Accenture defines embedded insurance as any insurance that can be purchased within the commercial transaction of another product or service. That covers an enormous playing field.

Embedded insurance isn’t new. Purchasing life insurance at the airport before flight departure was “version 1.0” of embedded—a model that turned into an incredibly profitable business. Another evolving embedded model is auto insurance added at point-of-sale together with a car purchase or lease. Again, not a new concept but a continuous area of interest with more recent insurer/car brand alliances. Such household purchases are major life change moments, with an opportunity to switch insurers, hence the constant attention. 

Embedded Insurance Market: Big and Getting Bigger 

The embedded insurance market is expected to grow from $156.06 billion of gross written premiums in 2024 to more than $700 billion by 2029, a CAGR of 35%.

According to Forrester’s recently released research report "Predictions 2025: Insurance," next year insurers will continue to pass on higher costs of claims expenses to customers. Continuing demand for tech and product innovation won’t bear much fruit, despite higher budgets. AI adoption will play a subordinate role to other business priorities. Insurers will increasingly rely on embedded and usage-based products to drive top-line growth and improve customer experience. 

The embedded insurance market is gaining traction due to several factors. First, it has offered a way to reach new customer segments and expand insurance coverage by embedding insurance products into popular platforms or products with large user bases. This approach has allowed insurers to tap into existing customer relationships and offer insurance solutions at the point of need or interest. Warrant protection for electronics, portable devices, and appliances is among the most popular; Allstate, in particular, is dominating the retail space with over 140 million customers since its acquisition of SquareTrade in 2016 for $1.4 billion. Last month Allstate Protection Plans acquired Kingfisher, which repairs, trades in, and upgrades mobile devices.

Embedded insurance addresses the issue of underinsurance or lack of awareness by providing coverage that is relevant and easily accessible to customers. Embedded insurance has even more potential to enhance customer engagement and loyalty. Insurers could create personalized and contextually relevant offerings by integrating insurance seamlessly into everyday products or services. Both traditional insurance companies and insurtech startups have been exploring embedded insurance opportunities, according to Modor Intelligence research. 

See also: Beyond the Hype on Embedded Insurance

Embedded Auto Insurance

Embedded auto insurance integrates offerings into the vehicle purchase journey, expanding the traditional F&I process. It not only makes the buying process easier for customers but provides advantages for dealerships, from boosting customer retention to additional revenue opportunities. While such offerings are not new, greater digitization, real-time quoting, and ease of billing/payment are newer advancements and are making embedded models more effective. 

Embedded Insurance Industry Overview 

The embedded insurance market is lightly consolidated, with few players. Some major global players include Lemonade/Metromile, Slice, Hippo, and Root Insurance. In the study period, market players were also involved in mergers and acquisitions, as well as partnerships focused on expanding their presence. The prospects for growth will likely ratchet up competition, but mid-size to smaller businesses are landing new contracts and breaking into untapped sectors thanks to product innovation and technology improvement.

Embedded Insurance for the Mobile Connected Revolution

Rapidly evolving mobility trends, advancements in connected technology, and rising customer expectations are contributing to increased volatility in the still young embedded insurance ecosystem. According to Capgemini, the rise of autonomous, connected, electric, and shared (ACES) mobility options are projected to reach 40% of the automotive market by 2030. And 42% of policyholders expect a single policy that covers them regardless of transportation mode.

The McKinsey Center for Future Mobility says connected cars are expected to account for 90% of all new U.S. vehicle sales by 2025. Advancements in connected car technology are not only reshaping insurance products and distribution but also redefining consumer relationships and expectations. Original equipment manufacturers (OEMs) like Tesla and Toyota now embed insurance directly into new car purchases. According to the 2024 Embedded Car Insurance Study by Polly, 81% of Millennials and Gen Z desire the option to purchase auto insurance as part of their car buying experience. In fact, 83% of these cohorts reported that they bought some type of embedded insurance with a recent purchase.

Adding or “embedding” insurance products directly into mobility services, including vehicle sales, ridesharing, car rentals, bike-sharing, and even public transportation systems, offers numerous benefits to consumers and service providers alike. Coverage is automatically included as part of the service, providing immediate and comprehensive protection. As demand for changing mobility grows, the potential for embedded insurance increases.

