Dr. John Sviokla is co-founder of GAI Insights. He previously was a strategic adviser at Manifold and former senior partner and chief marketing officer of PWC. He has almost 30 years of experience researching, writing and speaking about digital transformation — making it a reality in companies large and small. He has more than 100 publications in many journals, including Sloan Management Review, WSJ and the Financial Times.

Paul Carroll

To jump right in: You and your colleagues are living on the cutting edge of generative AI, so what are some of the latest examples you’ve seen of powerful uses?

John Sviokla

There’s amazing work being done at Blue Cross/Blue Shield of Michigan. About seven years ago, they began moving beyond functional excellence to build a platform that set them up to offer all sorts of new services to keep customers healthy. 

Ten years ago, they probably had 1% or 2% government business. Now it’s more like a third, and something like 30% of the premium dollars for Medicare Advantage are going to preventative care. That can even mean things like helping pay a light bill or getting someone a pedicure, because it turns out that when they give away pedicures they discover necrosis [dead flesh] far earlier, before it can lead to all sorts of complications. They save hundreds of thousands, if not millions, of dollars. 

When AI came along, they were primed to take advantage. They now have three generative AI products. One gives customers an idea of what benefits are available to them and allows for member self-service. This is super important, given how many benefits there are. One improves members’ security profile, which is needed because of increased access. The third provides an ability to review and manage contracts, which are putting pressure on the insurer because of all the new supply of services and providers, both medical and nonmedical, across the platform. 

They’re not only offering those products themselves but are making them available to the other Blues.

Jerry is going wild with customer service. We’ve written up a case study. They have about 5 million customers and help with insurance selection, refinancing and other issues related to cars. Since April of last year, they’ve been using generative AI to handle customer service via chatbots and text. They went from just over 50% of people getting responded to within a day to 100%. Most now get a response within 30 seconds. They've gone from 100% of issues going to a human being to 89% going to the robot—and the 11% that remain with humans get there faster for obvious reasons. 

They're way more scalable. They can grow this business without having to grow the customer service function. ROI is about $4 million a year.

Drug companies say generative AI has decreased the amount of time from discovery through clinical trials by 60% to 80%. J.P. Morgan claims over 300 productive capabilities. Studies from BCG, Harvard, MIT and so forth find increases in task productivity of anywhere from 10% to 15% at the low end to as much as 50% to 60% on software. 

Paul Carroll

You and two colleagues recently published a piece in Harvard Business Review about how companies aren’t designed for generative AI. What do they need to do so they can accommodate these capabilities?

John Sviokla

The big thing is that you have to redesign work to account for the fact that employees have a new conversation partner: one made of silicon. 

To design for that new dialogue, you have to understand the task at hand, work to discover the new interaction patterns between employees and AI and then codify those patterns and spread the frame. But this isn’t the same as a linear decomposition of tasks, which is how most systems development is done. This is much more about shared discovery, because as the individual is doing the work their job is changing. 

They’re conversing with a different sort of conversation partner, one with a hive intelligence and encyclopedic knowledge. And the conversation is codified into the knowledge base. 

We've got a turbocharged conversation.

Paul Carroll

How do you recommend people get started?

John Sviokla

I think you get started with low-risk parts of the organization. That’s certain kinds of customer service and certain kinds of inquiry, such as people looking for a job or employees with questions on benefits or policies. 

Then you think of this like the Quality process. You want a certain number of white belts, green belts and black belts. White belts, very much like in Quality, have some basic understanding, such as what an LLM [large language model] is and how prompting works. They need to know how to train a model. A green belt knows how to teach a white belt and has managed at least one project to completion. Black belts get more into the technology. The white belts are basically smart users, green belts are kind of in the middle and black belts are closer to people who actually start to build stuff. 

Paul Carroll

You’re always working on the next idea. Where do you think you’ll go next?

John Sviokla

We need to get past sequential processes and understand how to redesign team-based work, so I’m thinking about collective cognition and how to take advantage of our new partner, the machine. We’ve had 120 years of automation of the physical world, 67 years of automation on structured data and 18 months of automation of unstructured data. There’s lots of opportunity. 

Paul Carroll

I get excited about all the unstructured data that’s becoming available. I see companies using AI to make claims or underwriting more efficient, but getting access to all the unstructured data out there will take us to new levels of understanding.

John Sviokla

A lot of times, underwriters are working on descriptions that are pretty thin, that are historical. Well, this is about enriching those descriptions—understanding the semantics, understanding the functional interaction, being able to probe and assess all the dimensions of a risk. You're really expanding the bandwidth of underwriting radically. 

Paul Carroll

Thanks, John. It’s always great talking with you. 

Process: a series of actions or steps taken to achieve a particular end.

From time spent in the military, I will always remember the importance of process. Without process, there is no consistency to approach, no guardrails when times are tough and no foundation to measure against and improve upon. Having moved to the civilian workforce – from the financial industry to the shifting sands of cyber security within the insurance space – I see that that same principle applies. The ability to formalize processes is critical.

In 2024, the cyber situation is dire. According to a report from ESG and the ISSA, 70% of cybersecurity professionals say their organization is affected by the shortage of cybersecurity skills . Meanwhile, the global cyber insurance market tripled in volume in the five years that ended in 2022, and the Insurance Information Institute reports that global direct written premiums for cyber insurance are projected to escalate to $23 billion by 2025.

A skills gap. A surge in premiums. A growing demand. Something has got to give. The world of cyber resilience (which includes cyber insurance) continues to reinvent itself as it changes to mitigate new threats and bad actors. The industry must maintain pace with this evolving landscape. Process is at the heart of this equation.

