Scott Sayce is the global head of cyber at Allianz Commercial and group head of the Cyber Centre of Competence

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Paul Carroll

To start us off: What key trends are you seeing for cybersecurity and cyber insurance in 2024?

Scott Sayce

We've seen some commonality of trends throughout the past couple of years, and some of those are continuing.

Ransomware certainly hasn't gone away. It continues to be at the forefront, not only in terms of how insurers are trying to help customers but in terms of the points they’re raising with us. It's a critical area that causes them sleepless nights. Those that take it seriously and have the right mentality and the right culture are best positioned to protect themselves, and insurance can help.

A lot of people think of ransomware just in terms of the first-party element, affecting the particular company that was hit. But more and more we're seeing data exfiltrated as a result of ransomware attacks. Those data breaches affect third parties and have different impacts across the world, depending on the geographical, legal and regulatory regimes around privacy breaches.

Paul Carroll

When hackers exfiltrate the data, what do they do with it?

Scott Sayce

The exfiltration is another way to force somebody to consider paying a ransom. It causes mass disruption, outside of the pure financial impact to an organization and third party.

Paul Carroll

In the article you did last year in ITL, you talked about ransomware as a service and about how attacks are speeding up. They used to take weeks and now may just take days. I assume you're seeing that trend continue?

Scott Sayce

Absolutely. As technology continues to advance, so does the sophistication of attack, and so does speed. You don't need to be that cyber-savvy now to mount an attack, because of ransomware as a service. You can be a moderately smart individual and be able to target small, medium and large organizations by really renting a service.

The good news is that the new technology can also be used incredibly well for defense purposes. We hear a lot about risks around quantum computing and the future of quantum computing. But quantum computing and encryption can also be an incredible strength. So we have to both embrace it and be fearful and aware of the risks that it poses all at once.

Paul Carroll

A year or so ago, I spent some time with a professor from Cal Tech, who explained how quantum computing is perfectly designed to decrypt information based on the way it's encrypted now. I've since read a lot about what I think people are calling "Hack Now, Decrypt Later." In other words, hackers are grabbing information now even though they can’t decrypt it, because they know that quantum computing will let them do so in five or 10 years. How are you encouraging clients to think about that?

Scott Sayce

That’s a great question. We’ve done a lot of research on this. We've gotten smart people from a lot of different parts of our organization involved, as well as external expertise.

When we have any new threat or any new type of technology, we do quite a bit of research because we need to understand: A. Do we want to underwrite it? B. Can we underwrite it? And C. Is there a product offering that our clients need?

This isn’t just about me and my underwriters. It's about cyber models. It's about risk management. It's about our commercial cyber risk consulting team.

Some of the views are not definitive, because there will always be unanswered questions. There's always going to be, What's next? Cybersecurity and cyber risk are never set and done, right? So we have to always challenge ourselves that we don't know everything. Our customers don't know everything. But we hope that together we're making the right, informed decisions to help our customers protect themselves with the right knowledge. We hope we come up with answers to some of the future risks and some of the potential challenges they may face for their unique company, as opposed to generic topics like quantum computing, AI, etc.

Paul Carroll

I gather there's a standard being developed so people will be able to start encrypting things now in a way that will be at least resistant to quantum decryption whenever that becomes possible, years down the road.

Scott Sayce

I wouldn't plant my flag on that yet. Once we feel we have solutions, there's always a new way for hackers. I've been involved with cyber insurance for almost 25 years, and I don't think I've ever used the phrase, "We've got it nailed."

Paul Carroll

In terms of other things going on with ransomware, I read the other day in The Economist about how some hackers are lowering the profile of the targets they go after because authorities devoted so much time and energy to investigating some high-profile attacks, such as the one that shut down the Continental Pipeline. Is this something you're seeing? And are there other changes in terms of tactics and targets?

Scott Sayce

I think there are two areas of focus. You've got the out-and-out targeted, where somebody wants to target a particular company to really infiltrate it. That takes significant time, but the reward can be much higher. You also have the scattershot approach, where you're trying to identify a common vulnerability that hasn't been dealt with by organizations, whether they’re small, medium or large companies. You're going for quantity, hoping to pick out maybe one in 10, one in 100, one in 1,000 organizations, but not just focusing on one.

The data we’re seeing is that organizations that don't deal with those critical vulnerabilities, and don’t act when a patch is released for a zero-day vulnerability, well, they're more than 30% more likely to be affected. Yet a lot of organizations dismiss critical vulnerabilities. They think, "It's not going to be us."

And to your point, we’re not just seeing highly publicized attacks, but also ones hitting SMEs and mid-corporates, as well. Some larger companies can operate during an attack, but some of the smaller companies could be driven out of business.

Paul Carroll

What more can companies be doing to protect themselves?

Scott Sayce

Ransomware has ravaged organizations over the last three or four years, and companies have definitely stepped up their game. A number of organizations have embraced the need to constantly check what they're doing. I'll come back to my phrase, "set and done." Even once you’ve assessed and fixed a vulnerability, you have to realize this is a continuous cycle. There will be a new vulnerability, a new attack, a new zero-day impact. There'll be an update from one of your providers that causes a problem. So my biggest recommendation is: Don't just check once; check continuously.

An ecosystem of insurers, insurtechs, customers and brokers and agents has developed, so we aren’t just writing a check at the end of the day when a customer has a claim. We’re about the services we can provide to a customer to help minimize the impact or even prevent it. If an attack does happen, how quickly can we deal with it? I remember a cybersecurity expert saying to me: "We keep them out for as long as we can. And once they get in we get them out as quickly as we can."

And that's the thing. I think the insurtechs and insurers, along with customers, brokers and agents, have been working to create that ecosystem of solutions and services, backed up with risk transfer. This is a much better solution than, Here's a policy, and if you have an issue call this number.

I'm incredibly proud of what the cyber insurance industry has built over the almost 25 years that I have been involved. I think we've innovated faster than any other line of business, on services coverage and the ability to bring in diverse talents into the industry.

Paul Carroll

How helpful are governments being?

Scott Sayce

Some countries look to the insurance industry on cyber, while others look to insurance and government in a hybrid model. The cyber insurance market, in my opinion, will be larger than some of the traditional lines over the next 10 to 15 years, so we need to continuously look at what we can insure, what we can't insure and where we need governments to help. I think having a mutual understanding is the first step, and I think we're coming to the table to do that, which is really positive.

Paul Carroll

How about industry cooperation? A few years ago, somebody wrote a piece for me saying there ought to be as much sharing as possible within the industry. Obviously, Allianz has great scale on its own that it can learn from, but is there much prospect for cooperation among the big insurers?

Scott Sayce

We're talking about critical cyber information for potential customers, and you have to be very mindful of that. We also have to be very careful of what the intended purpose is of sharing that data. There are a few businesses out there now that are not monetizing the data in any way and actually are looking at collating it to provide industry trends and provide information out there. And that's good. We tend to look at our own data.