Some Noteworthy Embedded Models:

• Liberty Mutual partners with Jaguar Land Rover North America to provide tailored auto insurance solutions for Jaguar vehicle owners in the U.S. during the car buying process
• Tesla comes with built-in insurance features
• Toyota Auto Insurance is underwritten by Toggle, a digital and embedded insurance company that is part of Farmers Insurance
• Carvana entered into an exclusive partnership in 2021 with insurtech carrier Root to develop integrated auto insurance solutions for Carvana’s online car buying platform
• INSHUR formed a partnership with ride-sharing service Uber in 2018 to embed insurance directly into Uber’s platform, providing on-demand drivers with streamlined, personalized insurance coverage that adapts to driving schedules
• Turo, a peer-to-peer car-sharing platform, collaborates with Liberty Mutual to offer embedded insurance for its users

As technology continues to advance and the mobility sector evolves, the direct integration of insurance products into mobility services will become increasingly common, offering enhanced convenience, personalized coverage, and revenue opportunities for all stakeholders. Embedded insurance will drive mobility forward.

See also: Embedded Insurance: Challenges and Opportunities

Where Agents Fit In  

Through our research on the insurance consumer, we’ve learned that while customers are increasingly comfortable with learning about insurance and comparing options online, they are often not ready to make a purchase before consulting with a human agent. Most customers still pick up the phone to a call center. 

According to Accenture’s Insurance Consumer Study, 85% of consumers prefer to interact with a human when asking for advice on products or offerings. Only 15% conduct their purchase solely online.  

If consumers are looking for human touchpoints when purchasing just one insurance product, they increasingly need guidance when combining multiple, more complex products. As the risk of being wrong about the type of coverage they need multiplies, customers want to be able to rely on a single source of truth to help them sort out their exposure and figure out how to be adequately covered.  

We are sure that agents still have a significant role to play even as some products move toward embedded 3.0. Specifically, we believe that role includes helping customers understand their risk profile and how the coverages and products they buy explicitly or implicitly cover them—including where there might be overlaps in coverage. We feel insurers should pay attention to the relationship between agent and embedded and the implications for carriers, agents, and embedded distributors. 

Headwinds/Tailwinds and Challenges

It is reasonable to question embedded models’ potential distribution channel conflict, the fit for licensed agents, and the viability of the forecasts for enormous embedded premiums. Many wonder just how much of new embedded premiums are a shift from other channels, negating net gains. Will embedded serve as a catalyst for early adopter carriers to take market share from competitors? 

Meanwhile, there are threats of channel conflict, which insurance agencies have encountered since the advent of carrier direct phone sales, followed by the explosion of on-line options. Either way, agents are not only required to legally sell insurance, they are vital in navigating a myriad of insurance complexities and need to be included in embedded insurance model designs. This issue alone is a challenge for the industry, not to mention the commission compensation paradigms that have to be addressed.

The growing protection gap has never been more evident and is anticipated to accelerate. This has been widely demonstrated throughout the last several years with lack of flood insurance. Currently, insurers are re-tooling insurance policies to limit or exclude coverages in reaction to soaring loss costs and part of multi-pronged strategies to restore profits. 

Consumers and businesses alike are raising deductibles, dropping coverage, and “self-insuring” to blunt the impact of seemingly endless premium rate increases. These tectonic changes set the stage for new insurance products, including parametric and interval coverage, gap protection, and yet-to-be-developed solutions – all of which are likely to be added on and outside of existing policies. The protection gap alone creates significant tailwinds for forward-minded carriers, MGAs and insurtechs willing to enter the P&C space.

Looking Ahead

Whether you are an insurer, insurtech, agent, broker, MGA, retailer, wholesaler, or anywhere else in the insurance ecosystem and supply chain, you must invest now in learning how your business can participate in the embedded economy of the future.

Paul Carroll

I’m hoping to ask you questions about a host of issues, but let’s start with what artificial intelligence is doing in workers’ compensation. 

Bill Zachry

The amount of change that’s happening right now is awe-inspiring.

For instance, these large language models can take a lot of disparate records, put them in logical order, summate them, and give the information to the doctors in a way that they can turn into a medical-legal report. One of the problems the system has had is that there is a lack of consistent quality in the medical-legal process. AI can improve the consistency and quality of the reports. I'm very optimistic about that piece of the puzzle. 

I'm a former claims adjuster, and I think of the system in terms of a linear progression: Start with prevention, move on to benefit provision, and then close/settle the claim.

Starting with injury prevention, I’m an adviser to Voxel, a fascinating AI company that takes videos within store distribution centers and uses AI to determine whether employees are lifting safely, whether they're wearing PPE equipment, whether they're driving the forklift safely, and whether they're following other safety-related guidelnes. AI has so many applications. 

Paul Carroll 

At The Institutes, of which ITL is an affiliate, we've been talking a lot about the potential for the insurance industry to move toward a Predict & Prevent model, so I'm interested to hear more about the prevention piece of the puzzle. 

