In early 2022, Enlyte conducted a survey of workers’ compensation professionals to understand how the industry’s use of technology changed over the past year. The results demonstrated that COVID-19 is still a major driver of technology adoption in the industry today. Most payers are focused on applying technology to solve, or at least ease, COVID-related pain points, from managing unpredictable claim patterns to dealing with staff changes due to "the Great Resignation."

Looking ahead five to 10 years, the survey indicated payers will place greater emphasis on technology to create a better claims experience for injured employees, such as by improving communication and focusing on the return-to-work process. 

Additionally, the survey found that there is still a significant opportunity for payers to increase straight-through processing of medical bills, because only a few respondents said they process more than half of their medical bills automatically. 

Survey Results

Here were some of the most interesting results from Enlyte’s 2022 workers’ compensation technology survey:  

1. Today, payers are focused on using technology to improve efficiency and help solve COVID-19 challenges.

• In 2021, the industry invested most in initiatives to implement telemedicine, electronic payments and billing.
• Of all respondents, 27% said COVID-19 claims challenges, specifically claim volumes and types of claims, are the biggest drivers of technology adoption in the workers' compensation industry today, followed by a need to increase efficiency.
• Those who said their organizations implemented new technologies in 2021 indicated that artificial intelligence and improved user experience and integrations made the biggest impact on their businesses.

One of the biggest trend changes revealed in Enlyte’s 2022 survey was the growing number of respondents who said their companies invested in electronic payments last year. Electronic payments and billing moved from fourth place in the 2021 survey to second in 2022. This demonstrates how payers have been working to implement technologies to support virtual workforces and modernize their processes. Additionally, electronic payments and billings can provide cost savings. Paying medical bills via paper check can cost between $4 and $30 per transaction — an electronic payments program can reduce that cost by up to 90%. 

2. In the next five to 10 years, the industry will focus on using technology to improve injured employees' experiences. 

• Of all respondents, 22% said mobile apps and other communication technologies are expected to have the most influence on the industry in the next five to 10 years.
• Additionally, 28% of respondents believe communicating with injured employees is the step in the claim lifecycle that could benefit most from applying new or additional technology.

While implementing new technologies in this area can provide significant benefits to all stakeholders in the claims process, it is important that the industry strikes the right balance by focusing on technologies with a low entry barrier, so injured employees will actually use them. For example, while mobile apps may be convenient, finding investment dollars and resources to build them and convincing people to download them can be challenging. Instead, it may be more effective and efficient for all parties involved if a payer focused on improving communication with technologies people already use in their everyday lives, like text messaging or email. 

3. The workers’ compensation industry has an opportunity to increase automation.

• Only 21% of respondents said their organization is automating more than 50% of their bills using straight-through automation.
• 32% responded that their organization is only automating 0-5% of workers’ compensation claims. 
• About half (48%) of respondents said their company uses a homegrown claims system, which can allow payers to create extensive customization but may cause them to miss out on opportunities to increase automation.

It is easy for payers to increase straight-through automation using business rules engines and other technologies they already have in place. For instance, a high-powered bill review rules engine can facilitate up to 90% straight-through processing rates. The survey revealed a clear opportunity for many payers to take a closer look at automating many of their medical bills for more consistent and efficient outcomes. 

Understanding the Future of Technology in Workers’ Compensation

The survey results show an industry interested in applying technologies to not only react to major trends and changes but also to improve the experience for its main constituents – injured employees. In the near term, we expect workers’ compensation will continue to focus heavily on improving efficiencies, getting a new generation of employees up to speed fast and creating overall better processes internally. Once organizations have laid a solid internal foundation, then we will see focus shift to the return-to-work process, improving injured employee communication and more. Overall, all of these technology changes should lead to improved claims processes and overall better outcomes for all parties. 

Enlyte surveyed 115 workers’ compensation professionals in January and February 2022. Most survey respondents have a role at their company at the manager level or higher and work for an insurance carrier, third party administrator or employer. About 40% said they have been working in the workers’ compensation industry for more than 15 years, and about half work for a company with 1,000 employees or fewer.