See also: Top Global Business Risks in 2024

Refining policies and grappling with pricing in this industry’s infancy

It wasn’t too many years ago where the relatively new market of cyber security was thrust into the spotlight following an explosion of digital transformation, at a state and national level. 

The first (and very limited) cyber insurance policies were written in the late nineties and early aughts. Cyber insurance policies were historically centered on security liability, data loss and unauthorized access. 

The scope of cyber continues to broaden as digital transformation and connected technologies overhaul every facet of our lives, from museums opening online collections and archives, to restaurants taking a digital, omnichannel tack with orders, and every business in between. You’ll be hard-pressed to find any industry that doesn’t have digital tentacles attached, ranging from bring your own device (BYOD), to remote devices, to digital subscriptions and more. All this to say that what makes up policies and how they are priced are constantly moving goalposts. 

As cyber insurance matures, and businesses and carriers better understand pricing and risk, this equation will (we hope) balance out. However, to offer more personalized policies and align coverage with unique business demands, the industry will have to shape up quickly. Carriers should look at their underwriting, policy and claims processes to see where efficiencies can be found, where new data can be leveraged and where creativity can support more competitive products. This isn’t the silver bullet to solve all of the industry’s growing pains, but it’s a welcome start.

See also: Top 10 Challenges for Data Security

Lowering demand through education

It’s also important to flip the script from carriers to insureds. This is a team game, with cyber insurance a borderline non-negotiable for any business, agency or organization. The demand is rightfully high. This shouldn’t change. However, there are steps that security leaders can take within their organizations to help the industry at large. Putting the right processes in place can help the industry – as a whole – grapple with this challenge and potentially lower premiums.

Business continuity and disaster recovery strategies: Too often, organizations have these documents or strategies in place, but they are seldom representative of an actual incident that might take place – let alone practiced in such a way. This needs to change. Organizations must show a holistic approach to cyber resilience, from continuing training to technical support to flexible policies that support recovery and damage mitigation. This extends beyond your organization’s four walls and into your partner network, as well.

Balancing domain knowledge with technology expertise: Security leaders will be tasked with showing that they understand not only the security landscape but the business landscape they are supporting. Simply doing security for what is believed to be security’s sake is a path to trouble. The better security leaders can articulate how their processes, programs and protocols map to the specific business challenges at hand, the better their chances at finding and paying for the right coverage. Make sure your organization – and your enterprise ecosystem – is evaluated on having a blend of expertise.

Better measurement of what success looks like: Part of the industry’s maturation is understanding how to measure or demonstrate value to help paint the full security picture. This information can also be used to better evaluate risk and to find, build and price a policy. Security leaders are taking a more prominent seat at the table. Just as a sales or operations or technology leader shows value through key performance indicators (KPIs) and performance measurement, so should security leaders. A good place to start is stacking up against widely accepted security frameworks and training programs. 

Shifting the conversation from cyber security to cyber resiliency

This has been slowly happening, but cyber resiliency will be the way forward – underpinned by the right processes. Resiliency is about more than that one moment when a problem occurs. It’s what is done before, during and (arguably most importantly) after a threat or failure. 

The cyber insurance, mitigation and resilience conversation is here to stay. And it is far from an easy solve. We must reinforce processes – specifically, collaborative processes – in which all parties involved support the common goal of lower premiums, more accurate policies and resilience through education. 

These are the building blocks to a manageable future.

Artificial intelligence (AI) has transcended science fiction to become a fundamental part of our daily lives. In fields like medicine, we've witnessed AI learning in ways that mimic human cognition, particularly in processing immense datasets at remarkable speeds.

While AI algorithms often surpass humans in data processing capabilities, they lack essential human qualities such as empathy and creativity, which are integral to nuanced decision-making in fields like healthcare. The ultimate aim of AI is to replicate human behavior and perform tasks traditionally executed by humans, but there are significant questions about its feasibility and ethical implications, especially in domains where responsible AI practices are crucial.

A report published by McKinsey in early 2023 suggests AI could automate up to 30% of work hours for U.S. employees by 2030, with a more moderate impact expected in healthcare. The report indicates that allied health professionals may see a 4% to 20% increase in automated tasks, while healthcare professionals could expect up to 18% of their work to be automated with AI by 2030. Rather than view AI as a replacement for human expertise, we should see it as a complement—a synergy between humans and computers that leverages the strengths of both. 

Many, myself included, believe that the optimal interaction between humans and AI in healthcare involves a blend of human expertise and AI augmentation. This balance can automate routine tasks that often burden healthcare professionals, such as electronic medical record (EMR) documentation, administrative reporting and even triaging radiology scans. Additionally, AI can assist in analyzing large datasets, providing valuable insights for physician oversight and decision-making.

See also: Streamlining Medical Record Reviews Via AI

AI stands as a transformative force, offering significant advancements in the operation of medical devices and diagnostic capabilities. Deep learning algorithms, for instance, have demonstrated the ability to interpret CT scans at a pace far exceeding human capacity. 

Despite its immense potential, AI in medicine encounters several hurdles that warrant careful consideration. Privacy concerns loom large, as the use of patient data for AI algorithms raises questions regarding data security and patient confidentiality. Moreover, ethical biases embedded within AI algorithms pose a significant challenge, as they have the potential to perpetuate or exacerbate existing disparities in healthcare delivery. 