Paul Carroll

When I first started tracking the cyber issue, years ago, insurance companies were rightly scared of the risk because they didn't know how big the liability could be, yet customers didn't want to pay much. So there was this huge gap in the middle about what pricing should be. But it feels to me like the market has matured to the point where there's more of a meeting of the minds. Does that sound right?

Scott Sayce

I think you're right. Over the last four or five years, ferocious ransomware has hit so many organizations, and cyber insurance proved its worth with the volumes of claims that were paid.

Some called cyber insurance a bit of a fad many years ago. But it's now proven to be a staple insurance purchase in many locations, and demand for this product is evident more and more in certain countries.

Some insurers may well be fearful of new capital coming into the market, but we need this capital across the insurance industry for us to be able to continue to serve the market. As long as your underwriting integrity remains, and as long as customers continue to improve and work on their cyber hygiene, we know that cyber risk management and cyber risk transfer will continue to go hand in hand. And we're here for the long term.

Paul Carroll

That's great. Any final words?

Scott Sayce

Well number one, thank you for your time. It's always good to get to engage and listen to insightful questions. But also know that for us, cyber is a consistent line of business. It's a core line of business for us, one that we're committed to across vast different countries to provide solutions and products to our customers and help them stay protected, as well.

Paul Carroll

Thanks, Scott.

Last week, Amazon shut down its U.K. homeowner insurance comparison website, as Google did for its auto insurance site several years ago.

Over the past days, we have primarily heard exactly what was said eight years ago about Google shutting down Google Compare (look at the string of comments under that old post):

• they didn't succeed, but they will be back
• they did well, but not enough for their standard, and they make more money on other businesses
• they come to learn, they did it and they are working on the disruption
• the market was not ready
• the insurance sector is heavily regulated...

What seems missing is any acknowledgment that insurance incumbents, their employees and their intermediaries do their jobs pretty well, and that many of them are even taking essential steps in the usage of (new) data and technology in their core business.

In the last edition of this newsletter, I shared a recent interview with Lemonade's CEO, and I observed how Lemonade's recent storytelling moved to aspects that make more sense. About automation in customer support and claims, I commented: "It would be difficult to find an incumbent who is not working on applying AI to these processes."

One insurer that has already done an impressive journey with AI is Nationwide. Here, you can read my dialogue about it with Jim Fowler, EVP and CTO at Nationwide.

MC: Over the past months at conferences, we have heard comparing the ChatGPT moment to other events in the story. I have heard multiple speakers saying that the ChatGPT moment is less relevant than the discovery of fire for humanity but more relevant than the iPhone or the web. What is your opinion about GenAI and its potential impact on the insurance sector over the next five years?

JF: The changes I’ve seen in technology during my 30-plus-year career have been amazing, and the revolution Gen AI is driving today is the most significant I’ve experienced. Frankly, the world is going to be forever changed – and insurance is one of the industries likely facing the most disruption.

There are clear opportunities coming from AI for companies to find efficiencies in routine, repetitive work. In some respects, everyone is going to need to be a technologist. The underwriter of the future doesn’t just need to know how to underwrite, they need to train a bot or a model how to underwrite.

While there are also AI-powered opportunities for customers to save money, the more meaningful advancements will come when we reach Gen AI’s potential to enhance how we care for customers. AI promises to enable more personalized solutions to protect families and their futures, provide better suggestions to improve our customers’ lives and businesses and supply expanded resources to our teams as they deliver comfort in moments of need. That vision is what’s guiding our approach at Nationwide.

See also: What to Learn From Amazon's Failure

However, over the next few years, our industry will also need to adapt to protecting our teams and customers from those looking to leverage Gen AI to do harm. We’re already seeing people try to generate false images of accidents to file a fraudulent claim or trying to recreate someone’s voice to gain access to their accounts.

Our industry needs to focus on developing new skills and tools – just as much for defense as for offense. We’re doing just that with our red team-blue team approach at Nationwide. The benefits of this technology far outweigh the concerns, and I’m very excited to think about where we’ll be in the future.

MC: I have the impression that generative AI is dropped today into any discussion and paper about innovation, even when other AI models (such as recurrent neural networks, gradient boosted machines or reinforcement learning models) seem to be more adequate to address a specific problem than LLMs. I saw an infographic representing Nationwide’s AI journey of almost 15 years. Based on this successful experience, what is your vision for using the different AI models in the insurance business?

JF: Generative AI, specifically, has received an enormous amount of attention since its inception because it has made the possibilities of AI more tangible and accessible to a much bigger slice of the world. Because of Gen AI, AI isn’t just for technologists anymore.

However, it’s not a one-size-fits-all solution. Nationwide has been using different types of AI since 2010, and there will continue to be a need for a wide variety of tools. We have an AI steering committee that evaluates potential use cases and helps direct toward the best tool, model or analytic to solve the specific problem.

The explosion of AI has been made possible through the improving use of the cloud and data.  A key reason that Nationwide has been successful putting AI to work is that we invested heavily to get to single systems of record for all of our businesses.  Machine learning tools working with our data have been particularly helpful with risk assessment, fraud detection and claims processing, and these aspects will continue to be critical to our success. Also, our initial use of natural language processing has been promising in analyzing real-time customer feedback.

There’s a world of options for all types of AI, not just generative AI, especially as these tools continue to mature.

MC: In one of your recent interviews, you mentioned how natural language processing allows one to ask questions and obtain answers in a commonly used language. This aspect obviously allows non-tech users to use the tool. Can we foresee a near future where any use case (based on different AI models) has a more intuitive user interface based on natural language processing [NLP] in an organization such as Nationwide?

See also: Insurtech Is at an Inflection Point

JF: In the near future, we can certainly foresee potential use cases within our organization for NLP-driven interfaces to provide a more user-friendly and intuitive experience. Here are some examples:

• Customer interaction: We anticipate that NLP-powered interfaces will become the primary mode of interaction for our customers. This will allow them to engage with us seamlessly, ask questions and receive answers in plain language. For example, customers could ask questions, initiate transactions or seek advice using natural language.
• Employee productivity: We see NLP as a tool that can empower our employees to interact with complex systems and access critical information more efficiently. For instance, analysts can query vast datasets in a conversational manner to extract insights, while customer support representatives can quickly access relevant information to address questions and better care for customers in a time of need.
• Decision support: NLP-driven interfaces will also play a pivotal role in enhancing decision-making processes. Executives and managers can use NLP to obtain real-time insights and recommendations in a more accessible manner, enabling quicker and data-driven decision-making.
• Personalization: NLP can enable us to offer more personalized experiences to our customers and partners. By understanding natural language queries and preferences, we can tailor recommendations and services to meet individual needs more effectively.

While we are enthusiastic about the potential of NLP to help us provide the best possible experiences for our customers, we also acknowledge the importance of challenges such as data privacy, model bias and data quality, which we are keeping top-of-mind to address.