*|MC_PREVIEW_TEXT|*

AI's Cool New Trick Paul Carroll, Editor-in-Chief of ITL One of my lingering misgivings from my days as a reporter at the Wall Street Journal concerns the role I played in unleashing PowerPoint on the world.  I was just doing my job. Honest. Like all good journalists, I was at the bar, at a conference in the spring of 1987, when a consultant I knew introduced me to one of the two founders of a startup that was about to unveil PowerPoint. The demo he showed me was intriguing, and the prospects for the product checked out with a number of smart folks I interviewed at the conference, so I wrote about it.  Next thing you know, Microsoft buys the startup, and we're all awash in bullet points and so many fonts and type sizes that presentations may look like ransom notes. Today, I'd like to introduce you to the latest advancement in visual presentations, an artificial intelligence that lets you generate an image based on a single sentence -- for instance, the AI produced the image above based on the prompt, "astronaut riding a horse in a photorealistic style."  I can't guarantee that problems won't eventually arise with this technology, too, but at least it'll be a lot more fun than fussing with PowerPoint templates.  continue reading > Podcast Alert Join Denise Garth for a new podcast featuring Deloitte's Abhishek Bakre, Senior Manager of Strategy and Santosh Kutty, Principal, to discuss changing customer demographics, absence management, and bundling with disability and leave products. Listen Now SIX THINGS In Competition for Top Talent, Innovation Matters by Tom Warden People want to do work that is interesting and purposeful. They value an organizational culture that places a premium on creativity, thought leadership and innovation. Read More How to Reduce Distracted Driving by Adam Pichon Few drivers are aware that texting while traveling at 55mph is the equivalent of driving the length of a football field with your eyes closed. Read More The Future of Work  Wednesday, May 4th at 1:00 pm EDT Sponsored by JobsOhio  As employees start to return to work after two years of mostly working remotely, smart employers are rethinking just about all aspects of how work is done to get the best of both the home and office worlds.  Register Today How to Preempt Disinformation by Vivek Wadhwa Just as the U.S. spends hundreds of billions on warships and planes, it should invest in an information warfare machine focused on continually preempting disinformation. Read More Top Brokers' Advantage by L.S. Ram For brokerages that intend to participate in fast-emerging opportunities, the future is now. It’s time to carpe diem.  Read More Time to Embrace AI in Climate Change Fight by Andrew Yeoman We often think about climate change in the long term. It's easy to forget that we already live with the consequences of unchecked climate change today. Read More Lessons From Travel Insurance by Ryan Brubaker When done correctly, technology is as transparent as it is critical. When implemented poorly, it's like begging your customers to look elsewhere for services. Read More Oliver Wyman: Reset4Value Series  Drive the next wave of growth  Sponsored by Oliver Wyman Oliver Wyman’s Reset4Value approach helps insurers transform cost and ignite growth. Our insight shares how leaders can leverage their firm’s culture strengths, enhance the capabilities that matter most, and unlock scarce investment dollars to fund them appropriately. Read Now  MORE FROM ITL April Focus: Automation and RPA Sometimes, innovation takes time.   Some 30 years ago, I wrote an article for the front page of the second section of the Wall Street Journal that declared a revolution in forms. We were far enough along in the personal computer revolution that software companies were coming out with products that would let users fill out forms on-screen, speeding the process and eliminating the errors that occurred as someone had to interpret people's handwriting. Even more magical, the spread of local area networks meant that information could flow straight from my screen into a corporate database, with no never to ever print the form and have someone re-enter the data. .  Everything I wrote was correct, and forms did take a major step forward, but, here we are three decades later, still drowning in forms. And the insurance industry is Exhibit A.  Read More   Winning the War for Talent  Thursday, April, 28th at 1 pm EDT Sponsored by PwC This webinar tackles a key issue -- maybe the key issue -- facing the insurance industry: How can we attract, train and retain the talent that we need and that the industry's mission merits. 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 2022 Media Kit

Artificial intelligence (AI) and machine learning (ML) are transforming the insurance industry. Many companies are already using them to assess underwriting risk, determine pricing and evaluate claims. But, if the proper guardrails and governance are not put into place early, insurers could face legal, regulatory, reputational, operational and strategic consequences down the road. Given the heightened scrutiny surrounding AI and ML from regulators and the public, those risks may come much sooner than many people realize.

Let's look at how AI and ML function in insurance for a better understanding of what could be on the horizon.

A Quick Review of AI and Machine Learning

We often hear the terms "artificial intelligence" and "machine learning" used interchangeably. The two are related but are not directly synonymous, and it is important for insurers to know the difference.

Artificial intelligence refers to a broad category of technologies aimed at simulating the capabilities of human thought.

Machine learning is a subset of AI that is aimed at solving very specific problems by enabling machines to learn from existing datasets and make predictions, without requiring explicit programming instructions. Unlike futuristic "artificial general intelligence," which aims to mimic human problem-solving capabilities, machine learning can be designed to perform only the very specific functions for which it is trained. Machine learning identifies correlations and makes predictions based on patterns that might not otherwise have been noted by a human observer. ML's strength rests in its ability to consume vast amounts of data, search for correlations, and apply its findings in a predictive capacity.

Limitations and Pitfalls of AI/ML

Much of the potential concern about AI and machine learning applications in the insurance industry stems from predictive inference models - models that are optimized to make predictions primarily or solely on correlations in the datasets, which the models then employ in making predictions. Such correlations may reflect past discrimination, so there is a potential that, without oversight, AI/ML models will actually perpetuate past discrimination moving forward. Discrimination can occur without AI/ML, of course, but the scale is much smaller and therefore less dangerous.

Consider if a model used a history of diabetes and BMI as factors in evaluating life expectancy, which in turn drives pricing for life insurance. The model might identify a correlation between higher BMI or incidence of diabetes and mortality, which would drive the policy price higher. However, unseen in these data points is the fact that African-Americans have greater rates of diabetes and high BMI. Upon a simple comparison of price distribution by race, these variables would cause African-Americans to have higher pricing.

A predictive inference model is not concerned with causation; it is simply trained to find correlation. Even when the ML model is programmed to explicitly exclude race as a factor in its decisions, it can nevertheless make decisions that lead to a disparate impact on applicants of different racial and ethnic backgrounds. This sort of proxy discrimination from ML models can be far more subtle and difficult to detect than the example outlined above. They also might be acceptable, as in the prior BMI/diabetes example, but it is critical that companies have visibility into these elements of their model outcomes.

There is a second major deficiency inherent in predictive inference models, namely that they are incapable of adapting to new information unless or until they are properly acclimated to the "new reality" by training on updated data. Consider the following example.

Imagine that an insurer wishes to assess the likelihood that an applicant will require long-term in-home care. They train their ML models based on historical data and begin making predictions based on that information. But, a breakthrough treatment is subsequently discovered (for instance, a cure for Alzheimer's disease) that leads to a 20% decrease in required in-home care services. The existing ML model is unaware of this development; it cannot adapt to the new reality unless it is trained on new data. For the insurer, this leads to overpriced policies and diminished competitiveness.