One of the most pressing issues surrounding AI in healthcare is the lack of comprehensive regulatory oversight. Unlike medical devices or pharmaceuticals, AI software is dynamic and continuously evolving, making it challenging for regulatory bodies such as the FDA to monitor and oversee effectively. As AI technology advances rapidly, regulatory frameworks struggle to keep pace, resulting in a regulatory landscape that is fragmented and often inadequate. 

In response to these challenges, proposals for public-private assurance labs have emerged. These labs would serve as independent entities tasked with assessing the safety, efficacy and ethical implications of AI applications in healthcare.

My journey into the realm of AI was marked by collaboration and a deep dive into the complexities of medical diagnostics. Teaming up with experts from Harvard Medical School, we embarked on an ambitious project to integrate AI with surface electromyography (EMG) readings, aiming to enhance diagnostic accuracy and efficiency. Initially, the allure of AI lay in its potential to streamline the interpretation of surface EMG data, a task traditionally requiring specialized expertise. However, as our endeavor progressed, we encountered the intricate nature of AI and its application in medical diagnostics. 

Surface EMG interaction involves a multitude of variables and considerations,  which needed to be broken down into discrete steps. From analyzing muscle activity to interpreting data points, each step in the process presented unique challenges that AI alone struggled to overcome. It was the human guidance for the AI process that was necessary for the better long-term outcomes. 

The complexity inherent in muscle activity analysis necessitated a comprehensive understanding of various factors, including muscle groups evaluated, movement expectations and spatial-temporal and functional  integration. It became evident that AI, alone, while powerful, lacked the nuanced insight and contextual understanding inherent in human decision-making. 

As we delved deeper into the nuances of AI integration, we recognized the critical role of human guidance in the process. Unlike conventional algorithms, which may operate within predefined parameters, AI in the medical domain demands monitoring and refinement. Factors such as medical history, demographics and individual characteristics must be meticulously accounted for to ensure accurate diagnosis and treatment recommendations. Achieving a seamless integration of AI into medical practice requires not only technological prowess but also human expertise to navigate the intricacies of patient care. 

Our journey with AI underscored the importance of recognizing its limitations and the indispensable role of human involvement in shaping its evolution. This early exposure to AI in the context of musculoskeletal (MSK) conditions underscored the intricate nature of developing AI solutions in medicine. While AI holds immense promise in revolutionizing medical diagnostics, its efficacy ultimately hinges on the quality of data inputted and the oversight provided by human experts. 

Our AI integration road map is grounded in a comprehensive understanding of the complexities inherent in EMG integration and medical indications. Collaborating with experts in these fields, we prioritize a phased approach to algorithm development, recognizing the need for iterative refinement. 

See also: Data Science Is Transforming Public Health

In my experience, the choice between deploying AI or relying on human expertise necessitates a thorough consideration of unintended consequences. While AI presents vast potential, its reliance on training data introduces the risk of bias, a factor that often goes unnoticed in decision-making. Moreover, AI exhibits a slower adaptability to unnecessary changes, lacking the imaginative and innovative capacities intrinsic to human cognition. Humans, on the other hand, possess a distinctive aptitude for exercising discernment, multitasking proficiently and comprehending information in nuanced ways beyond the capabilities of machines. 

The integration of AI into healthcare holds immense potential to improve efficiency and outcomes. However, it must be approached with caution and a keen awareness of the ethical considerations involved. By embracing a collaborative approach that combines human expertise with AI augmentation, we can harness the full potential of technology while prioritizing patient care and safety.

The rate at which data science techniques are developing and being adopted is increasing faster than insurers are able to develop their own understanding of the risk governance and ethics needed.

To make matters more challenging, two distinct groups operate within most insurers on the front line of data science, often in conflict rather than in harmony: data science teams using cutting-edge techniques without the necessary understanding of their organization’s risk frameworks, and insurance leaders who have limited experience with the latest advanced analytics. This internal disconnect leaves insurers and individuals that work for them exposed to risk.

Finding the right balance between governance and control, while still advancing the adoption of data science and the value that it creates, has become the magic middle ground upon which insurers have set their sights. 

See also: Why Becoming Data-Driven Is Crucial

Bias

As increasingly complex models are used, a key risk for insurers to consider is bias -- an issue so far  not fully understood and appreciated by many firms. When individuals or groups are differentiated from others based on particular characteristics, insurers need to understand why. Is the bias due to the data collected not representing the entire population? Is it caused by potentially flawed human decision-making that is represented in the data collected? Or was the bias introduced due to the artificial intelligence (AI) and machine learning models trained on the data? Is the inherent model form being used responsible for reinforcing the existing bias or even creating new biases?

The ability to detect hidden biases is essential to enabling appropriate strategies to measure, monitor and manage bias. Instead of thinking about bias at every stage of the model building process -- when an insurer first explores their data, when they build a model and when model outputs are used in a business decision – data scientists too often consider the risks as an afterthought. 

Choosing the right algorithm that will help an insurer find the optimum balance among interpretability, transparency and predictive power is another essential capability. There are a number of custom algorithms being developed in the market. For example, layered gradient boosting machines (LGBM) capture the same predictive accuracy of a GBM, while providing a much greater level of transparency and interpretability.

Open source risk

In recent years, open source adoption has seen unprecedented growth. While open source allows incredible flexibility and innovation, it also exposes an insurer to more risk, particularly relating to governance and security. Besides the potential for malicious code hiding in open source packages, key person dependency is another risk created by having either just one individual or a small team responsible for building and maintaining code. 