Bill Zachry

With Voxel, for instance, they provide a safety score and give the safety people specific examples of opportunities for improvement. They’ll show when people aren’t wearing their safety hats or gloves. Then the safety people go back and work with the front line. One of their big clients had a 64% drop in claims frequency, and that technology is only getting better. 

Some areas, like construction sites, are harder than distribution centers, warehouses, and retail facilities. The change in exposure is so rapid. But I think we’ll eventually get to those tougher areas, too.

Paul Carroll

I assume a focus for the future will be on getting the feedback to be in real time, or at least near real time.

Bill Zachry

It’s already there. The real challenge is making sure everyone is engaged so they will not only get the information but also be motivated to act on it. 

The next focus after prevention is claims administration.  As the U.S. has gone from a manufacturing to a service economy to a tech economy, claims frequency has been declining for decades. That trend is going to continue. But there’s been a slight uptick in severity. Severity is actually not what used to be considered severity. Severity used to be quadriplegics, burns, amputations, head injuries, hospitalizations, and things like that, which are decreasing. Now we’re experiencing claims that drag on for reasons beyond the physical injuries. 

At Safeway, we had managed care nurses call every injured worker and run them through a questionnaire to gauge the risk of delayed recovery. Identifying and intervening with the at-risk employees cut our claims costs by 40%. These are employees who had experienced what are called “adverse childhood experiences.”

My experience was that the bottom 50% of the claims only accounted for 10% of my loss dollars, while, the top 3% accounted for 60% of my dollars. I found that if I identified that 3% within the first couple of weeks and set a program to get the cases closed within the year, I could cut more than 40% of my claims costs.

Doing that is one of the big opportunities in this industry.

Paul Carroll

Is this mostly an issue of staying away from litigation?

Bill Zachry

We found that if we identified and intervened appropriately with the “at risk” injured worker, they would not litigate. These were employees who responded well if you told them that you were going to take great care of them; and if we did, it was amazing how they responded. We also found that if they had already gone to an attorney, they'd already jumped off the cliff, and there was nothing we could do to help them. They were not interested in recovering and returning to work. 

You have to be careful because every incentive has an unintended consequence. For instance, when you have safety programs, people don't want to report claims, because there are usually financial incentives for maintaining a low claims frequency. 

One of the biggest challenges in the workers’ compensation system is how injured workers’ representation is paid. That drives behaviors and drives results. An injury's optimum result is getting the right care at the right time and getting the person healthy and back to working with zero disability. Well, zero disability means the applicant's attorney gets no money. So, time and again, I see the applicant’s attorneys maximize the disability to maximize their revenue, even though that’s to the detriment of the injured worker, who can’t return to their normal job even though everything else means they could have. If you focus on returning to work instead of maximizing disability, you get a lot more people back to work.

At Safeway, we realized that there were these unintended consequences and that you have to adjust your incentives every year to account for the problems of misplaced incentives. For example, we charged the facility for every injury. Then we offered to reduce the “chargeback” if the facility provided light or modified duties. Early return to work maximizes recovery and reduces lost time.  We also did not charge the store for the hours that the employee was working while doing lighter duties. On the other hand, if the store did not report the injury within 24 hours, we added $5,000 to their charge. You have to be aware of each unintended consequence and adjust the incentives as you go along. 

Another impact of AI will be on promptly approving the right care for the injured workers. 

I believe many claims administrators will use AI to improve treatment based on the diagnosis. They will be approving the care automatically. However, there are potential unintended consequences. For instance, there is no study on the percentage of claims misdiagnosed in workers' compensation. For the 50% of the claims that account for only 10% of the loss dollars, it doesn't really matter. You could diagnose a stubbed toe, and the issue could be the right thumb, but if you just leave the worker alone, let them get treatment and go back to work, you'll be fine. It's that top 3%, 5%, 8%, or 10% of the claims where, if you have a misdiagnosis, then the treatment can really go in the wrong direction. We don’t have the guardrails to deal with a misdiagnosis, so how do you identify a misdiagnosis?

If the claims administrator is using AI to approve treatment based on the diagnosis, they should also use AI to determine if they are getting the results that they want to get in an appropriate time frame. And if we aren't, what's going on here that we should be looking at?

Paul Carroll

Where else do you see room for progress?

Bill Zachry

We have opportunities to reduce the time between requests and treatment approval; I think that can be automated.

We also need to use evidence-based treatment guidelines nationwide. For instance, according to the American Medical Association, most diagnosed carpal tunnel syndrome issues are not usually caused by work. They're caused by genetics and other problems, such as age and sex. But in California, where you need only one iota of exposure, it's 100% compensable, while in Colorado and Texas, it's very rarely considered compensable. I think getting a standard for compensability is one of the opportunities.