The lesson is that AI/ML requires a structured process of planning, approval, auditing, and continuous monitoring by a cross-organizational group of people to successfully overcome its limitations.

See also: Eliminating AI Bias in Insurance

Categories of AI and Machine Learning Risk

Broadly speaking, five categories of risk related to AI and machine learning exist that insurers should concern themselves with: reputational, legal, strategic/financial, operational, and compliance/regulatory.

Reputational risk arises from the potential negative publicity surrounding problems such as proxy discrimination. The predictive models employed by most machine learning systems are prone to introducing bias. For example, an insurer that was an early adopter of AI recently suffered backlash from consumers when its technology was criticized due to its potential for treating people of color differently from white policyholders.

As insurers roll out AI/ML, they must proactively prevent bias in their algorithms and should be prepared to fully explain their automated AI-driven decisions. Proxy discrimination should be prevented whenever possible through strong governance, but when bias occurs despite a company's best efforts, business leaders must be prepared to explain how systems are making decisions, which in turn requires transparency down to the transaction level and across model versions as they change.

Key questions:

  1.  In what unexpected ways might AI/ML model decisions impact our customers, whether directly or indirectly?
  2.  How are you determining if model features have the potential for proxy discrimination against protected classes?
  3.  What changes have model risk teams needed to make to account for the evolving nature of AI/ML models?

Legal risk is looming for virtually any company using AI/ML to make important decisions that affect people's lives. Although there is little legal precedent with respect to discrimination resulting from AI/ML, companies should take a more proactive stance toward governing their AI to eliminate bias. They should also prepare to defend their decisions regarding data selection, data quality, and auditing procedures that ensure bias is not present in machine-driven decisions. Class-action suits and other litigation are almost certain to arise in the coming years as AI/ML adoption increases and awareness of the risks grows.

Key questions:

  1.  How are we monitoring developing legislation and new court rulings that relate to AI/ML systems?
  2.  How would we obtain evidence about specific AI/ML transactions for our legal defense if a class-action lawsuit were filed against the company?
  3.  How would we prove accountability and responsible use of technology in a court of law?

Strategic and financial risk will increase as companies rely on AI/ML to support more of the day-to-day decisions that drive their business models. As insurers automate more of their core decision processes, including underwriting and pricing, claims assessment, and fraud detection, they risk being wrong about the fundamentals that drive their business success (or failure). More importantly, they risk being wrong at scale.

Currently, the diversity of human actors participating in core business processes serves as a buffer against bad decisions. This doesn't mean bad decisions are never made. They are, but as human judgment assumes a diminished role in these processes and as AI/ML take on a larger role, errors may be replicated at scale. This has powerful strategic and financial implications.

Key questions:

  1.  How are we preventing AI/ML models from impacting our revenue streams or financial solvency?
  2.  What is the business problem an AI/ML model was designed to solve, and what other non-AI/ML solutions were considered?
  3.  What opportunities might competitors realize by using more advanced models?

Operational risk must also be considered, as new technologies often suffer from drawbacks and limitations that were not initially seen or that may have been discounted amid the early-stage enthusiasm that often accompanies innovative programs. If AI/ML technology is not adequately secured - or if steps are not taken to make sure systems are robust and scalable - insurers could face significant roadblocks as they attempt to operationalize it. Cross-functional misalignment and decision-making silos also have the potential to derail nascent AI/ML initiatives.

Key questions:

  1.  How are we evaluating the security and reliability of our AI/ML systems?
  2.  What have we done to test the scalability of the technological infrastructure that supports our systems?
  3.  How well do the organization's technical competencies and expertise map to our AI/ML project's needs?

Compliance and regulatory risk should be a growing concern for insurers as their AI/ML initiatives move into mainstream use, driving decisions that impact people's lives in important ways. In the short term, federal and state agencies are showing an increased interest in the potential implications of AI/ML.

The Federal Trade Commission, state insurance commissioners, and overseas regulators have all expressed concerns about these technologies and are seeking to better understand what needs to be done to protect the rights of the people who live under their jurisdiction. Europe's General Data Protection Regulation (GDPR), California's Consumer Privacy Act (CCPA), and similar laws and regulations around the world are continuing to evolve as litigation makes its way through the courts.

In the longer term, we can expect regulations to be defined at a more granular level, with the appropriate enforcement measures to follow. The National Association of Insurance Commissioners (NAIC) and others are already signaling their intentions to scrutinize AI/ML applications within their purview. In 2020, NAIC released its guiding principles on artificial intelligence (based on principles published by the OECD) and in 2021, created a Big Data and Artificial Intelligence Working Group. The Federal Trade Commission (FTC) has also advised companies across industries that existing laws are sufficient to cover many of the dangers posed by AI. The regulatory environment is evolving rapidly.

See also: Time to Embrace AI in Climate Change Fight

Key questions:

  1.  What industry and commercial regulations from bodies like the NAIC, state departments of insurance, the FTC, and digital privacy laws affect our business today?
  2.  To what degree have we mapped regulatory requirements to mitigating controls and documentary processes we have in place?
  3.  How often do we evaluate whether our models are subject to specific regulations?

These are all areas we need to watch closely in the days to come. Clearly, there are risks associated with AI/ML; it's not all roses when you get beyond the hype of what the technology can do. But understanding these risks is half the battle.