Large language models (LLMs), such as ChatGPT, are examples of technology evolving and being adopted in a hurry. However, the governance risk and control frameworks have not kept pace, creating significant risks relating to data privacy and intellectual property. 

Through the use of LLMs, an insurer could potentially lose sensitive and proprietary data. There is potential to have no or limited control over how the data is used, including being used by competitors later. 

Another risk concerns hallucinations, which refer to the tendency of LLMs to produce text that appears to be correct but is actually false. This could be driven by bad prompts or simply due to an underlying weakness in the model, delivering results that are wrong but are presented with a lot of certainty. Reputational risk for an insurer is high if the data or model is used improperly.

See also: Data Science Is Transforming Public Health

Taking control

At the end of the day, the stability of the open source code is in the insurers’ own hands. They alone are responsible for making sure they meet their business needs. Therefore, it is important that an insurer clearly delineates roles and responsibilities to avoid confusion. Defining who is making which decision ensures that better accountability, visibility and opportunity to challenge decisions are in place at every level. 

Open source offers real potential to contribute to a more efficient and innovative insurance market. However, insurers must first address two critical decisions: what they should use open source for to gain an advantage; and then how best to integrate open source in such a way that good governance and control are in place, creating an optimal balance. 

Data science is spreading quickly. If insurers want to compete in this new AI-driven world, they not only need to simply adopt data science but also do it in the right way. This means a gradual evolution of governance to ensure the right oversight, alignment to internal values and regulatory compliance are achieved, combined with an evolving risk management framework to anticipate and mitigate future risks.

The first quarter of the year just ended, and so far 2024 feels a bit different than 2023. U.S. economic indicators are fluctuating month to month, and policymakers feel we are not out of the recessionary woods yet. The insurance industry has struggled to stay profitable under destructive weather events and ballooning costs. Insurers are striving to adapt to the new normal of continuous crisis, continuing digitization and evolving customer expectations. 

1. Developing Risk Resilience

In the middle of winter, firefighters raced to contain the Smokehouse Creek Fire, the second-largest wildfire in Texas history, raging in the panhandle. As we witness this early challenge posed by natural disasters, it prompts us to contemplate what lies ahead as we approach other extreme weather seasons, such as hurricane and wildfire seasons. After years of record natural disasters ushering in the era of global warming, the insurance industry is now bracing for further calamities. These unanticipated risks are not the only areas insurers need to watch out for. There is a new breed of non-natural disasters, ranging from cybersecurity to machinery malfunctions (such as Boeing's safety issues), that have prompted insurance companies to look for better ways of managing these exposures and enhancing risk resilience. 

To fortify risk management, insurers should combine traditional practices with advanced technology tools. These tools enable insurers to monitor and stay abreast of evolving conditions while efficiently gathering data to identify risks and vulnerabilities. By integrating technology into their risk management strategies, insurers can streamline processes, improve data analysis and address emerging threats, ultimately enhancing their ability to mitigate risks and protect their business and policyholders.

See also: 20 Issues to Watch in 2024

2. Insurance GPT: AI’s Impact

If 2023 proved anything, it was that large language models (LLM), aka generative AI, are not going anywhere. Insurance companies must understand the numerous use cases AI can have on the industry, specifically on underwriting, insurance product sales and customer service. As in most sectors, gen AI tools are great for freeing up various functions for higher-value tasks like risk mitigation, by automating numerous manual tasks, from summarization of documents to synthesizing large volumes of data. 

Adoption of the LLMs is improving workflows, boosting productivity, and unearthing efficiencies. It may be a while until we understand the depth of impact of LLMs on our industry, but insurers will need to integrate them into our daily practices sooner rather than later to stay competitive. Gen AI is in its infancy, where everyone is essentially a beginner, but experts are emerging. This presents a remarkable opportunity for organizations to carve out a niche and become leaders. For instance, Marsh McLennan has become an early adopter and launched a generative AI tool last year to help its 85,000 employees worldwide to streamline their work processes. Moreover, in an industry facing disruption, busy trying to recalibrate its role in society, LLMs could contribute to transforming the customer experience (CX) game.

3. Rate Increases

Premiums have already been increasing for four years, which makes elevating CX much more critical. Both the $32 billion in P&C net underwriting losses in January-September 2023 and P&C net combined ratio of 103.9% are decidedly unpleasant numbers. Fitch Ratings regards 2024’s outlook as “neutral,” and “the market combined ratio is projected at slightly over 100%.” While insurance companies are reinventing themselves with a laser focus on closer customer engagement, they know they need to increase premiums. Insurers are operating in a Catch-22 of sorts. They are feeling the pressure to reduce costs and raise rates in personal and commercial lines within insurance policies – but it’s hard to justify without adding value and becoming more transparent. Leveraging generative AI and other analytics technologies, combined with partnering with insurtechs, may be key to finding operational efficiencies.

4. Managing Social Media

While AI chatbots and other productivity tools are infiltrating every aspect of our lives, social media has not exactly faded into obscurity. In fact, consumers are turning to social media channels and online sources as an information source to dig for details on matters that affect their daily lives. With social media channels remaining as an important touchpoint, insurers still need to provide useful content across all communications channels to guide customers along their buying journey. This can prove problematic, hampering the underwriting process when consumers turn to social media to understand insurance policy pricing. This is a delicate line to walk as insurance companies cannot neglect their social media presence and must ensure their processes are completed properly. Conversely, insurance companies can still glean actionable data from consumers interactions with social media. 

See also: Insurance in 2030: What Does the Future Hold?