MC: Last, I read about your vision of a bionic enterprise where AI is crucial in augmenting human capabilities. Where are the areas of the organization where you have measured or expect the highest economic impact by using AI? Where do you expect to find the highest one in the next few years?

JF: At Nationwide, we believe AI’s greatest potential is to act as virtual assistants that automate the simple tasks of our associates. This will enable our people to do what they do best and apply their judgment, reason and empathy, to allow Nationwiders to be uniquely human.

We’re experimenting with tools that could support our associates’ work in many different disciplines. We see opportunities to help our technology, sales/marketing, claims and customer service teams provide even more care to our members and partners.

The biggest opportunities for our company will come from the new ideas and strategies these teams can create in the future when they have more time for critical and creative thinking. These new directions, delivered through hyper-personalized offerings for our members and supported by a bionic team of people working hand-in-hand with AI creates a compelling vision for the future for our company.

Even today, we’re starting to implement solutions that a year ago I thought could have taken five or even 10 years to develop. The pace of change is astounding and will lead us to some amazing places over the next few years.

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Now, let's go back to Lemonade's current storytelling. Telematics ("continuous streams of highly precise [...] data to us") was one other point touched on as a source of competitive advantage in the interview.

To hear someone claiming that "incumbents cannot for structural reasons" use telematics data well makes me laugh, almost as much as when Lemonade's CEO said in 2022, "When a customer churns, you have not lost a customer, instead you created an alumnus."

Let's look at some facts and figures about personal auto telematics in the U.S.:

• One of the top 10 auto carriers has shown more than 3.5 mllion weekly active users in their telematics app on average over the past 54 weeks (source: Data.ai)
• Customers new to an insurer have a UBI participation rate of 26% on average in the market (and accumulated participation of 17% of auto insurance customers), and many of the top U.S. carriers have astonishing penetration rates (at least in their direct channels) 

Some of the U.S. insurance incumbents are using telematics data pretty well!

If we have to criticize the incumbents, we should focus on the limited appeal of the current approaches for their intermediaries and the need to extend the usage of these telematics capabilities to the portfolio of loyal customers already in the portfolio to improve the bottom line materially. The evolution beyond the current switch-and-save approach would be a more extended discussion. 

On telematics, Nationwide -- a participant at my IoT Insurance Observatory for many years -- is one of the insurance incumbents leading the pack. I had the pleasure of collaborating with Kelly Hernandez, AVP, personal lines, telematics, at Nationwide, on an article about their telematics capability and the vision for the future of auto insurance. Here are some insights:

• "Nationwide has observed that customers are increasingly willing to share their data through telematics, with a substantial 70% to 80% of direct customers choosing telematics at the point of sale [...] over the past 18 to 24 months, Nationwide has witnessed a remarkable 60% increase in telematics program adoption among independent agents"
• "App interactions increased as much as nine percentage points on days push notifications were sent, and engagement continued several days after"
• "A 10% reduction in everyday hand-held distractions [occurred, proving] that a carrier can affect driving behaviors through telematics programs"
• "For those that have participated in a telematics program, Nationwide consistently sees statistically significant improvement in overall satisfaction, the likelihood to stay with and recommend Nationwide. This is seen in customer retention, as well, which is typically two to three points higher than [for] those that do not try telematics"
• "These results come from a 10-year journey using the telematics data. Nationwide is firmly convinced that telematics is a permanent fixture in the insurance landscape and will persist in its evolution" 

While generative AI is a game changer for business, including insurance, the rush of excitement over any new technology always makes me a bit nervous. Remember when the metaverse was going to change everything? How about Google Glass before that? Virtual reality? And I'm not just talking about all the VR hype a decade ago, when Facebook bought a startup, Oculus, for $2 billion. I'm also talking about the first time virtual reality was going to change everything, back in the 1980s. 

I think it's worthwhile to look out for examples of problems with AI from time to time, if only to remind us of what it can't do, at least not yet, and of what problems overreliance on it can cause. Just to keep us honest. 

So that's what I'll do this week: I'll run through some recent examples of problems and the lessons I think they provide. I'll start with a story that I was initially tempted to dismiss as simply lunacy but realized showed in extreme form two mistakes that people make with AI all the time. For one, people often treat output from AI as gospel, seemingly just because it comes from a computer. For another, people often pay far too little attention to the quality of the data they feed into the AI -- and it can be poor. 

The Cold Case

In 2017, police investigating a sexual assault and murder that occurred in 1990 in Berkeley, California, learned of a company, Parabon NanoLabs, that claimed it could produce a good likeness of a person's face from their DNA. The police sent the company genetic material collected from the crime scene decades earlier and were sent what was purported to be a 3D rendering of the murderer's face. Police published an image, asking the public for help identifying the man, but received no leads. 

According to the article in Wired that I'm drawing from here, "Parabon says it can confidently predict the color of a person's hair, eyes and skin, along with the amount of freckles they have and the general shape of their face.... [But] Parabon’s methods have not been peer-reviewed, and scientists are skeptical about how feasible predicting face shape even is."

Even a top technical executive at Parabon is quoted as saying, "'What we are predicting is more like—given this person’s sex and ancestry, will they have wider-set eyes than average. There’s no way you can get individual identifications from that.'”

Still, a detective took the next step in 2020 and asked to have the image run through facial recognition software and matched against a police database. While this particular investigation went no further, numerous police officials from around the country are quoted in the story as defending the practice, so the odds seem high that "matches" are going to be generated based on such speculative images.

That makes no sense. Feeding semi-reliable input into a semi-reliable facial recognition system will lead to so many false positives that there is bound to be trouble. Just because you can point to AI as the source of a match doesn't make it accurate.

Which brings me to my next story.

The False Identification

In January 2022, two men waving guns robbed a Sunglass Hut in Houston. EssilorLuxottica, Sunglass Hut's parent company, used facial recognition software on security video and identified a man as a suspect. When a store employee who had witnessed the robbery picked that man from a photo lineup, police arrested him and held him in jail for 10 days. But he had an alibi, and it was ironclad. According to the Washington Post, he had been in jail in California on the day of the robbery, on unrelated charges. 

Prosecutors dropped charges, but, in a lawsuit filed a week and a half ago, the man says he was raped while in jail and is demanding $10 million from EssilorLuxottica and Macy's, whose facial recognition software it used on what the suit claims was low-quality surveillance footage. 

So, at least according to the lawsuit, we again have buggy technology being fed buggy data -- and the result was treated seriously, this time with all sorts of repercussions. (While police doublechecked by using a photo lineup, those lineups are buggy, too, as all sorts of research has found. While eyewitness testimony was long considered to be the gold standard, it's now recognized that people who are being robbed at gunpoint often can't remember the events clearly.)

The Biden Deep Fake

Just last week, I read a note from Andreessen Horowitz bragging about an investment in a company that can mimic people's voices, at a valuation that made it a "unicorn." Now, I read in Wired that technology from the company, ElevenLabs, was probably used to make the deepfake of President Biden that was used in robocalls before the New Hampshire primary to tell people not to vote. 