New solutions are hitting the market to help insurers win the risk war by developing strong governance and assurance practices. With their help, or with in-house specialists on board, risks will be overcome to help AI/ML reach its potential.

As first published in Dataversity.

ITL:

I’m reading a fascinating book called “The Dream Machine” about the very early days of computing, and the idea of “computability” was phenomenally powerful even back in the 1930s, ‘40s and ‘50s. You’ve updated the idea considerably and are applying it to insurance, among other areas. Let’s start by having you spend a couple of minutes laying out what “computability” means today.

John Sviokla:

Businesses are finding that they can take known algorithms and capabilities of mathematics and apply them at whole new levels. There are all kinds of insurance now where the statistics that are being used to price a $500 insurance policy are as good as what was used for a multimillion-dollar policy 40 years ago. That’s simply because of the availability of the data plus the low cost of using the algorithms to make that happen. You've taken something that used to be based on judgment, because of a combination of lack of data and high cost, and made it computable.

And there are lots of implications. For instance, businesses can now do mirroring – setting up a fully digital, detailed model of their business or major parts of it so they can experiment with changes before implementing them in the physical world. And that mirroring allows for really profound changes because you’ve created a symbolic world that can exist even in and of itself. Basically, you can have algorithms talking to algorithms via robots.

You can think of market slices not in terms of geography but in terms of points in time. That’s a whole new dimension of a market that's being born in a symbolic space that is only accessible to the computable decision that can operate in billionths or millionths of a second. You’re creating these limits, almost like little Russian dolls in time. Every second expands out into tradable realities in the context of these symbol systems.

One last part is sensors.

Generally speaking, one way to look at how these symbol systems relate to reality is that they are representation systems. What is a Chinese character? It's an abstraction as part of a representation system, like the sounds we make on the phone that distill reality and communicate.

What's happening with IoT is a lot like what happened with the Gutenberg press. People had read before, but the Gutenberg revolution expanded existing representation systems called language and books. We're seeing another vast expansion with all these sensors that can help us understand and represent the world digitally.

And neural networks are incredibly powerful at taking continuous variables measured by these sensors and making them discrete. Variables that used to be analog can now be registered in digital form, where AI can unleash all kinds of advances.

ITL:

I think we can start to imagine some of the implications for insurance.

Sviokla:

This move from continuous to discrete variables can be a profound starting point for claims. We gather all kinds of new, continuous variables, and you can create a whole new representation system that vastly improves your decision making.

You can also build on your new access to computable data and look at your business in new dimensions. Am I just fixing this Toyota? Am I fixing this Toyota Camry today after it's been hit? Am I looking at the whole population of Toyotas? Am I contracting with service centers across the country to do a continuous service level agreement that keeps all Toyotas in the U.S. up and running?

You get so much granularity that you can act like the capital markets, where you can trade the bond or the mortgage-backed security or a portfolio of securities or a synthetic CDO or whatever. In insurance, you can “trade” that Toyota Avalon crash or trade Avalon bumpers or trade the parts that go into the bumpers or whatever, including in derivative markets based on all those physical assets. The key is to have the syntax and semantics of trading, which computability allows you to do.

ITL:

Let’s drill down into claims, as long as you’ve cited that as a key area where computability can transform insurance.

Sviokla:

There's a tremendous amount of variation in how claims can and should be assessed and handled, but if you have tons of claim data, tons of pictures, structural information, etc., then you basically just have a large numbers problem, and you can compute whatever you need. You can then drive toward more and more diagnostic specification accuracy, lower labor costs. quicker turnaround and higher quality. You can also do a better job of managing claims at the population level.

ITL:

Are we primarily doing a better job of computing what we already know, or is computability also taking us into unexplored territory?

Sviokla:

The majority in the near term will be in computing better what's already known. That, alone, will have a massive effect on the productivity of claims agents and allow for the least expensive provision of service.

Over time, at the more aggregate level, you'll start to see a combination of different variables that will begin to inform us about where accidents happen – what intersections, which drivers, what time of day, what conditions and so on. I think that's going to be more longer-term because the issue doesn't sit in anybody's wheelhouse, in particular. Each piece of that value equation is spread out.

ITL:

I’ve long been a proponent of the idea that we need to sell protection and not just indemnify people after a loss. Nobody wants to buy insurance, but everybody wants to be protected.

Sviokla:

I think we're going to need a couple of generations of smart cars. A car is a moving powerplant with sensors and a human, right? And the number of those sensors is going to continue to go up, and the intelligence is going to continue to go up in the car. Those capabilities will birth all kinds of new computable reality, and there’s a threshold effect. At some point, some clever person at Tesla or maybe Mercedes is going to say, buy a car from us and we will give you road intelligence based on the sensors in all our cars, plus NASA, plus blah, blah, blah. And that's real-time road intelligence, which will reduce claims.

Everything about a claim will be informed by the digital representation of how someone has taken care of a vehicle. So, did you take good care of the truck? Were you driving too fast? Where are the parts from, such as the brakes? Did they work the way they're supposed to in this condition? The claim becomes a punctuation point in the context of a continuous analysis.

The general argument is that you have isomorphisms among different representations of reality. I mean, think of the design of a building. In the old days, you had an outside drawing, then the engineering drawing and construction drawing and contractors’ blueprints. Then you have the “as built” and maintenance documentation on top of that. Those are all projections of one reality across time and function. What you have the ability to do now is to have one reality, which then gets projected for each audience in each need across time.