There is also the expansion of work presumption injuries for public safety officers. I truly appreciate what the public safety officers do and how they do it. They are the ones who are running toward the gunshots and toward the fire. But the comp system is being used inappropriately. It’s evaluating them as though they should be able to run as fast, jump as high, and lift as much when they’re 45 or 55 as when they were 25 or 35. That’s just not realistic, and generally, they already have medical care 24/7. 

I think there’s an opportunity with gig workers based on a black cab company program in New York City. Uber and Lyft drivers are considered independent contractors in California, but if we added a 3% surcharge to everything they do and used those funds to create a program for them, we could get them covered for workers’ comp through the State Fund, or whatever. 

Another impact of AI is that much of the compliance work currently done by examiners or assistants will, I think, disappear. The examiner's new focus will be on the relationship between the examiner and the injured worker, trying to find out what the injured worker needs and wants. I think that's something that can't be done by AI. I think it has to be done through creating a relationship.

In the Medicare Set Aside process, which is focused on making sure that the cost is not shifted from workers’ comp to the Social Security system or to disability, it costs several hundred dollars to have a nurse pull all the data together and generate a report. But I’ve seen prototypes where you press a button and get the report in two seconds. The cost is eight cents. The piece of the industry that generates those reports will go away. 

If you look out to the future, based on technologies like CRISPR, I'm very optimistic about what we can do for severely injured people. You can even see a day when we won’t have disability.

Paul Carroll

That's a fascinating idea that never occurred to me, but I love your optimism, especially in light of all the other promising ideas you’ve presented. Thanks for the time and the insights, Bill.

About Bill Zachry

William M. Zachry is a board member of the California State Compensation Insurance Fund, appointed by governors Arnold Schwarzenegger and Jerry Brown. He is currently program co-chair of National Comp.  He served three years as a senior fellow at the Sedgwick Institute. Zachry was awarded the Summa Comp Laude award in November 2020, the RIMS Risk Manager of the Year 2014, the CCWC Workers Compensation Professional of the Year 2016, Co-Chair AMICUS Committee California Chamber of Commerce. He is the former GVP risk management at Safeway /Albertson's, former board member California Self Insurers' Security Fund, former co-chair California Chamber of Commerce AMICUS committee, chair California Fraud Assessment Commission, Zenith Insurance VP claims, HIH (C.E.Heath) (Care America) S.V.P. claims. references.

There was a day when life insurance agents sat at a customer’s kitchen table and got to know them personally. Sure, a policy was agreed to and sold, but the bond between the agent and the customer was at the heart of the deal.  As the saying goes, insurance isn’t bought – it is sold. 

Today, technology is slowly replacing the human touch, with AI-enabled bots answering phones, responding to chat inquiries, and pushing products to customers over every available communications channel. The industry has, by and large, moved away from the people business and closer to transaction management.

AI solutions abound that sift customer purchase data, social sentiments, and risk profiles to offer fitting products that improve loss ratios and enhance margins. But these solutions are available to all insurers.

See also: How AI Is Changing Insurance

What can an insurance firm do to substantially differentiate from the competition? What can a firm do to cement relationships and turn customers into raving fans?

The answer is to provide customers with a more meaningful human experience. We are all born for human connection. Robots are incapable of providing it. So, what shall we say of AI in light of this reality?

AI can greatly support the promise of personal service. Bots can provide a frictionless experience, elevating and accelerating customers; journey from shopping to buying to ultimately becoming partners on a life insurance journey that covers myriad life events.

AI can liberate team members for more one-on-one, personal interactions. It can eliminate the repetitive tasks that dehumanize employees and free customers from having to input personal information again and again as they shuffle from one contact to the next. It can heighten the experience by leveraging massive stores of data to serve up poignant insights, at the exact moment of truth, when it matters most.

“I hear you’re struggling to make it to rehab, Ms. Jones, because you have three children to care for. Can I suggest some assistance? Share a few links?”

“It looks like your employee is going to be out for longer than a brief absence, Mr. Smith. Have you considered shifting to Social Security disability? It might offer some relief.”

“Mrs. Anderson, we noticed your recent inquiry about long-term care options. Based on your existing life insurance policy, you're eligible for a rider that allows you to use part of your death benefit to cover long-term care expenses. Would you like to discuss this option in more detail? Additionally, we can provide resources on home healthcare services available in your area, as it seems you prefer aging in place based on your recent conversation.”