Steering the Year Ahead

Dealing with the unexpected is a routine challenge in the insurance sector, and need to do so is likely to escalate in 2024. To navigate successfully, insurers must prioritize adaptability and adjust their strategies to match the dynamic landscape of the industry. Maintaining an aggressive approach to risk management and embracing innovation and technology will be essential for staying ahead of emerging challenges and seizing new opportunities.

This week, I'll quickly hit three themes, including an audacious new form of cyber attack and General Motors' decision to back down on sharing data with brokers from GM vehicles, in the face of a recent uproar. But I'll start with a step for homeowners insurance toward a "Predict & Prevent" business model and away from the traditional repair-and-replace approach.

Realtor.com said it will provide projections on climate risk for each listing. Heat, wind and air quality scores will predict what an area will experience, going out 30 years, supplementing existing data on fire and flooding, so home buyers can know more about what perils -- and high insurance premiums -- they may face as the years go by, BEFORE they make the purchase. 

Economists at Realtor.com say some $22 trillion of U.S. residential properties are at risk of ‘“severe or extreme damage” from flooding, high winds, wildfires, heat or poor air quality. And First Street Foundation, which provides the data that is the basis for Realtor.com's analysis, says nearly 36 million homes -- a quarter of all U.S. real estate — face rising insurance costs and reduced coverage options due to mounting climate risks, First Street Foundation, a nonprofit that studies climate risks, says 18% of U.S. homes will be at risk of damage from hurricane-strength winds over the next three decades.

The projections will be far from perfect, especially in the early days, but the hope of the Predict & Prevent model is that we can keep adding intelligence to the system and help customers make decisions that let them avoid putting themselves at risk, long before a flood or fire or windstorm can damage or ruin their property. (If you're interested in learning more about what Realtor.com is doing, or about Predict & Prevent in general, I encourage you to check out The Institutes' Predict & Prevent newsletter. You can also sign up for regular podcasts on Predict & Prevent.)

Now on to the brazen new cyber attacks....

The Wall Street Journal reports that cyber criminals have gone far beyond stealing data and are now stealing data that they use to then steal physical goods -- they hack digital exchanges that facilitate shipping via truck and divert loads of products that they hold for ransom. The WSJ says reports of freight fraud more than quadrupled in 2023 from the year before, and fraud-related losses were estimated to be at least $500 million last year.

Criminals hack into an exchange that those shipping products use to coordinate with trucking companies and steal the unique identifier of a trucking company. They use that identity to submit a lowball bid and win the right to ship a truckload or more. The criminals then subcontract with an actual trucking firm that, not knowing it's participating in a crime, picks up the load or loads and delivers the goods to the hackers. The hackers contact the company that owns the goods and demand a ransom. Often, the goods are perishable, such as yogurt, and some store chain is angry about not getting its scheduled delivery, so a ransom is paid before authorities even have a chance to start unraveling the crime.

Everybody seems to be pointing fingers at everybody else. Those in the shipping industry say authorities need to do more to stamp out the fraud. Authorities, who say they don't have sufficient resources, say shippers, brokers and truckers could do a lot to police the problem themselves by watching out for bad actors and coordinating to kick them out of the system.   

Trucking capacity was so scarce as the economy heated up following the worst of the pandemic that many shippers and brokers didn't pay enough attention to making sure that a trucker was legitimate, and fraud may abate as everyone -- including insurers -- pays more attention. Here's hoping. But in the meantime, freight fraud has become a major problem. 

As for General Motors: Following loads of negative publicity (including from yours truly) about customers complaining that they were being tracked without their approval, leading to higher insurance premiums, the company said it would stop sharing data on drivers with data brokers such as LexisNexis and Verisk. That is a smart PR move, but I don't think the issue will go away. GM still faces a lawsuit that the plaintiff hopes will be certified as a class action, and the whole issue of tracking drivers, perhaps without their consent, seems to have drawn the attention of the Federal Trade Commission and of Congress.  

Stay tuned.

Cheers,

Paul

As the first quarter of 2024 winds down, the insurance industry is embracing digitization and recognizing AI’s great promise for improving and simplifying what we do. It’s exciting to contemplate all the potential that technology affords. Yet we can’t lose sight of the fact that technology will never fully replace human connection.

Finding the best ways to use technology is understandably a focus for our industry, but the key to success lies in finding ways for technology, soft skills and empathy to work together. This is best done by investing in technologies that automate lower-level tasks and make employees’ jobs easier, which helps to clear the way for more high-quality interactions with clients and customers. 

Equally important is finding ways technology can be used to cultivate a culture of empathy internally. A workplace empowered by empathy and efficiency allows employees to build strong relationships with both clients and policyholders. By combining human connection with technology-enabled solutions, claims professionals will have more time for fully engaged customer experiences. Think of this as human intelligence supported by artificial intelligence, not as artificial intelligence replacing human intelligence.

Opportunities to improve claims process efficiency with technology should not come at the expense of the human element. 

Insurance is a product that nobody ever wants to use. But claims are inevitable, and when they do occur it’s important that policyholders feel that their insurer is there for them on a human level. If your home or your car is damaged, or if your business operations are on the line, you may not want a robot or an automated text message telling you that everything will be okay. Rather, you want a person to reassure you and give you confidence that things will be taken care of. For a policyholder or claimant who’s just gone through potentially one of the most terrible days of their life and wants to be shown empathy, reassurance is part of the product – and that reassurance needs to come from a person, not a bot.