My takeaway is pretty simple: AI giveth, and AI taketh away. It'll always reflect a contest between the good guys and the bad guys, with both using great new technology for their very different purposes.

The Taylor Swift Deep Fakes

I hesitate to even mention the situation, because the deep fakes of Taylor Swift are reprehensible, but they underscore the lesson from the Biden deep fake. It seems that a Microsoft tool was used to generate deep fakes of all sorts of celebrities. Safeguards were built into the tool that were supposed to prevent such uses, but some sick people circumvented them. Microsoft has closed the loopholes, but nobody is terribly sanguine that they'll stay closed -- or that hackers won't find vulnerabilities in someone else's tool. 

There are calls for new laws to punish this sort of sleazy behavior, so perpetrators can't just crawl back under their rocks when caught, and maybe those could work. In the meantime, I'd say the takeaway is still that there will be a constant tussle between good and bad uses of AI.

The George Carlin Not-So-Deep-Fake

This is a weird one. A comedy podcast sold an hour-long comedy special as being generated by AI based on the work of the late, great comedian George Carlin. To no one's surprise, his estate sued for copyright infringement. One of the podcast hosts now acknowledges that the material was actually written by a human.

My takeaway: The term "AI" will get sprinkled onto all sorts of products and services like fairy dust. Some of the claims (many?) will be as fake as the Carlin AI podcast.

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While none of these stories has a direct tie to insurance, I think it's worth keeping them in mind as we incorporate AI into all sorts of processes -- and to keep a weather eye out for more cautionary tales. There will be temptations to trust AI too much, based on inputs that we don't vet thoroughly enough. While we focus on the good that AI can do, we may overlook the problems that can come with it, including those caused by bad actors. We'll certainly be peppered with claims about AI in everything. And I'm sure we'll encounter problems that I, at least, haven't envisioned yet. 

I still think the end result will be a breakthrough for the world of insurance and all of business, but a lot will happen between here and there. We should learn from others' mistakes, so we don't have to make them all ourselves. 

Cheers,

Paul

 

From its inception in 1995 to today, Amazon has been a pioneer.

At this stage, it'd be easy for the tech giant to simply sit back and treat most of its business portfolio as cash cows, there to be milked until‌ they run dry. However, 29 years later, Amazon continues to deliver lean startup models, learning fast, adapting and moving forward.

Before I dive into the recent Insurance Store experiment and its implications for the insurance industry, it's important to note that Amazon is an ecosystem driver. That last word is particularly important. 

Broadly, there are two ecosystem models. On the one hand, you have drivers, like Amazon, who strive to own the ecosystem in which the market exists. They are the apex predator, holding the most customer knowledge while consistently increasing their ability to act on it. 

Being a driver is an incredibly powerful market model when you can make it work, and it's unsurprising that some of the biggest brands in the world are also ecosystem drivers: Apple, Google and Netflix, to name a few. 

On the other hand, there are modular producers, which specialize in operating in everyone else's ecosystems. They not only hold acute knowledge of the customer, they specialize in acting on it wherever they are. Think PayPal for payments or Intel in manufacturing. 

You could argue that Amazon does both, but I believe what makes it rare is its push to own the ecosystem. Alexa isn’t a byproduct of technology-based innovation. It's a result of a corporate culture that relentlessly looks to achieve the greatest share of its customers' minds and optimizes its ability to drive purchase habits. Being ever-present and able to interact through voice was an obvious way to help do this.

See also: Learning From Failure

So what does all this mean for insurance? 

In my opinion, there are no true ecosystem insurers yet. There are those that operate their own value chains in ecosystems, and there are those that participate in others'. But insurers simply aren't structured in a way that can leverage an ecosystem model to its fullest potential.

Amazon wasn’t built around the concept of books. It was built around the customer and the desire to optimize convenience, price and the buying experience. Why? Because these are the things that drive relationships, engagement and sales. As Amazon advanced its technology foundations, it became a logistics and technology business capable of increasingly selling anybody anything. This makes Amazon one of the most-talked-about threats to any market, including financial services and insurance. 

Insurers can learn a lot from both ecosystem business and technology models, particularly the concept of being built around the customer. Being built around a policy or value chains of policies creates siloed organizations. It prevents the flow of data and an organization’s ability to act on it in real time. As a result, most carriers aren’t even capable of recognizing a car insurance customer in their home insurance experiences. 

The ecosystem model will enable the creation of insurance experiences that connect with customers in myriad ways, from recognizing a change of circumstance and making my insurance and life feel more connected, to helping me understand and act on risk. Even better, maybe I can mitigate the risk entirely. 

In the case of the Insurance Store, Amazon clearly spotted a gap. Low levels of product knowledge and trust meant setting a cover standard could well provide the differentiation needed. Reassuring customers that they are comparing products knowing they meet certain cover levels reduces cognitive load and allows customers to explore the more nuanced aspects of home insurance. Add this approach to an experience model that’s proven and easy to understand, and the long-held Amazon ethos of “Don’t make me think” (thank you, Steve Krug), and the Insurance Store could have been highly disruptive. 

Sure, many predicted it wouldn’t upend a saturated market led by price. But let me be clear: Amazon isn't out of insurance.

It will remain, offering embedded solutions and healthcare and often participating in cross-selling. Leveraging your assets (customers in Amazon’s case) is always sensible.

Many commentators have said that a) they find the closure of the Insurance Store after 15 months a predictable outcome and b) it reflects the complex and competitive nature of insurance. But I'd largely disagree with them. 

I think this was always a massive learning opportunity. Whether that opportunity succeeded or failed, it was going to bear some fruit -- even if that fruit was highlighting what not to do.

My hat is off to all those who participated. Creating a standard that made sure each insurance policy met a specified minimum level of cover made it easier for customers to know what they were getting. Further, the desire to create a better experience was a noble effort. Amazon showed the direction all insurance should be going.

Price-leading purchase habits in insurance require aggregators (e.g., Confused.com) to validate the best price they can. But doing so requires scale and market coverage. Otherwise, it's hard to justify telling customers something is "cheapest" or "best value." 

It was therefore impossible for the Insurance Store to compete in this market model without being competitive on price. I suspect market coverage played a part, as well.

See also: 5 Key Mistakes in Long-Term Planning

My advice is that Amazon should now focus on their embedded insurance potential and focus on markets such as healthcare where there's enough differentiated value in creating better experiences and convenience, and where they generally have more levers to compete best. I suspect they'll do exactly that. 

For insurers, we need to keep experimenting, taking on market opportunities and learning fast from them. Amazon’s Insurance Store showed that there are gaps that other markets and other businesses will continue to look to fill. Insurtechs and neo-insurance models are just getting going.

We're at the dawn of the insurance ecosystem era, and a protracted tipping point. If we keep going, everyone will benefit. The only way to be a winner in this next phase is to be increasingly adaptive. And to do that competitively you have to be on the steepest learning curve of your competitors. 