See also: How AI is Redefining Insurance Pricing Strategies

These human contacts, supported by AI, in context, can transform the way you relate to your customer. Individual or group, it doesn’t matter. Insurers deal with people who experience real-world life events. Imagine what a difference it would make if your people were free to suggest practical ideas and support, at just right moment, to a customer in need.

In the hospitality industry, a concierge is a guest’s most important contact for everything from arranging travel to securing play tickets to running errands. Could it be that AI is the key enabler that launches a new era of “concierge” service in your firm?

Technology alone doesn't create relationships, but when paired with human empathy it can elevate customer experiences to levels never before possible.

Check back in as we continue to explore how you can leverage data-driven insights and prioritize consumer needs, using AI as a pathway to compelling customer service.

Cybersecurity Ventures predicts payouts from cybercrime victims will reach $10.5 trillion by 2025. Cybercrime has become the world’s most profitable non-government enterprise—larger than the illegal drug trade and larger than all but three of the world’s national economies. In this environment, companies large and small are turning to cyber insurance for protection. 

For insurers, though, it’s challenging to gauge each client’s level of risk. Understanding the risk from cyberattacks requires highly accurate assessments of each client’s security defenses, assessments that are increasingly difficult to obtain as cyber-defense technologies evolve and attacks become more complex.

See also: The Evolving Landscape of Cybersecurity

Elements of Attack Prevention

The keys to good protection are visibility, threat detection, and rapid responses to attacks.

Visibility must include all IT and operational technology (OT) infrastructure, including the network, servers, endpoint devices, applications, cloud instances, and user behavior.

Threat detection mechanisms must be able to identify anomalous conditions and also to correlate multiple threat signals to reveal multi-vector attacks. (Most large cyber-attacks use a combination of tactics – tactics that may individually seem innocuous.)

Rapid responses are essential because the more time hackers have inside the infrastructure, the more damage they can do. Many large thefts of personal financial information from businesses like AT&T or Target weren’t discovered for months. Many cybersecurity tools now use AI to improve response time.

Insurers should strongly encourage clients to implement these mechanisms.

See also: As Cybercrime Advances. Cybersecurity Must Keep Up.

Gauging the Risk

For insurers, it’s essential to stay abreast of the evolving cybersecurity landscape (attack types as well as how they’re being defeated),and to have accurate sources of data that reveal each client’s readiness for cyberattacks. 

Larger companies that staff their own security operations (SecOps) facilities can provide readiness metrics that show how and where attacks are occurring and how they’re being stopped. These reports are typically prepared for upper management by the IT managers or directors, but there’s no reason insurers shouldn’t have access to them, as well.

Small and mid-sized companies without the resources to staff a SecOps center may outsource the function to managed security service providers (MSSPs), and these services can also provide client readiness reports to insurers. Ideally, SecOps centers and MSSPs can be a bridge between insurers and prospective clients.

Insurers working with small or mid-sized companies can use MSSPs to open a new channel through which to reach potential customers, access expert regulatory compliance support, and create individually tailored coverage that meets their needs as well as their clients’ needs.

For potential clients, readiness reports and referrals to participating cyber insurance groups streamlines the process of obtaining insurance.

By partnering with cybersecurity providers, insurers gain expert advice about whether potential clients are protected, along with direct access to a new funnel of prospective clients from providers those clients already trust.

Agencies aiming to reach their peak performance must consider the full roundtrip of the insurance lifecycle. A seamlessly connected digital roundtrip that connects each stage of the insurance lifecycle enables agencies to grow, operate, and control their business more effectively.

Put more simply – think of the Digital Roundtrip as a track and the agency as a relay team running to each step of the insurance lifecycle. Agencies need to go from the beginning of the customer relationship, starting with marketing and sales, to searching for appetite and quoting risks, to customer payment, and back around again to providing the customer with their bound policy. 

Connecting all systems, people, and stages of the client journey and policy lifecycle makes the baton handoffs much easier. Let’s take a lap around the track to see how the Digital Roundtrip of Insurance works and how it can help your agency achieve peak performance.

Leading Off With Core Automation

The first leg of the race starts in the system you log in to every morning: your management system. This is your agency's core platform. It should have capabilities that automate operations and allow you to communicate better with insurers and policyholders. Your technology foundation should allow you to leverage essential tools, such as customer relationship management, sales automation, financial accounting, reporting, and policy and benefits administration. 

When choosing your agency management system, it’s important to look for one that has an open and scalable architecture. This will allow you to pass the baton on to your relay partners outside of your core team. You get the choice to integrate with the partners you chose as part of your tech stack, allowing for seamless workflows between applications without ever leaving your core system.