Computability will make all kinds of things easier and open up all sorts of funky possibilities. Maybe somebody files a claim, and you say, we’ll just buy the car from you, get you a comparable model from Carvana and deliver it to you. You already know what the resale value is for the car you’re offering to buy. You might even have it resold before you buy it. Maybe you’re in the parts market and are getting contingent offers on the car you’re buying.

Computability creates a possibility cloud around every asset.

ITL:

Thanks, as always, John

One of my lingering misgivings from my days as a reporter at the Wall Street Journal concerns the role I played in unleashing PowerPoint on the world. 

I was just doing my job. Honest. Like all good journalists, I was at the bar, at a conference in the spring of 1987, when a consultant I knew introduced me to one of the two founders of a startup that was about to unveil PowerPoint. The demo he showed me was intriguing, and the prospects for the product checked out with a number of smart folks I interviewed at the conference, so I wrote about it. 

Next thing you know, Microsoft buys the startup, and we're all awash in bullet points and so many fonts and type sizes that presentations may look like ransom notes.

Today, I'd like to introduce you to the latest advancement in visual presentations, an artificial intelligence that lets you generate an image based on a single sentence -- for instance, the AI produced the image above based on the prompt, "astronaut riding a horse in a photorealistic style." 

I can't guarantee that problems won't eventually arise with this technology, too, but at least it'll be a lot more fun than fussing with PowerPoint templates. 

The AI was developed by OpenAI, a nonprofit company backed by Microsoft, among many others, that has the very serious goal of developing what's known as artificial general intelligence -- basically, AI that works broadly, like the human brain, rather than being finetuned for a specific task, such as playing chess or recognizing images. OpenAI has produced an intriguing series of highlights, including defeating a professional e-sports world championship team on a livestream and solving Rubik's Cube with a robot hand. 

But I'm mostly intrigued by the playfulness that becomes possible for presentations via OpenAI's latest development, which is known as DALL-E 2. 

At the risk of telling you more than you want to know about my twisted sense of humor, here are the two images generated by DALL-E 2 that I've enjoyed most so far:

That was generated based on the phrase, "medieval painting of complaining that the Wi-Fi isn't working."

And:

"Ancient Egyptian painting depicting an argument about whose turn it is to take out the trash."

Oh, okay, two more:

"Photo of a grizzly bear confused in calculus class."

And:

"Leonardo enters the metaverse."

I can imagine that the ability to just conjure up an image could contribute to the growing problems for deep fakes -- though the OpenAI folks are also working on ways to make sure AI is used only ethically. So, for now, I'll just let my imagination enjoy itself.

Cheers,

Paul

How do you think the insurance industry is going to look in 10 years? With online-only companies becoming more popular and more desirable, they’re causing the entire industry to rethink how they’re going to stay relevant. The introduction of artificial intelligence into the policy recommendation process is just another piece in the puzzle of how businesses and families will interact with insurance companies 10 years from now. 

Some thoughts to keep in mind:

--Accenture's global Insurance Consumer Study found that 57% of consumers said that they would find it appealing to get advice from their insurer or bank on how to travel and shop more sustainably. Millennials and Zoomers showed even more interest, at 67%. 

--As of 2020, a study conducted by the AnitaB.org Institute found that women make up only 29% of the tech workforce. Another study published by the World Economic Forum found that only 22% of AI professionals across the world are female. These low numbers help contribute to AI systems that show bias. In the next 10 years, women and other diverse members are going to be essential at making sure the industry can identify biases in data before they make it to the consumer.

--Usage-based insurance (UBI) has been gaining traction in the U.S. at a compound annual growth rate estimated above 29% from 2019-2026. 

“Policy holders expect more out of their insurance when it comes to the overall experience. Artificial intelligence isn’t just for smartphones and video games but can also be used to keep us safe and protected and save money. Over the next several years, we'll see AI play an ever-increasing role in the recommendation and accuracy of policy coverages, while also bringing the entire insurance industry into the 21st century to be on par with organizations that offer superb services,“ says Linh C. Ho, chief marketing officer at Zelros.

In recent years, there has been a shift in the world of business, from a focus on transactions to an emphasis on relationships. Today’s companies no longer shortsightedly prioritize profits over everything else but are instead increasingly concerned with earning their consumers’ trust. Instead of simply selling consumers products, they market themselves as companies to believe in and make part of one’s life.

While this trend is evident across the business spectrum, from banking to footwear, there are some industries that should be particularly attentive to such changes and become especially in tune with the needs and wants of its customers—such as insurance. Insurance, after all, represents a relationship with policyholders and their beneficiaries, a sustained commitment to supporting them through tough times. 

It is clear, however, that the financial support provided by insurance payouts, while an incredibly important lifeline to so many families, is not nearly enough in today’s relationships-centered marketplace. If insurers want to address the true needs of their customers, they need to think outside the box and provide added value. 

Insurers’ customers are not just case numbers; they are human beings. And, when insurance companies acknowledge the unique needs and challenges of those they serve, often at some of the hardest times in their lives, the companies benefit themselves, as well.

Insurers can go beyond the payout

So what can insurers do to establish trust with and care for those they support?

In treating their interactions with customers not as sales but as sustained relationships, insurance companies can go beyond payouts to provide beneficiaries with real peace of mind. For example, Sensa, a car insurance company, automatically calls emergency services so that their customers have one less thing to worry about. Some car insurers have also begun offering rental reimbursement coverage, going an extra step to make sure the customer isn’t stuck without transport while waiting for claims or repairs.  