You must participate in the emerging ecosystem by constantly evolving. This requires the same foundations as the ecosystem businesses we already have in other industries, bringing in the proven new technologies pioneered in the cloud and software as a service (SaaS) world and applying them at scale to insurers. This will create agility through data-fluidity, expanding ecosystem and intelligently orchestrated experiences. 

It will also reduce both the cost and complexity of change, particularly in extensibility and scaling, and create boundless value potential and the opportunity to build more meaningful relationships with customers. 

The future is insurance that’s embedded intelligently into our lives, risk-mitigating, human-centric and adaptive.

KEY TAKEAWAYS:

--Network tokenization has emerged as an ideal solution for numerous businesses. It involves substituting a customer's primary account numbers (PANs) and other card details with a token issued by the card brand at the point of checkout. The PAN numbers remain securely concealed throughout the transaction, guaranteeing protection against data breaches.

--According to Visa, payment transactions using network tokens can reduce fraud by nearly 26% and have an average authorization rate increase of 2.2%.

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Over the past few years, the global economy has witnessed a remarkable digital payment revolution. Since the onset of the pandemic, an increasing number of consumers have made their initial shift from traditional cash payments to digital and contactless alternatives.

This surge has been particularly pronounced in industries reliant on digital transactions such as retail, restaurants, banking and insurance. In fact, according to McKinsey, a staggering nine in 10 Americans are now embracing various forms of digital payments, driving a substantial demand for faster, more convenient and secure payment experiences.

As the volume of contactless payments rises, so does the volume of challenges, specifically in payment security. To counter these security threats, an increasing number of businesses are turning to tokenization. Tokenization is a security measure that replaces sensitive information with a unique code known as a token. These tokens provide a protective barrier against identity theft, proving especially valuable in cases where a consumer's information is compromised, exposed or stolen.

Below are some of the biggest benefits of payment tokenization for both businesses and consumers.

See also: How Blockchain Enhances Reliability, Speed

Leveraging the Value of Network Tokenization

Network tokenization has emerged as an ideal solution for numerous businesses. It involves substituting a customer's primary account numbers (PANs) and other card details with a token issued by the card brand at the point of checkout.

The PAN numbers remain securely concealed throughout the transaction, guaranteeing protection against data breaches. However, unlike other tokenization methods that rely on processors or non-portable vaults exclusive to specific digital platforms or channels, network tokenization boasts end-to-end interoperability within the entire payments process. Consequently, it can seamlessly traverse various channels it encounters. In this system, cards are directly tokenized with the card networks, such as Visa and Mastercard, and their authenticity is confirmed by the issuing bank.

As security breaches and threats become more common, businesses must find solutions for limiting risk of exposure and alleviating compliance obligations, making network tokenization more critical for merchants than ever.

Here are three reasons why companies are turning to network tokenization and how it has become one of the most powerful tools available:

Higher Payment Authorization Rates

Payment cards can be declined for various reasons, from suspicion of fraud to inaccurate or outdated information, which may result in the loss of sales and a poor customer experience. With network tokenization, brands can directly communicate with the issuing bank to verify the legitimacy of the card, preventing card declines.

Because the tokens are going directly to the source, these transactions are deemed to be more secure and trusted by both the issuing and acquiring banks. According to Visa, payment transactions using network tokens can reduce fraud by nearly 26% and have an average authorization rate increase of 2.2%. With more successful payment transactions, businesses increase revenue and create loyal customers who are likely to repeat transactions from that brand.

Lower Interchange Rates Create Lower Costs

Accepting credit cards entails costs, with the majority originating from interchange fees. These fees serve to balance processing expenses and mitigate potential risks associated with payment approvals. The interchange rates are calculated using units of measurement called "basis points." For credit card processing, one basis point (BPS) is typically 1/100 of one percent.

In 2022, Visa disclosed a rate increase of nine to 10 basis points for qualifying Visa transactions when processed without using a network token. The rate increase has affected many interchange categories across several verticals, including insurance, banking and retail. By using network tokenization, businesses can avoid these rate increases on qualifying categories, thus lowering costs.

See also: Why Are We Still Talking About Digital Transformation?

Increased Retention and Persistency

According to Visa, more than two-thirds of U.S. consumers choose to store a credit card on file or set up recurring billing with merchants to avoid manual key entry. However, people many times gave a scenario where they need to be reissued a credit card. Per Visa, 35% of their survey respondents admitted they had forgotten to update their card information with merchants at least once. Their research also found that merchants generally reach out to customers two to three times to try to update card details before canceling services.

When organizations that offer services like insurance, utilities and subscriptions and other recurring payments move to digital payment methods, they often require additional resources that add to operational costs. These issues can lead to missed payments or cancellations, damaging the customer experience. However, network tokenization offers a solution by allowing real-time updates to tokens, preventing recurring payment failures. This, in turn, reduces the need for service center calls, minimizes disruptions and enhances customer retention.

The Positive Impact of Network Tokens

Network tokenization wields significant financial influence, particularly for large enterprises dealing with substantial transaction volumes. Even a slight reduction in interchange fees and payment declines can yield substantial benefits when applied to thousands or even millions of monthly transactions. The transformation of stored credit card data into secure network tokens offers online customers, whether they are shoppers, diners or policyholders, the benefits of enhanced security, convenience and an overall improved customer experience.

In the era of rapid digital payment growth, customers expect greater speed, convenience, safety and choice. Collaborating with payment networks to harness this essential capability, businesses are undoubtedly poised to boost revenue through increased repeat business while consistently providing a smooth, hassle-free payment experience.

In 2024, "model benchmarking" is set to be one of the emerging trends in AI adoption, particularly within the insurance sector. Enterprises have struggled to address the persistent challenge of "POC purgatory," where promising AI solutions often become stalled in the proof-of-concept (POC) stage and struggle to scale across the organization.

To combat this issue, specific benchmarking criteria will gain prominence. These benchmarks will serve as essential metrics to evaluate progress during the development and deployment phases, enabling businesses to make informed decisions on whether to scale, ultimately streamlining the AI implementation process.

Understanding Model Benchmarking

Model benchmarking involves assessing AI solutions based on their performance and impact. There are two main categories:

Technical Benchmarks:

These benchmarks employ various metrics, such as precision, accuracy, recall and F1-score, to gauge how effectively the model performs specific tasks. These metrics help assess the model's ability to make correct predictions:

• Precision: The ratio of correctly predicted positive observations to the total predicted positive observations.
• Accuracy: The ratio of correctly predicted observations to the total observations.
• Recall: The ratio of correctly predicted positive observations to all actual positives.

See also: 4 Key Questions to Ask About Generative AI

Product Value Benchmarks:

Unlike technical benchmarks that focus on model metrics, product value benchmarks assess the real-world impact of AI solutions on end users and businesses. These benchmarks measure how the AI solution affects user experiences and business outcomes. They include:

• Retention rates: The percentage of customers/users who continue to use a product over a specific period.
• Churn rates: The rate at which customers stop using a product or service.
• Engagement metrics: Various user activity indicators such as daily and monthly active users, time spent on a platform, interactions per user, etc.