Race to the Market

When a customer brings a risk to an agent, the next leg of the Digital Roundtrip relay is to search for markets and quote the business. This process is incredibly tedious. Searching for markets, completing the necessary paperwork and navigating multiple individual carrier portals is incredibly time-consuming. There is also a lot of potential for errors due to the repeatedly rekeying information. 

This is where a commercial lines quoting tool comes into play. By integrating this tool with your management system, you can have the baton automatically handed off. Information flows from the management system to the commercial lines quoting tool, saving time and errors caused by manual data entry. These tools also give your agents a better view of the coverage available, in real time. They will be able to offer clients the best possible coverage for their risk, without spending as much time searching for markets.

See also: A Data Strategy for Successful AI Adoption

Winning Gold

The next leg of the Digital Roundtrip race is digital payments. While you may not get paid with a gold medal, digital payments helps you get paid faster and allows you to provide a better experience to your customers. Collecting checks and cash has been a steady state process for decades, but it doesn’t come without its challenges. It requires a significant amount of time and expense to your business and slows payment reconciliation. Not to mention the security risk of keeping cash and checks with client bank account information in a desk drawer where anyone can gain access.

Implementing a digital payments solution that integrates into your management system and customer portal will make the payment process seamless for your staff and your clients. This will automate payment collection, processing, and reconciliation. It eliminates the manual application of credits to debits and reduces operational overhead by automatically applying the credit once the payment is processed. The integration with the management system also allows for a paper trail of invoices and notifications to be tied to the client’s account, giving your staff a single view of that client.

On the customer side, digital payments solutions provide the effortless, modern checkout experience that internet-savvy consumers have come to expect. You are able to provide your clients with access to their bills through multiple platforms. Whether it be a payment portal, a secure link, or an invoice, your clients can pay their bill in the way they are most comfortable. They can also use credit cards or ACH – no more relying on checks and cash. Integrating your payments solution to your customer portal means your client’s information will flow seamlessly between platforms, requiring the client to enter minimal information when paying their bill.

See also: Characteristics of an Effective Change Agent

Compete and Win at Digital Speed

Now we’re in the home stretch. We’ve come right back around the track to the management system, where all this information is stored. This will give your team easy access to all the information they need to service and remarket accounts at renewal. 

Creating a seamless end-to-end policy lifecycle for all stakeholders eliminates the frustrating, time-consuming, expensive, and broken processes in insurance today and provides high-value experiences that help agencies find the best markets, effortlessly submit business, and quote and service clients faster. 

With the right tools, your agency can easily embrace the Digital Roundtrip to compete and win at digital speed!

In today's hyper-connected digital landscape, enterprises need to have a robust and cost-effective security service edge (SSE) solution to combat increasingly sophisticated cyber threats and to support digital transformation. While the foundational technologies behind SSE, such as secure web gateways (SWG), cloud access security brokers (CASB), and zero trust network access (ZTNA), have been around for over a decade, their integration into comprehensive SSE solutions has become more prominent and sophisticated over the past five years or so.

But as the market has evolved and expanded to over 30 vendors, some cracks and shortcomings are beginning to show. A number of these SSE offerings and vendors are starting to create technical or business challenges for their customers. So, if you are using a "legacy" SSE product (or even if you just have a couple of standalone point products), how do you know when it's time to upgrade or replace your existing SSE solution?

See also: Top 10 Challenges for Data Security

Here are the top 10 signs it might be time to make a switch if your SSE solution or vendor is falling short in any of these areas:

