification in favor of an immediate reward. In other words, many of us would seize the prospect of a sip of a good cabernet over a less tangible future gain, such as healthier liver function.

Many of us are at pains to set objectives - avoiding an extra serving of cheesecake or sticking to the sparkling water over the pint of beer -- but, despite the best plans, personal trainers and advice of friends, we fall into old habits. Similarly, many of us fail to follow through on New Year's resolutions, a phenomenon behavioral scientists call the "intention-behavioral gap." Often, our "doing-selves" do not follow the intentions of our "planning-selves." We know we should take a real break to have a proper, healthy lunch but still end up ordering a snack.

Is there a way to help people achieve goals set by our "planning-selves?" Can insurers act to help people by designing value propositions that promote healthier behaviors? The answer may lie, in part, in whether carriers can fully grasp human biases and behaviors and harness technologies to use this knowledge to improve health.

Understanding Motivation

First, consider how people make decisions. In his book, "Thinking Fast and Slow," famed behavioral scientist Daniel Kahneman argues that the human brain has two different operating systems: System 1 and System 2. "System 1" is fast, instinctive and emotional. "System 2" is slower, more deliberative and more logical. When we think of human decision making, we often assume that people are rational, calculating decision-makers (System 2) and therefore think that providing people with facts will change behavior.

Reality is a bit more complex. Matthew Battersby, chief behavioral scientist at RGA, often uses smoking as an example: "Lots of money has been spent for many years trying to inform and change the minds of smokers about the dangers and risks associated with the activity," he says. "Many smokers know these risks yet still don't change their behavior, and those who do change aren't necessarily influenced simply by information. Instead, they may have been persuaded to stop smoking by various other factors, such as changes in the environment (e.g., smoke-free workplaces) and motivational aspects (e.g., it's much harder to smoke when lots of your friends are no longer smoking)."

Even when consumers are well-informed about a health danger, some may continue destructive behaviors. Information alone -- or System 2 thinking -- is not enough. System 1 thinking may explain why; many smokers make instinctual, and perhaps irrational, decisions to continue the habit.

Most of us respond to environmental and social cues in a way that requires very little conscious engagement. Health decisions may be rational, but actual health behavior is much less so and more often driven by often unconscious, cognitive processes. Memory is also imperfect, and attention spans are limited, making fully fact-based decision-making even more difficult.

So, rather than bombarding a consumer with wellness data to encourage healthier behaviors, insurers can, instead, influence behavior by using use tools/mechanisms that target human System 1 responses (fast, impulsive), appealing to psychology and not rationality. This can take the form of reinforcement or reminders that focus attention on personal health goals and rewards or even celebratory messages when individuals reach health milestones, such as a certain step count.

See also: Life Insurance With Mortgage Protection

Applying Motivational Techniques

However, understanding human motivation is only half of the story. Success will require a means to reach the right customer, at the right time, with these messages. Wearable technologies offer a compelling means to help more people adopt healthier behavior, but they also present limits.

Why are wearables so promising? Consider the enormous popularity of these portable, data-rich devices. CNN Business reports that the Apple Watch sold 31 million units worldwide, while all Swiss watch brands combined sold 21 million units, according to research from consulting firm Strategy Analytics. Market researchers have found that 81% of wearable device owners feel that they have made an improvement in their overall health/lifestyles by using these devices.

So, the adoption of wearables technologies represents one step forward for the watch trade and one giant leap for the life/health insurance industry, right? Not necessarily. Many consumers appear to tire of these devices, and levels of comfort with health data-sharing can vary.

The COM-B model in behavioral science can help explain why. The model states that human behavior (B) consists of three components: capability (C), opportunity (O) and motivation (M). Capability refers to the belief that we are psychologically and physically able to change or improve a behavior. Opportunity refers to a social or physical opportunity to make a change (or reduce the environmental triggers that spurred negative behavior). Motivation is linked to the desire to carry out a certain behavior over competing behaviors.

Capability (I can cycle) and opportunity (cycling near home is safe) are obvious, but what about motivation? How do insurers get it, and how can insurers provide it?

Mastering Motivation

All of us have faced situations in which we must motivate someone to make a change, whether that person is a child, a coworker or a stranger. We use persuasive techniques that draw on behavioral science, often unknowingly. For example, it is common to promise a reward: Do X thing and receive Y benefit.

For organizations aiming to make us healthier and help us live longer, rewards make sense. But what rewards work? Behavioral science offers clues.

• Frequent feedback: We are more likely to achieve a goal if we receive frequent and qualitative feedback, such as push notifications that congratulate users or explain areas of improvement.
• Scarcity: Options or rewards that are perceived to be scarce can seem more attractive. For example, the idea of a "last chance" to win points can prompt someone using a wellness app to walk an extra 1,500 steps.
• Commitment contracts: We are more likely to achieve goals if we have committed to them, so encouraging customers to commit to goals through contracts leads to greater success.
• Messenger: Our reaction to information and messages depends greatly on the credibility of the messengers. For example, a recommended exercise regimen from a famous gym coach will carry greater weight than a suggested regimen from a friend.
• Salience: Our attention is drawn to what is novel and prominent -- that's why "Walk Now" push notifications in health apps may increase our activity rates.
• Relative ranking: We tend to do better if we can compare our performance with others. Competition yields results.
• Utility: We act in ways that prioritize advantages, minimize losses and maximize perceived value. That's why health programs and apps that allow participants to clearly evaluate value, such as weight loss or health improvement, can drive results.
• Ease: We are more likely to change our behavior if we perceive that change to be easy. Programs that allow for incremental improvement, such as tips on how to walk 150 steps more per day, can yield greater participation.

Looking Forward

Insurance carriers are often perceived to have fewer customer interactions compared with other financial services providers, like banks. However, the ability to engage through wearables could create more active relationships that benefit both the consumer and the insurer.

The popularity of connected devices offers an opportunity to support insurers in offering services that can adapt to consumer behavior and support customers in managing personal risk and improving health. Insurance companies can move from being perceived as simply "providers of policies that protect against risks" to being seen as guardians of health and longevity.