Other insurance companies have incorporated clear social impact goals directly into their offerings, showing customers that their beliefs and commitments are as important to them as their financial security. Insurer Lemonade has implemented a giveback program, charging a flat fee and then giving the leftover money from claims to a charity of the customer’s choice. 

See also: Focus on Evolution, Not a Revolution

Helping with the hidden costs of death

Out of the various insurance verticals, however, the one that most needs to implement such policies is life insurance. Life insurance establishes a relationship over the long term, with a promise to be there for the policyholder’s beneficiaries when they need it the most, when they are experiencing the repercussions of great loss.

Bereaved families often experience not only grief but fatigue and frustration from dealing with complex bureaucratic processes, anxiety and stress from the added financial burden and the potential for even more, including strained relationships and troubled communication. 

In a recent survey of these families, we identified some of the most pressing issues they face. In total, all the logistics around a loss, including planning the funeral, paying bills and taxes, filing the will and handling probate, took an average of 420 hours, or the equivalent of 10.5 full weeks of work.

This is an area where insurers can show up for their beneficiaries in a big way. These burdens are so tough that alleviating them even a little can improve someone's outlook and peace of mind. Providing guidance, resources and tools to help beneficiaries navigate all the complexities of loss, from funeral planning to estate administration, as a supplement to insurance payouts is therefore a relatively simple and low-cost way to cement the trust and loyalty of insurance customers and their beneficiaries.

Conclusion

This new world of deepening reciprocity between businesses and consumers allows us to imagine a new role for insurance, one that makes good on the promise of what is, for many of us, one of the longest relationships we will have with a corporation -- a connection that doesn’t simply involve cash payouts but that directly addresses the situation of the families whose claims it is paying.  

It’s the kind of pairing we expect to see more and more of in the years to come: a relationship that goes beyond the transaction, to bring families not just the money they need at a difficult time but care and support through all the things they need it for.

In today’s highly competitive job market, attracting the right talent is not just about money. It isn’t necessarily about offering flextime or office perks, either. For most would-be employees, choosing the right company is about finding deep, meaningful engagement. People want to do work that is interesting and purposeful. They value an organizational culture that places a premium on creativity, thought leadership and innovation.

What’s the alternative? Stagnation. Boredom. Slow decline. As Will Rogers famously said, “Even if you’re on the right track, you’ll get run over if you just sit there.”

Companies that lack innovation are stuck managing the status quo. Employees naturally want to be part of a winning team. The best organizations in the world strive for continuous improvement. They generate new ideas. They are populated by creative innovators, not by mere managers.

We’ve been hearing a good deal lately about the so-called Great Resignation. A tectonic shift is taking place as workers take stock of their lives and their careers. The COVID-19 pandemic has been a catalyst for change. People are leaving. Some are taking early retirement, others are reordering their priorities and seeking new opportunities. For employers, that has meant increased competition for talent.

In the context of this shift, innovation matters more than ever. This isn’t to say that innovation should exist simply for its own sake, of course, but it’s an important ingredient in any successful corporate culture. Businesses succeed by developing superior products, by being more efficient and by mustering the right mix of resources. In all of these endeavors, innovation matters.

Data-Driven Innovation

Without a doubt, the present wave of innovation is fueled by data. That’s true across every industry and in virtually every business function within large organizations. We’ve moved well beyond the realm of speculative projects that are simply aimed at exploring new technology. Today’s analytics, artificial intelligence and machine learning initiatives are delivering measurable real-world value, often with remarkably rapid ROI.

We have more data available to us than ever before, and it’s coming at us with lightning speed. Processing power and efficiency have improved vastly in recent years and continue to do so. Storage costs have plummeted. Cloud technologies are providing elasticity that makes computationally intensive processes affordable and reasonable. Data governance has emerged as a core requirement for any sizable enterprise. All of these ingredients are coming together to help us master the deluge of new data.

There are important qualitative changes happening, as well. We’re better than ever at ingesting information and discerning its meaning. Natural language processing provides a window to unstructured and semi-structured data, enabling machines to make sense of human communication. We can discern sentiment, extract relevance and discover anomalies. Advanced analytics, AI and machine learning are changing virtually everything. Top-performing companies are riding the crest of this innovation wave, and their employees know it.

What should insurers do in the face of all of this? Embrace it. Use it to build competitive advantage. Identify the business cases that can drive value in your organization and commit to implementing technology aimed at producing game-changing results.

As you do, be mindful of how the effort benefits your organizational culture. Make innovation integral to your corporate DNA. As the Great Resignation unfolds, innovation will increase your organization’s capacity to attract top talent.

See also: Innovation Is Really Happening

Innovation and the Great Resignation

Imagine you’re an ambitious job seeker with multiple options before you. Now, imagine you’re employed at one of the industry laggards, slow to innovate and mired in bureaucracy. You’re offered a job with a competitor that is challenging the status quo. What do you do?

In today’s world, top talent will choose innovation nearly 100% of the time. It won’t be the sole factor, but it’s a very important element. In the past, this inclination might have been offset by the prospect of long-term job security, but that simply isn’t true anymore. In today’s world, slow-moving companies don’t offer security — just gradual decline.

Talented people don’t want to be part of that decline. The best ones always have other options. Technology innovation offers companies a pure win-win scenario, delivering tangible business value while fostering a high-performance culture with far-reaching benefits to you, your customers and your employees.

As first published in Carrier Management.