Product value benchmarks are crucial, as they showcase the practical significance of the AI model's performance. A high-performing model may not always translate to valuable business outcomes if it doesn’t improve user engagement or retention or reduce churn rates.

By considering both technical benchmarks and product value benchmarks, insurance companies gain a comprehensive understanding of an AI model's effectiveness. This holistic approach ensures that the AI solutions not only perform well technically but also improve the end-user experience and help with business objectives.

Challenges in Scaling AI Solutions

Despite the potential, insurers struggle with challenges in scaling AI solutions beyond the POC stage. The industry has witnessed a “POC purgatory” scenario where only a meager 10% of tested AI models in financial organizations progress to production and scalability. 

Complex workflows and legacy data architectures are major hurdles. The interdependence of workflows heightens the risk of error propagation across systems. Legacy data silos obstruct efficient access to unified data, essential for machine learning (ML) model training and fine-tuning. The lack of human adoption of AI tools adds another layer of complexity. Even highly proficient and accurate AI tools fail to deliver lasting value if not embraced within an organization or by customers.

To navigate these challenges, insurers can establish stage gates and success criteria, creating specific milestones for AI projects. For example, an agile governance board, employing benchmarks as guiding tools, can aid in decision making, ensuring alignment with strategic objectives and customer needs. Involving key stakeholders early in the process fosters buy-in and enhances the viability of AI solutions.

See also: AI: Beyond Cost-Cutting, to Top-Line Growth

Deciding on AI Implementation and Future Outlook

When considering scaling AI use, insurers must evaluate if AI is necessary or if other approaches suffice. Compliance use cases might better suit rules-based algorithms due to their explainability. Examining the current infrastructure and data architecture determines the feasibility and scalability of AI implementations.

Looking ahead into 2024, generative AI will continue to headline discussions. However, there is already a shift toward specialized, smaller language models tailored for specific insurance use cases. Vision algorithms, like OpenAI's ChatGPT with vision, promise more accurate visual assessments, such as claims estimates. These developments are indicative of a future where AI's integration aligns seamlessly with insurance processes, paving the way for enhanced efficiency and better customer experiences.

KEY TAKEAWAYS:

--Digital performance marketing involves a partnership with an individual, a company or a network that delivers high-quality leads to insurance agents on a commission basis. More cost-effective options are becoming more directly accessible to agents in 2024.

--In looking for a partner, agents should focus on: getting inbound leads, having exclusivity on the leads, receiving multiple lead types, having flexibility about the pacing of the leads and, of course, working within a budget.

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If you aren't consistently finding and going after qualified leads, your agency’s sales will suffer, but finding good leads in the insurance space is much more easily said than done. While you may always try to have your finger on the pulse when it comes to finding leads, several factors are working against you that remain out of your control. 

With big corporate brands gatekeeping their leads from captive agents, a lack of clarity around the sources of purchased leads and questions about whether leads are being resold to other agents, generating worthwhile leads can (at best) feel like a time-consuming pain in the neck and (at worst) be an almost unconquerable roadblock. 

Fortunately, digital performance marketing is becoming a more accessible, more reliable way for agents  to find high-quality leads and grow new business. 

What is digital performance marketing? 

Digital performance marketing involves a partnership with an individual, a company or a network that delivers high-quality leads to insurance agents on a commission basis. Because these partners only get paid when they offer good leads, this practice is extremely low risk for agents.  

Traditionally, the most accessible way for agents to buy leads would be to purchase the contact information of a potentially interested consumer with little to no information about where the lead came from, its exclusivity or the likelihood that the lead would convert into a new policy. While there are still merits to this practice (if you have the right partner), it is far from perfect, and it’s no wonder agents have become frustrated. 

This new wave of digital performance marketing is different because agent and partner co-exist in a  symbiotic relationship. They both benefit from the success of the other.  

See also: Digital Underwriting Now a No-Brainer

Pay-per-click lead generation 

While generating leads with a pay-per-click strategy is far from new (think advertising on search engines such as Google and Bing), more cost-effective options for pay-per-click are becoming more directly accessible to agents in 2024. This kind of lead generation happens when an agent works with a partner to display an advertisement or recommendation on a website with a relevant  audience – like an insurance comparison site or financial news outlet – and pays each time a consumer clicks on the ad and visits the agent’s website. 

If a pay-per-click campaign is thoughtfully crafted, it can offer agents several benefits, including cost control, an increased likelihood of reaching their target audience and relatively quick results. 

Additionally, pay-per-click lead generation offers agents the ability to measure and track the overall performance of their advertisements. This guarantees that they are spending their money the right way.  

Pay-per-call lead generation 

Pay-per-call lead generation offers many of the same benefits as pay-per click. With a pay-per-call strategy, an agent receives in-bound phone calls from prospective customers and only pays their partner if the call lasts for a predetermined amount of time. 

This type of lead generation has the potential to have a higher conversion rate because, oftentimes, leads who make a phone call are further along in the insurance shopping process and will be looking for an agent more seriously than those who opt to click on an advertisement. 

This type of generation allows for the agent to engage directly with the potential customer and begin to foster a relationship.  

See also: An Insurance Agent's Guide to SEO Marketing

How to effectively implement a performance marketing strategy  

One of the main benefits of performance marketing is that agents can run the campaign directly, with little management necessary. After finding the right partner and finalizing the strategy, they will have access to a consistent stream of interested leads that they can trust and control.  

When it comes to finding the right partner, there are several important things to look for:

1. Inbound leads 

First and foremost, it is important to partner with an organization or individual who can offer inbound leads. These are the most effective leads because the consumer has opted in, called themselves or requested more information, so they are already warmed up when they get to the agent.  

2. Exclusive leads 

Finding a partner that does not sell the same lead to multiple different agents increases the chances of  that lead converting because the agent is not in direct competition with anybody else.  

3. Multiple lead types 

Agents should also look for a partnership that offers them multiple lead types. These include clicks, calls and lead forms. This allows for a more encompassing overview of the marketing portfolio and gets agents' offer in front of more consumers, increasing the likelihood of a conversion. Additionally, having access to multiple forms of lead generation will offer insights into the best-performing strategy for an agency. With this information, you can easily determine where the best place to spend your money is.  

4. Flexibility 

Next, finding a partner that offers flexibility of hours and volume of leads is essential. If an agent is running a pay-per-call campaign, but the partner is having too many calls come in during one period, the agent will not be able to properly invest in each lead, and there will likely not be a great conversion rate. Being able to customize the hours, pacing and geographies a performance marketing campaign is targeting will ensure that an agent can stay on top of all their inbound leads. 

5. Budget 

Finally, it is important to keep your budget and pricing limitations top of mind. The right partner will offer you competitive pricing that fits within your budget while providing high-quality and qualified leads. 