In contrast to claimants looking for the human touch, technology enablement is perfect for the policyholder primarily looking for simplified self-service solutions or higher degrees of digital engagement in their claim experience. We must be careful, however, not to miss the mark by assuming that we know what someone needs or wants. A claim is a personal experience for everyone, and our role as claims professionals is to meet each customer where they are. With technology, we are better equipped to tailor the approach down to the individual preferences or needs of the customer and their circumstances. The integration of self-service interfaces to the claims process gives each insured the ability to define their own journey – and their claims team the ability to deliver the experience each customer needs and expects. 

See also: Adding Humanity to Life Insurance

Beyond process efficiency, empathetic claim management can also help avoid large jury awards and nuclear verdicts.

Just as the impact of biases can work against businesses, a good claims experience can work in an organization’s favor. With social inflation and nuclear verdicts currently creating challenges for the insurance industry, it’s worth noting that a little bit of compassion can have a significant impact in combatting the anger and fear driving these trends. Claims organizations can avoid litigation and achieve quicker, less costly settlements when they take an advocacy-based approach that prioritizes care for injured parties. It stands to reason that jurors who see the insurance company as caring and empathetic may be less likely to punish them. 

Critical components of an advocacy-based model include ensuring early outreach, offering empathy and support regardless of fault and going above and beyond to demonstrate care. Each of these “human” behaviors can go a long way toward mitigating confrontational litigation. This is all the more reason to resist the urge to over-automate larger value claims processes and to keep empathetic personal connections.

Empathy can be more profoundly extended to clients if it is first demonstrated to employees.

Not only should we look to balance automation with human support for policyholders, but as employers, we must also strive to provide empathy to our own people. Adjusters’ behaviors can change depending on what’s going on in their personal circumstances, and we as an industry must respond to these shifts by showing empathy to our own people, as we ask them to show empathy to others. 

Creating a workplace empowered by empathy is essential to building a resilient workforce. Using human connection as a lens through which day-to-day business practices are viewed and implementing technology that frees up more time for relationship building are two key ways that organizations can sustain and improve empathy in the workplace. Workers aren’t going to be able to focus on empathy when they’re struggling, so it’s essential to prioritize holistic health and ensure that employees can manage the stress that accompanies helping customers in difficult circumstances.

See also: 4 Key Questions to Ask About Generative AI

The goal for 2024: Make empathy and technology work together to empower employees and drive client satisfaction.

As technology matures, it offers increasing opportunities to streamline workloads, allowing employees to focus on clients' claims with more attention and empathy to deliver personalized customer experiences. 

The newest tools offer even more promise for blurring the lines between technology and empathy. AI algorithms can now identify and categorize emotions by analyzing text, speech and other data sources, and this information can be used to increase empathy even in tech-driven interactions. AI-powered sentiment analysis can help organizations to analyze and understand customer emotions, attitudes and opinions, as well as to monitor social media feeds, customer service interactions and customer feedback surveys to identify issues requiring additional response. These tools can also be leveraged to help companies identify bias in their customer interactions. And new call center tools are available that provide real-time coaching and flag potential issues, allowing the agent to focus on the caller, not the script. These tools could also flag the agent to signs of anger, frustration or confusion with the caller so they can course correct during a call.

When human connection is combined with technology-enabled solutions, claims professionals have more time for fully engaged customer experiences. And when employees are feeling their best, they will have the patience and soft skills necessary to be present and thoughtful with clients and customers. Technology that supports and builds time for human interaction should be the priority for the claims industry and will be what drives the industry’s continued success in the coming year.

Emerging tech like generative AI poses new fraud risks for insurers. They must adapt with improved security, detection tools, and data management to stay ahead.

Content: Bad actors intending to commit fraud have always been innovators, finding new ways to defraud insurers and honest policyholders and often staying one step ahead of investigators. Advances in technology, like AI and generative AI, will bring new risks to the insurance industry as criminals search for weaknesses they can use to their advantage.

As technology shifts toward a more collaborative open system approach through the use of open AI programs and other generative AI applications, insurers will need to understand these risks and be proactive to prevent breaches and fraud attempts. A recent Aon report found AI will become a top-twenty risk in the next three years, highlighting the need for the industry to focus on the risks associated with it.

Whenever new technologies are introduced, bad actors search for ways to exploit them. Ring cameras have been hacked, with horrifying examples of hackers spying on people, making death threats, or scaring children through the cameras. A Jeep was hacked as an example of how software updates can be exploited, with the “carjackers” taking complete control of the vehicle as it was being driven. The various smart devices that now fill the homes of many people are often vulnerabilities, including smart TVs, lightbulbs, and thermostats.

The risks generative AI poses are dynamic and will continue changing alongside the technology, which means the industry must try to keep pace with bad actors.

New Risks, New Opportunities From Generative AI

While AI and generative AI use is still in the early stages, the insurance industry cannot ignore the emerging risks that accompany the opportunities. Some of the risks include:

• Data privacy and security concerns.
• Inherent bias built into generative AI applications.
• Ensuring compliance with legal and regulatory requirements.
• Potential over-reliance on AI and generative AI.
• Attacks by hackers on vulnerabilities within generative AI programs.
• Data poisoning when bad actors introduce bad information into AI databases.

FRISS recently released its 2024 Fraud Report examining global beliefs about fraud and actions taken to detect and prevent fraud in the insurance industry. Looking at emerging issues like fraud in AI and other technology, the survey examined how respondents found and prevented application and claims fraud and the tools they use to help fight fraud.

The majority of respondents (59.8%) would like to see their organizations implement automated fraud detection tools. Respondents believe implementing these automated tools combined with increased fraud awareness training and better collaboration between departments would help their organizations better fight fraud.