Workers injured by picking up a bag of French fries, hitting a bowling ball with a sledgehammer and getting a snack out of a vending machine are just three of the wackiest workplace injuries, according to Evotix, which provides health and safety solutions.

While most of us think of the workplace as a hub to network, host meetings and build our careers, there are hundreds of thousands of accidents - many of them serious - in workplaces every year. The International Labor Organization reports that, every year, 2.3 million men and women experience accidents while working.

The majority of workplace injuries are slips, trips or falls; handling, lifting or carrying items; or being struck by moving objects, but there are many unexpected injuries, such as getting hit by lightning or falling overboard. Here is Evotix's list of seven downright peculiar workplace injuries:

1. Lighting Strikes Indoors

With the chances of being struck by lightning being about one in 300,000, the odds are marginal to begin with. Yet Robert Jones, a PE teacher at Walker Elementary school in Northport, Alabama, was struck when he was sitting inside. (He made it out alive.)

The Centers for Disease Control and Prevention says about one-third of lightning-strike injuries actually occur indoors. For the best protection during a storm, make sure you have some distance from big windows!

2. Saving the French Fries

When a small bag of French fries slipped from a manager's hands at a McDonald's, she impulsively bent over quickly to catch the bag before it hit the floor. In doing so, she hurt her neck and filed a claim against the fast-food giant. 

Despite this seeming like an accident unrelated to any breach of safety procedures, she was awarded the claim because of the nature of the incident and because she had to quickly serve customers who were waiting in the drive-thru line.

Would you have saved the fries?

3. Bowling Ball Target Practice

What co-workers get up to in the workplace can be bizarre. This is a cautionary tale of what could happen when a sledgehammer and a bowling ball meet on a construction site.

Some workers attempted to kill time while waiting for a delivery of a truckload of asphalt. A worker was challenged to see if he could break apart a bowling ball found in the parking lot. Before doing so, he was warned by his foreman to "knock it off," or "stop."

He continued -- and pieces of the bowling ball shattered, causing him to lose an eye. The court decided against his claim due to positive-work environment violations.

See also: Bring Certainty to Remote Injury Claims

4. Falling Overboard

Have you ever fallen into freezing cold water? Well, an unlucky news reporter did -- on national television. As the news anchor went to interview someone on a boat, she slipped backward and fell into the water. She was fine, but the segment was broadcast across the Netherlands before going viral on social media. (You can see the clip here.)

Drowning is the third leading cause of unintentional injury death worldwide, so it's important to have proper procedures and safety measures in place.

5. The Vending Machine

We've all experienced the frustration of having a snack stuck in the vending machine after slotting our coins into it. When one employee was seeking help from a co-worker to retrieve his pack of crisps, the worker fractured his own hip.

If there's one lesson here, it's that, when it comes to man vs. vending machine, the vending machine usually wins. If you're having trouble retrieving your items, contact the maintenance team.

6. Video Camera Superglued to a Face

In retail stores, there are countless accidents at work. One of our favorites concerns a worker at an electrical store in the U.K. who managed to superglue a video camera to his eye.

Some children came into the store and broke a video camera, so the employee was instructed to superglue the lens back on. Testing the camera, he realized it was superglued to his eye.

At the hospital, he was told to stop filming. He had to admit that he wasn't recording; he just had the camera glued to his eye. 

7. Feeding a Bear Under the Influence

If feeding bears is your job, it's probably not a good idea to get high before work. An American working in a bear enclosure did just that and ended up getting bit on his backside.

The man admitted he smoked a joint before feeding the animals at Great Bear Adventures, a privately run park where black bears and grizzlies roam outdoors. While the man was in the bear enclosure, a grizzly named Red attacked and severely injured him.

The court agreed that his decision to smoke weed was mind-boggling and stupid but also awarded him compensation to cover his medical expenses.

An image of an injured Ukrainian social media influencer staggering from the rubble of a maternity hospital struck by Russian forces has become a new symbol of the global disinformation wars. When the shocking image of Marianna Vishegirskaya, who is known as @gixie_beauty on Instagram, went viral, a post on a Russian state-controlled site, ironically called “War On Fakes," claimed that Vishegirskaya was actually a “crisis actress” paid to play the part. Using this post as a launchpad, the Russian embassy in the U,K. tweeted out the link, along with claims of “#fakenews,” numerous times. Twitter quickly took down the tweets, and numerous Western media outlets wrote articles debunking the claim.

Some social media watchers claimed this as a victory against disinformation. If so, it is a small one, and it is certainly not a blueprint for larger success. To fix the problem of disinformation, we need to think about the system that makes it so easy and profitable to sow inaccuracies at such a low cost. The good news is that there are a number of concrete steps governments and social media companies can take to rein in disinformation without reinventing the wheel or killing social media.

The Disinformation War Is a Forever War

Governments have always used propaganda to serve national and ideological ends. The U.S. and its allies created news outlets such as Voice of America (VOA) and Radio Free Europe (RFE) to combat Nazi and Soviet propaganda. World War II and the Cold War were far tidier information wars, with radio towers providing the primary broadcast power. Through these efforts, the West learned one of the key lessons in fighting propaganda: Provide credible, accurate information in an accessible format.

The internet, ubiquitous connectivity and social media have radically changed the rules of information warfare. The internet made it inexpensive to create seemingly credible content. Global internet access made it easy to achieve mass distribution at a low cost. Social media companies changed the way we consume media, applying machine learning and algorithms to modify our news diet and drive us to increase our interaction with the news.