Impact on ROI

Through the use of digital performance marketing, agents can control their own lead flow as well as the type of leads they are receiving. At Rate Retriever, we have seen this result in a higher ROI and the continued growth of insurance agencies.  

Lead generation will always be a necessary component of being an insurance agent, and with a good partnership and a performance marketing plan that is customized for the business, agents can succeed.

What a difference a year makes. The "Great Market Reset" of 2023 changed the reinsurance landscape for the better. A focus on underwriting profitability and tighter terms and conditions created a much-needed change that has led to a healthier and a more sustainable trading environment. 

As a consequence of the hard market, the 2023 full-year results from the reinsurance sector are expected to be record-breaking -- with many combined ratios sitting not just under 100 but down well into the 80s. 

In 2024, the first three quarters are set to be just as profitable, setting the tone for the 1/1/25 renewals. 

Further highlights include:

• 2023 was a record year for catastrophe bonds. This trend is set to continue into 2024.
• For the first time in years, investors will receive good news and handsome profits from their insurance-linked securities (ILS) managers.
• Existing and new investors in the reinsurance sector are poised to enjoy the rewards from the reset.
• Lloyd’s will continue to flourish after a number of initiatives to attract new investors and a strict focus on underwriting profits. 
• Brokers will continue to innovate using all the tools in their armory to produce creative solutions for their clients. This will result in clients being better served by the industry. 

Reflecting on the frantic and stressful 2023 renewals

The January 2023 reinsurance renewals were some of the most difficult and grueling in recent memory, resulting in dislocated reinsurance protection as the industry contended with multiple issues. 

The over-supplied and ill-disciplined "tenties" left us with a marketplace that was fragile and ultimately unsustainable. As an industry, we have had to contend with multiple "grey swan" events and not enough fuel in the system to meet the new norm, which was global aggregate insured catastrophe losses of $100 billion-plus versus the previous average in the decade 2012 to 2021 of $85 billion in aggregate.

Opportunity knocks in 2024 as we return to a balanced and sustainable market 

Nobody wanted a repeat of the rancorous negotiations from last year. The market worked together to ensure there were no surprises and a better alignment of expectations. 

There were still some tensions and vigorous discussions around pricing and attachment points. The relationship "dance" among broker, insurer and reinsurer is based on nuanced and established relationships, and a consistent rhythm to discussions returned. 

The words “stability” and “orderly” have been used to describe negotiations in the run-up to the renewals, and this was an accurate and welcome alternative to the frantic and stressful environment experienced last year. 

Specific pockets of some portfolios faced rate increases and restriction of capacity where results have been less favorable due to territories and perils having been affected by losses (for example, Midwest U.S., Turkey, Australasia). However, more capacity entered the traditional P&C markets.

See also: Why Brokers Have a Leg Up in Insurtech

Driving creative solutions into a more stable and secure environment

What does 2024 have in store?

Investors are providing more capacity as confidence returns to the reinsurance market. Investors returning or entering reinsurance for the first time will find a stronger market following the 2023 industry reset, and a highly disciplined environment that will now present the opportunity for incumbents, returnees and providers of fresh capital to achieve long-term value. 

Meanwhile, Lloyd’s of London has opened the door to more investors via more flexible initiatives such as: London Bridge, the Standards Board for Alternative Investments (SBAI) and Syndicate-in-a-Box.

This has resulted in new structures and products coming to markets – such as the recent $100 million property catastrophe bond from Lloyd’s syndicate Beazley transacted via the London Bridge mechanism. This proves that Lloyd’s is a great place for institutional investors to gain access to global insurance and reinsurance risk. 

The Great Reset of 2023 provides the platform for innovation via new technology, enhanced portfolio management tools, lower distribution costs and increased fuel in the system via investment that will lead to a more robust and less fragile market environment. Brokers can continue to develop creative solutions where investors will have the chance to deploy capital into specific programs or market sectors where there are measured opportunities.

There has always been a power struggle for who holds the reins in reinsurance negotiations. There has to be a finely balanced right pecking order, with buyers at the top, brokers responding with service and innovation and the sellers seeking to protect and grow their capital. 

The 2024 outlook is positive for the reinsurance sector, particularly for investors who been calling for a more secure and profitable environment. The industry-wide reset has created a more sustainable market with long-term relevance and new opportunities. 

This has paved the way for entrepreneurial talent to continue to set up their own shops and target the specialist business lines. As a result, the delegated authority and managing general agents (MGA) space is likely to receive a boost as the offering is clearer and more compelling than in previous years. The growth of the U.S. E&S market is set to provide investors and capital providers with opportunity to grow in niche territories and products. Underwriting talent matched with technology will also continue to drive change in the market.

From destructive wildfires and hurricanes to epic snowstorms and floods, 2023 was the worst year on record for billion-dollar disasters. Natural disasters are not only happening more often, but they are also more severe, devastating homeowners and driving up insurance rates. People who live in Lake Arrowhead, California, for instance, pay high premiums because the city is considered at high risk from wildfires and earthquakes. This is the same area where homeowners and tourists were trapped by a severe snowstorm last winter.

The increase in natural disasters is a major contributor to the challenges facing the insurance industry. There is also a widening gap in health insurance coverage, retirement savings and life insurance -- adding up to a $1.4 trillion shortfall in coverage in the U.S. 

With today's digitally savvy consumers, many insurance companies have started their digital journey with AI and data analytics through intelligent automation (IA), business process management (BPM), chatbots and customer experience (CX) portals to meet customer and market demands for speed and convenience at low costs. Many insurance companies are also using new underwriting models and distribution channels to appeal to new customers.

While the increase in adoption of AI-driven policy calculators and application processes demonstrates a shift in mindset for the industry, some firms struggle with implementing a comprehensive approach to using advanced technologies. According to a recent Deloitte survey, of 100 U.S. life insurance and annuity chief information officers whose firms have begun their core system modernization, fewer than a third have completed some (20%) or all (12%) of their initiatives. Just over two-thirds have projects currently underway or in the planning stage.

Intelligent automation provides an enterprise road map to more efficient business processes while enabling insurers to offer personalization of services to attract and retain customers by enhancing the customer experience. AI and BPM are key ingredients of intelligent automation, along with robotic process automation (RPA) and other complementary technologies that can enact change for your organization.

Intelligent automation creates efficiencies using your existing structure and systems, while lifting the burden from your workforce and allowing employees to focus on work with more impact.

Technologies designed to work together are key to achieving the productivity gains promised by digital transformation. There are three steps you can take to help you navigate and understand these technologies, see how they fit into your operation and deploy a model of best practices for implementation.

See also: Automation 2.0: What's After RPA

Step 1

Establish an ideal outcome and return on investment (ROI) target for your intelligent automation, whether that be revenue, cost savings, error reduction, employee satisfaction, customer experience or compliance. Once you have your objective clearly laid out, it will be easier to deploy your automation where it will be most effective.