1. Increased Security Incidents: The rapid evolution of cyber threats means that SSE solutions must continually update and adapt. If your organization experiences a rise in security breaches, malware infections, or other cyber threats, it may indicate that your current SSE solution is no longer effective. This is especially true if you're still using a first-generation virtual private network (VPN) solution for remote access.
2. Latency and Poor User Experience: Many SSE solutions centralize security inspection processes in specific points of presence (PoPs). If these PoPs are not optimally located, it results in increased latency and poor user experience. Persistent slow network performance and frustrated end users are clear signals that your current SSE is insufficient.
3. Fragmented Architecture: Some SSE solutions are built on fragmented architectures, leading to management complexity, data sovereignty concerns, and inconsistent security enforcement. If your vendor's SSE solution is cobbled together from disparate parts, it's time to find a unified platform.
4. High Total Cost of Ownership: Stories abound about inflexible and inflated pricing, hidden fees, complex licensing models that combine per-user and per-bandwidth elements, frequent licensing changes, and unexpected cost increases at renewal time. If the cost of your SSE solution is high but does not deliver proportional value in terms of security, performance, and support, consider switching to a more cost-effective vendor.
5. Inadequate Support and Response Time: Slow or unhelpful customer support during critical incidents suggests that the vendor may not prioritize your needs effectively. If you are having problems with slow response times, insufficient technical guidance, or a lack of responsiveness during critical incidents, it's time to look for other solutions.
6. Weak Backbone/PoP Network: A strong PoP backbone network ensures low latency, high availability, and consistent security enforcement. But PoP coverage is a mixed bag. Some vendors have fewer PoPs than you would expect, some limit the number of PoPs customers can use, and a number of vendors do not run all their services on all their PoPs. If your vendor has a weak PoP network or runs services primarily over public clouds, consider evaluating alternatives.
7. Complex Deployment and Management: If deploying your SSE solution is complex and time-consuming, requiring multiple consoles and significant effort to integrate with existing systems, it may be time for a change. Look for solutions that provide easy deployment and unified management.
8. Lack of Innovation and Modern Features: Most SSE solutions incorporate a basic set of security capabilities, including secure SaaS and private application access, threat protection, and data protection. If your legacy solution lacks features such as sandboxing, advanced data leak prevention (DLP) functionality, digital experience management (DEM), and customizable reporting, it may be time for an upgrade. Advanced capabilities like AI-based threat detection and support for clientless use cases are essential for modern SSE solutions.
9. Insufficient Reporting and Analytics: For effective SSE security, it's important to see the entire security fabric, including users, devices, cloud gateways, and how traffic is traversing the internet. If your vendor struggles to offer detailed visibility and analytics, making it challenging to assess your security posture and respond to threats, it's clear you need to consider alternatives.
10. Weak SASE Road Map: SASE represents the future of SSE, offering a unified, scalable, and flexible approach to securing and managing modern enterprise networks. Some vendors offer simplistic "toy" traffic forwarders that they call SD-WAN but are not enterprise class. Others don't support key connectivity use cases like site-to-site connectivity, zero trust in the branch, lateral movement prevention in the branch, and dynamic protocol security. If your vendor lacks a compelling SASE roadmap, it's time to find a vendor that embraces this concept.

See also: The Evolving Landscape of Cybersecurity

As you see the renewal date for your SSE solution approaching, it's time to think about whether you're getting what you need from your platform. If you see any of the above issues in your current SSE solution, consider looking for your "next generation" solution. There are some good, tightly integrated products on the market to help you best meet your requirements, resources, and budget.

The insurance industry is leveraging artificial intelligence (AI) to overcome operational challenges and enhance customer experiences. As carriers and agencies adapt to hybrid work models, AI-powered tools are becoming increasingly crucial for maintaining efficiency and quality of service.

Revolutionizing Contact Centers

One of the most significant applications of AI in insurance is in contact centers. Real-time coaching and sentiment analysis are revolutionizing how agents interact with customers. By transcribing and analyzing conversations as they happen, AI can provide immediate suggestions to agents, helping them steer difficult conversations in a positive direction. This not only improves customer satisfaction but also aids in agent development, especially in remote or hybrid work environments where traditional “sit-with” coaching methods are no longer feasible.

See also: How AI is Redefining Insurance Pricing Strategies

Enhancing Customer Relationships

The integration of AI with customer relationship management (CRM) systems is creating a more holistic view of policyholders. By connecting various communication channels and automatically updating customer profiles, insurers can provide more personalized experiences. This omnichannel approach ensures that, regardless of how a customer chooses to interact—be it through phone, email, or chat—their information and history are readily available to agents, leading to more efficient and satisfying interactions.

Streamlining Claims Processing

AI is making significant strides in claims processing, particularly in handling first notice of loss (FNOL) during catastrophic events. Intelligent virtual assistants (IVAs) are being deployed to manage the surge in claims during disasters, ensuring that policyholders can initiate claims quickly without long wait times. These AI-powered assistants can gather initial information empathetically, set proper expectations, and seamlessly hand off to human agents when necessary, all while providing a consistent claims experience.

See also: How Gen AI Will Revolutionize Claims

Improving Underwriting and Risk Assessment

The use of AI in insurance operations extends beyond customer-facing roles. It’s also enhancing underwriting processes by triaging incoming documents and providing quick insights to underwriters. This accelerates decision-making while still maintaining human oversight for complex cases. Moreover, AI is being used to analyze large volumes of interaction data to identify trends, potential coverage needs, or underwriting issues that might otherwise go unnoticed.