To fight these risks associated with generative AI programs, insurers can implement tools and strategies designed to detect, prevent, and control fraud.

Ways Insurers Can Help Manage Generative AI Fraud

Insurers will need to stay ahead of trends and changes in technology and use cases to effectively help manage fraud from generative AI technology. Knowing one key risk lies with data privacy means insurers can focus on improving their data security systems to help reduce some of the risks introduced by generative AI.

Another way to strategically fight against generative AI fraud is through the use of a fraud detection and prevention platform. An average of 28.18% of respondents to the FRISS survey said they had no platform currently in place to help detect and prevent fraud. This represents an opportunity area for those without a platform to consider an external or homegrown solution to supplement other tools they already deploy to help fight fraud.

33.82% of respondents worried that keeping up with modern fraud methods was one of their biggest organizational fraud challenges and 39.8% were concerned with data protection and privacy. But the biggest challenge was with data quality, with 61.98% of respondents expressing their concerns about the quality of internal data.

Insurers can focus on these challenges by improving their data security methods and tools. The quality of internal data has historically been a challenge for incumbent insurers as they have tried to analyze their data and draw conclusions from it. Modernizing to a digital platform to detect, manage, and prevent fraud at the application and claims level could be a way to shift to a more predict-and-prevent model when it comes to fraud. To learn more, read the full 2024 Fraud Report, available for download on the FRISS website.

Sponsored by ITL Partner: FRISS

As an insurer, if you’re looking to simplify your core processes and make them digital-ready, insurance management software can help, but, you’ll have to make several key choices, from the many features and functionalities to the implementation plan to training, onboarding and customer support experience and more. 

One of the most-often-overlooked aspects of the software is its deployment format. Selecting the right deployment model is crucial for optimizing operations, ensuring data security and maintaining resilience. In this blog, we talk about cloud, on-premise and hybrid deployment models, along with their pros and cons, to help insurers make a well-rounded decision.

See also: Moving From Legacy Systems to the Cloud

Cloud-Based Insurance Management Software

As the name suggests, cloud-based insurance management software stores, manages and distributes software applications, computing resources and databases over the cloud. Such a deployment model makes the insurance management software accessible to all authorized stakeholders regardless of their physical location. 

Benefits include:

• Cloud-based insurance management solutions are highly scalable and allow businesses to adjust resources, processing power, and other parameters as business needs change.
• Because the cloud-based insurance software company offering this service is responsible for end-to-end platform management, you don’t have to dedicate resources to maintenance, security updates, etc.
• They follow a pay-as-you-use model that can make them more affordable.
• They boast a solid infrastructure paired with disaster recovery capabilities that infuse reliability into insurance processes and workflows.

In contrast, cloud-based insurance industry software struggles with the following limitations:

• Not having control or ownership of the data and infrastructure can make insurers hesitant to try out cloud solutions due to data security concerns.
• Cloud-based solutions rely on a stable internet connection to operate. Any disruptions on this front can suspend or break down insurance processes.
• With insurance deeply ingrained in legacy systems, transferring all the data onto the cloud without losing any of it can be a challenge.
• Relying on a third-party cloud hosting service provider could result in the insurance business getting exposed to technical issues and downtime.

On-Premise Insurance Management Software

On-premise software is a deployment mode wherein the server, software applications and hardware infrastructure, are hosted locally within the organization. On a large scale, server closets, server rooms and server warehouses may be rented out. The software is installed on a single computing device, and the data is stored in a physical location. 

Some of its key advantages include:

• Absolute data control and security, as the insurance company is solely in charge of the data and exercises granular control over it.
• Ownership of data also allows them to adhere to stringent industry regulations more effectively and maintain compliance.
• Because all the software and data are hosted internally, there are fewer chances of disruptions or downtime attributable to external factors.
• Although there is a significant upfront cost, the best insurance software solution on-premise will have predictable and controlled expenses.

Limitations of on-premise insurance management systems include:

• Setting up the insurance software system can be expensive.
• Maintaining the insurance management software will add to the costs and resource-intensiveness.
• On-premise systems lack the scalability to adapt to changing workloads and business requirements.
• They restrict accessibility, as insurers cannot use on-premise solutions for remote teams.

See also: Why Cloud Platforms Are Critical

Hybrid Insurance Management Software

Hybrid insurance management systems combine the elements of on-premise and cloud-based solutions. Such a blend offers flexibility and customization, mixed with security and control. In such a setup, critical or sensitive data may be stored on-premise to ensure control and compliance. Business processes can be deployed on the cloud for greater scalability and accessibility.

This combination offers the following advantages:

• Hybrid insurance software systems are flexible, as insurance technology service providers allow you to choose what parts to store on-premise and what to deploy on the cloud.
• They are efficient in terms of costs. 
• They encapsulate robust disaster recovery strategies to ensure business continuity and reliability, even in the face of disruptions.
• You can customize a hybrid insurance software system to match the unique requirements of your insurance business.

Despite the many advantages, hybrid systems also possess the following limitations:

• Managing and adopting such an infrastructure can be complex, as it requires careful planning and implementation while maintaining consistency.
• Ideally, hybrid models are cost-efficient. However, it is easy to get wrapped up in different features, options and complexities that give rise to cost creep.
• Integrating hybrid insurance management systems can pose challenges while introducing friction or bottlenecks within processes.
• Managing a hybrid environment requires a diverse set of skills, as the IT team would have to be proficient in on-premise and cloud technologies.