The idea of generating interest by presenting news of great extremity is not new. “If it bleeds, it leads” has long been an accurate description of the media's priorities. The three above factors can combine, however, in ways that harm society. Digital media production made it easy to create disinformation that looked like news. It made it so easy, in fact, that numerous businesses unaffiliated with any government cropped up in places such as Macedonia to create bogus political content solely to generate clicks. The internet and social media algorithms distributed disinformation widely and simultaneously amplified our predictable responses, thereby encouraging further creation of disinformation. Russia and China have eagerly embraced the new reality of information warfare, even targeting Canadians with relentless anti-U.S. propaganda.

For example, the Wagner Group, a Russian mercenary company, frequently uses disinformation to prop up unpopular dictatorships in Africa and other regions by aiming disinformation at political opponents with the goal of causing political unrest and even violence. In Mali, this is part of Wagner's business strategy and Russia’s political strategy, which aim to both profit from political strife and undermine stability in regions that are strategically important to the U.S.

Because disinformation is so inexpensive and easy to create and distribute, the West is now engaged in an unwinnable game of “whack a mole." Asking the large social media companies to radically alter their business models is not an option; these platforms were designed from the ground up for virality. Banning social media, too, is not a solution. Countless people use social media in ways that benefit them, and society uses social media as a tool for collective action and creative expression.

There are, however, ways to tweak the algorithms and handle disinformation that might break disinformation’s hold on our information sphere. The U.S.’s effective “pre-bunking”—releasing intelligence to counter Russian disinformation—has completely changed the information battlefield against Russia and provided crucial lessons on how to fight and win this broader information war. What the West needs now is a cohesive disinformation warfare strategy and machinery that works in partnership with social media companies to reestablish a healthier information sphere.

See also: How to Put a Stop to AI Bias

Deliberation as Choice Architecture

In their book, Nudge, Richard Thaler and Cass Sunstein outline a concept of encouraging actions through “choice architecture,” which affords users sufficient freedom but nudges them in a desired direction. In the case of disinformation, the desired outcome is less sharing of factually incorrect content. There are a number of ways to inject friction into the sharing process to slow it down. Twitter already asks users whether they want to share articles they have not actually clicked on and read. Facebook could easily inject an “are you sure you would want your mother to read this?” prompt to make users stop and think before they hit "share." Alternatively, if a user has never interacted with an account that they are sharing content from, social media platforms could ask: “You have never read or retweeted anything originating from this account. Do you trust it?” The general goal would be to encourage people to stop and think before they share.

Introducing Costs to Bad Actors

There are other ways to introduce costs to bad actors, the simplest being removing any content they post from amplification algorithms. For example, social media companies could have a “three-strikes” policy that de-amplifies any account with three reported violations for disseminating inaccurate content. This doesn’t mean that they can’t post or that people directly following them cannot read their content. But it throws sand into the gears of virality. And de-amplification will destroy the business models of commercial entities that attract customers through disinformation. A more drastic step would be blocking commerce on these sites or accounts, which often profit by selling T-shirts and other merchandise. While social media platforms are already responding to complaints about disinformation, giving low-status, low-pay content moderators full-time jobs, better pay and more power to enact more rigorous penalties would go a long way toward imposing costs on disinformation.

Pre-Bunking Everything, Everywhere

The two suggestions above focus on changing algorithms to reduce disinformation. An equally powerful change would be enacting a policy of global “pre-bunking.” Over the course of the war in Ukraine, the U.S. government has controlled the information space in Europe by electing to publish intelligence to preempt well-worn Russian disinformation narratives. The very act of saying publicly what intelligence agencies hear secretly altered the equation, defanging Russian disinformation in the West and establishing a baseline of factual information. In the U.S. and the West, pre-bunking robbed Russian intelligence agencies of the ability to sow doubts about the facts on the ground and get ahead of public perception by spreading mistruths on social media unchecked and unopposed.

This was a discrete preemption effort, but it should become the norm. Wherever bad actors are spreading disinformation, the U.S. is likely gathering intelligence about what is happening. Just as the U.S. spends hundreds of billions on warships and planes, it should invest in an information warfare machine focused on continually preempting disinformation machines. In comparison with the costs of military gear, the price tag would be a pittance. The loss of secrecy might pose some risks and should always be a considered choice. But in an era of open-source intelligence, when private satellites provide military-grade photographs and information warfare plays out in real time, sharing more and not less with the public will generate goodwill and credibility while also advancing Washington’s geopolitical goals. For social media algorithms, pre-bunking can serve as a counterbalance against disinformation and as a ballast to anchor the information space rather than allowing a continuous loop of reaction to false posts and stories.

What technology takes away, technology also gives. Unthinking adoption of algorithmically driven content engines has made it easier than ever to create and spread disinformation, and smart changes to those algorithms could yield disproportionately beneficial results. Over time, people may even begin to think differently about how and what they share, and new business models may emerge that are less reliant on blind clicks and the hijacking of raw attention. Disinformation will always be with us—it’s part of the world of information exchange. Whether we let disinformation dominate our information sphere and drive the conversation depends on providing better, broader choices and feedback by fixing the algorithms that enabled single actors to dominate the information sphere in the first place.

This article was written by Vivek Wadhwa and Alex Salkever. They are the authors of The Driver in the Driverless Car and From Incremental to Exponential: How Large Companies Can See the Future and Rethink Innovation. Their work explains how advancing technologies can be used for both good and evil, to solve the grand challenges of humanity or to destroy it.