It is important to have a robust strategy that empowers employees from business and IT to work together to deliver a clear plan. The Gartner Avoiding the 10 Most Common Mistakes in Financial Services Automation report provides insights into how financial services companies can achieve long-term value from their investments. A digital operations center of excellence is essential for a structured approach to automation because it focuses on governance, technology selection and choosing the right automation to deliver business outcomes.

Step 2

Identify bottlenecks or inefficiencies in your operations. One way is through process mining, which analyzes your current processes to determine where improvements can be made. From there, you will be able to set goals, from the quick wins of routine task automation to the orchestration of long-running processes and augmenting intelligent decision-making capabilities to evolve those processes.

Step 3

Deploy an automation operating model, ROM2, that meets your business change management needs to scale automation and orchestration. Using proven methodologies and orchestration management empowers you to establish a solid foundation that enables you to customize, sustain and expand your intelligent automation program. With BPM and RPA, you can coordinate processes and avoid human errors during manual data input, which is especially important in financial services. Errors can cost you and your customers money and lost investment opportunities.

BPM excels at orchestration, but it cannot directly interact with legacy systems. RPA, on the other hand, is great at automating any user interface but struggles with long-term case management. Combining the two creates a complete solution that can tackle end-to-end processes. Robotic process automations are managed with BPM software, using automation wherever possible.

Humans are still accountable to ensure they sign off with the BPM system's delegation and coordination of tasks and processes across human and digital workers. The operational transformation will reverberate across the organization because of the scalability of BPM and RPA used in tandem within an intelligent automation platform.

This setup positions you to manage roles and rules while gathering insights on operational analytics. With both, your business model will mature and help guide you in everything from efficiency to enrichment to total reinvention.

See also: Digital Self-Service Is Transforming Insurance

Manage and mitigate financial risk

There are many reasons why insurers are looking to harness the power of automation and digitization to become more agile and achieve economies of scale. As natural disasters such as hurricanes increase in intensity, it becomes more difficult to predict how severely places will be affected, and officials and residents have little time to prepare. In addition, with diminishing investment returns, vast workflows and growing data volumes, competitive encroachment from non-traditional players, changing customer expectations and rapid advancement of new technologies, it is clear the insurance marketplace is transforming and is looking for digital advancements.

Service diversification is also required as a preventative measure to help reduce customer risk and build customer loyalty, while safeguarding reputations. Intelligent automation platforms and tools enable organizations to improve employee effectiveness and deliver a differentiated customer experience. They help you target strategic priorities, close the digital gap, unlock the full potential of orchestration and digital workers to deliver the necessary transformational business value needed today.

Throughout most of 2023, there was a notable decline in overall marketing spending across personal lines insurance. While there are a number of reasons, including strategic moves by insurers to mitigate the impact of elevated loss ratios brought on by inflation and other economic factors, what has emerged is a deeper investment in digital marketing. 

In fact, through the first nine months of 2023, paid social spending increased by nearly 30%, and online video increased by 3.9% year over year, according to Comperemedia. Though online video advertising was only slightly up in the first nine months, in the third quarter spending was up more than 50%, signifying momentum that should continue through 2024. Perhaps the most notable digital metric comes from TikTok, where P&C insurers increased paid spending almost 200% in Q3 2023, year over year. All of this has occurred despite budget reductions, which have included not only marketing cuts but operating cuts.

What Does This Mean?

First and foremost, let me decry the death-to-direct-mail song that many have sung in recent years. Direct mail is still a pivotal channel in insurance marketing and will remain so for the foreseeable future. In addition, some of the investments toward digital channels can be attributed to the budget cuts many firms have made, because their use can be easily tied to customer conversion and they are seen as more efficient and agile. 

However, the growth in insurance digital marketing in 2023 is not transitory and reflects the larger consumption of digital media overall. In many ways, today’s insurance media mix serves as a precursor to more sophisticated omnichannel strategies. And while direct mail will remain a formidable acquisition tool, over time it will likely be leveraged more as a way to pivot consumers to other channels and less as a bottom-funnel marketing tactic. 

See also: Underwriting in the Digital Age

How Can Insurers Boost Their Digital Strategies?

Several key tips will help insurers as they build out their digital strategies in 2024. They include:

Establish Authenticity

Authenticity is crucial for digital marketing, especially across social media platforms. Insurers looking to maintain authenticity on social channels can do so by first staying true to who they are as a brand while also staying true to the cadence and decorum of the specific platform they are advertising on. This means using TikTok, for instance, in a way that does not disrupt the usual experience for the customer. There is also opportunity to leverage mascots or visual logos to make the brand more personable and “real” on social channels. For brands that don’t use mascots, broadcasting real customer testimonials to play up a network effect would help increase credibility. This route also highlights the quality of a brand’s products, acting as a differentiator in a market that is grappling with high shopping rates and waning consumer satisfaction.  

Develop a Short-Form Video Strategy

Short-form video has become a highly leveraged marketing tool in recent years, as platforms such as Instagram and TikTok have grown in popularity. The momentum behind short-video consumption growth led YouTube to develop its own short-form ad features in 2022 and 2023. Mintel data also shows that video ads are the most recalled ad type on digital channels. Therefore, short videos that are more product-focused are attractive for consumers in the consideration stage of the customer journey and have a downstream effect on conversion. 

Use Digital for Brand Awareness

While many insurers still lean on performance marketing to grow their books, the opportunity for brand awareness marketing will remain strong in 2024, especially as insurers continue to try to boost profitability. Companies should use digital channels to differentiate their brands and show the value they offer customers. Social media remains an effective tool for brand awareness, as 98% of internet users visit social media sites regularly, according to Mintel. However, efforts to boost brand awareness can be incorporated across channels, and firms should remember that using their native social media account is a great, low-cost approach.

Create a "Phygital" Experience

Direct mail still maintains a notable share of overall insurer marketing spending and can be leveraged to direct customers to digital channels. Therefore, by leveraging both physical mail alongside digital channels (phygital), insurers can get the most bang for their marketing buck. Insurers have the opportunity to move customers to digital channels via QR codes or social media handles placed within direct mail pieces that help answer or simplify consumer questions or concerns when purchasing a product. Insurers that rely heavily on direct mail can slowly introduce more digital engagement to test and learn. Adding an entertainment element could also be an effective strategy that casts the insurer in a new light. 

See also: Top 10 Challenges for Insurers

What Will Digital Marketing Look Like in 2024 and Beyond?

Digital marketing has helped carriers as they wrestle with growing their book of business profitably, and this will continue into 2024 as they look to maintain consistent brand awareness and engagement. 

Mintel projects advertisers across all industries spent $257.7 billion on digital advertising through the end of 2023 (up 13% from 2022), and insurers will continue to play their part. Additional factors, such as the growth of television streaming, podcast listenership and gaming across generations, create even more opportunities for insurance marketers. Finally, the rise of AI in marketing will allow for deeper and more sophisticated targeting and personalization, further boosting the digital marketing boom.