Conclusion

There is no black-and-white answer to what’s better -- cloud vs on-premise vs hybrid. The choice eventually boils down to the insurance business’ specific needs, priorities, resources and capabilities. Moreover, the choice may also be dynamic, as what you choose may change with the evolution of the business objective, regulatory norms and long-term business sustainability. As such, insurers also need to know how to overcome common challenges when implementing insurance software.

Policyholders may look back on 2023 and remember the trend of premium increases. However, new technologies such as artificial intelligence (AI) are reducing costs for underwriters, which will translate to more competitive customer rates.

You can expect the remainder of 2024 to bring continued technological improvements powered by AI. Policyholders can insure more assets in less time, while carriers continue to reap the cost benefits of greater efficiencies. Of course, when it comes to emerging tech like AI, it's not all good news. New technologies can be used to cheat the system as well as benefit underwriters and policyholders.

When we take a closer look at AI applications in the insurance industry, here are five trends we anticipate seeing in the months ahead:

Insurance On-Demand

Are you renting out your vacation home, trailer or snowmobile? Consumers can monetize their assets through rental companies and online brokers. If you plan to rent your assets, particularly for short-term use, insurance is necessary and can't be left to the renter's discretion

You must be sure that your assets are adequately covered and that you are protected from personal liability. Thankfully, with on-demand policies, getting the necessary coverage becomes simple, whether you are a renter who requires a policy or an owner looking to guard against liability. With the help of AI risk assessment, your policy is ready in minutes and can be customized to your specific needs.

See also: 5 Ways Generative AI Will Transform Claims

Embedded Insurance Everywhere

Sure, you may know how to add insurance for your purchases or services, but why not sign up at the point of sale and make it all a simple transaction? After all, who wants to wait and go through yet another transaction to add insurance? This process isn't a new strategy. For example, travel insurance is often offered with a plane ticket.

What's new is that embedded insurance is being extended to other purchases, whether it's concert tickets, a new e-bike, ride-sharing or online banking. Embedded insurance reduces risk for consumers and avoids their having to shop for coverage. Just sign on the dotted line to include insurance with your purchase, and, who knows, the merchants may give you a special deal you can't find elsewhere. Embedded insurance presents an opportunity for insurers to capture new customers before they have a chance to shop for coverage.

More Benefits From Generative AI

Would you buy insurance from an AI bot? Perhaps you already have. AI-powered automation cannot replace the human touch entirely, but sometimes underwriting can be very transactional, and generative AI can make that process much faster and smoother.

There are several ways generative AI will benefit underwriters and policyholders:

1. Personalized insurance - Generative AI can analyze vast amounts of data, including local news and statistics, social media, IoT devices and historical claims, which makes it easier to create custom risk profiles for personalized policies at more accurate prices.
2. Underwriting - The same generative AI technology can be used for underwriting, making it easier to analyze nontraditional data points.
3. Claims processing - While some larger, more complex claims require careful human scrutiny, others are just business as usual, and who doesn't like business to be faster and more efficient?
4. Customer service - More companies are using chatbots and virtual assistants powered by generative AI to provide information on policies, claim, and more.
5. Marketing and sales - Generative AI also plays an increasingly large role in marketing content and sales strategies.

See also: Balancing AI and the Future of Insurance

More Platforms to Protect From Generative AI Fraud

AI isn't only about improving speed and efficiency. Fraudsters see generative AI as a new tool to falsify and exaggerate real claims and defraud insurers. But similar technology being used by fraudsters can be applied to protect insurance companies and their customers.

Here are just some of the ways that AI is being used to turn the tables on cyber criminals:

1. Anomaly detection - AI models trained to identify normal transaction patterns and claims data can identify outliers and anomalies that could point to fraud, flagging them for investigation.
2. Behavior analysis - Using AI to analyze behavior patterns associated with fraud helps identify potential fraudsters with greater accuracy.
3. Synthetic data - Synthesizing data that mimics fraudulent behavior can help train fraud detection models to make them more accurate and effective.
4. Predictive modeling - AI can analyze historical data such as trends, patterns and past claims to predict fraudulent activities.
5. Document and image analysis - AI is an ideal tool for detecting phony images and forgeries, detecting alterations and inconsistencies. For example, generative AI can compare images submitted for claims with similar images or analyze the image to detect digital alterations.
6. Text analysis and NLP - Using natural language processing (NLP), generative AI can provide investigators with fraud indicators and suggest areas that need further investigation, making the review process more efficient.
7. Continuous learning - As the fraudsters become more sophisticated, so do the fraud detection tools. Generative AI learns and adapts as it encounters new forms of fraud, adjusting fraud detection models accordingly.

Demand for More Cyber Insurance

Now that the bad guys are getting smarter about using AI, there are more cyber threats to worry about. It's time to batten down the hatches because, in addition to fraudulent claims, hackers are using AI to access critical systems, release malware and launch more elaborate phishing schemes. While steps can be taken to protect and guard against these threats, ultimately, insurance is the backstop to protect against losses. As the landscape for cyber-threats broadens, it's only natural for companies to cast a wider net to protect their businesses and reputations.

These are just some of the trends AI will shape in the insurance industry. Companies that embrace AI-based insurance will be able to develop innovative products for policyholders. Underwriters can also use AI to improve efficiencies and reduce costs. At the same time, generative AI can pose new threats to insurance underwriters.

Those insurance companies positioning themselves to thrive are finding new ways to apply AI to improve operations, develop innovative products and protect themselves from AI-generated fraud and cyber threats.