The Future of AI in Insurance

Looking ahead, the integration of AI with other technologies like telematics and Internet of Things (IoT) sensors promises to make insurance more proactive and predictive. Insurers will be better equipped to help policyholders prevent losses, whether by warning of impending weather events or suggesting safer driving habits. This shift from reactive to proactive insurance not only benefits policyholders but also helps insurers manage risks more effectively.

As the insurance industry continues to evolve, the successful implementation of AI technologies will require careful change management and a focus on both employee and customer experiences. The goal is not to replace human interaction but to augment it, creating more efficient operations and more meaningful customer engagements.

To learn more about how AI is driving insights and overcoming operational challenges in the insurance industry, watch our recent webinar on the subject, sponsored by RingCentral and featuring expert insights and real-world examples.

I'll keep this short this week because I feel like I've been holding my breath for months in advance of Election Day in the U.S., and my lungs are about to explode. Maybe my brain, too. Many of you probably feel the same way.

But I do want to share some thoughts about a smart white paper from BCG that looks at the progress that has — and hasn't — been made on using artificial intelligence in business in the nearly two years since ChatGPT debuted. 

The headline numbers: While 98% of companies are experimenting with AI, only 26% have moved beyond the pilot stage and are generating value. Just 4% are at what the consulting firm  considers to be the forefront of implementation of generative AI.

Let's take a look at who the leaders are, where they're generating value, and how insurers stack up.

The BCG white paper opens with the enormous potential for AI: 

"A financial institution is committed to achieving $1 billion in productivity improvements, in addition to enhanced risk outcomes and better client and employee experiences, by 2030. A biopharma company is chasing $1 billion in value potential (revenues and costs) by 2027. A major automaker expects to cut its cost of goods sold by up to 2% and accelerate product development by 30%."

Based on research into more than 1,000 companies worldwide, BCG found:

"Over the past three years, leaders’ revenue growth has been 50% greater than the overall average. Their total shareholder returns are 60% higher, and they gain 40% higher returns on invested capital. These companies also excel on nonfinancial factors, such as patents filed and employee satisfaction, and they are in pole position to benefit as AI platforms and tools mature."

BCG says six factors distinguish the 26% of companies it identifies as leaders:

• They focus on the core business processes as well as support functions. 
• They are more ambitious. Leaders’ expectations for revenue growth from AI by 2027 are 60% higher than those of other companies, and they expect to reduce costs by almost 50% more.
• They invest strategically in a few high-priority opportunities to scale.
• They integrate AI in efforts both to lower costs and to generate revenue.
• They direct their efforts more toward people and processes than toward technology and algorithms. Leaders follow the rule of putting 10% of their resources into algorithms, 20% into technology and data, and 70% into people and processes.
• They have moved quickly to focus on GenAI. 

How does the insurance industry stack up? 

BCG says insurance is somewhat above-average on the firm's AI maturity curve and adds that the industry does especially well in using generative AI in core processes. On core processes, BCG ranks insurance fourth in a list of 17 industries, behind only software, media, and fintech.

BCG says insurers' biggest challenges "involve people and processes: improving staff AI literacy, prioritizing opportunities over other concerns, and establishing ROI for identified opportunities. They also wrestle with the tasks of integrating AI with existing IT systems and of increasing the accuracy and reliability of AI models."

I'd second what BCG says about insurers using AI in core processes. I've been impressed, in particular, with what I've seen concerning claims and underwriting and, to a slightly lesser extent, with agents and brokers. 

I'd also say that insurers have done well on the final two of BCG's list of six characteristics of leaders. The insurance industry has focused on people and processes, rather than just on the technology, and has moved quickly.

On the other three characteristics, I'm less sanguine. 

I'm not sure that insurers have been as ambitious as they could be. Even with claims, underwriting, and agents and brokers. the emphasis has been on making current processes more efficient — a worthwhile goal, to be sure, and one that can deliver quick benefits — rather than trying to reimagine processes or to plumb AI for insights on risks and customers. 

I hear about fairly diffuse efforts on AI, not the "few, high-priority opportunities" that BCG saw at the leaders. 

And I don't see a lot of focus on using AI to generate revenue, rather than just cutting costs. 

So I'd say insurance is off to a good start on generative AI — but still has an awful lot of opportunities in front of it and could benefit from some more ambition and focus, especially on revenue opportunities.

Along those lines, we're starting an AI newsletter later this month that will highlight not just articles on ITL but from around the Web and from consulting and research firms that provide examples of innovative successes and that explain how the rest of us can emulate the leaders. 

Please keep an eye out for it.

And, in the meantime, if you're eligible, please vote.

Cheers,

Paul

As insurance leaders navigate an increasingly dynamic market, the ability to understand and predict future risk has never been more critical. Predictive modeling offers insurers powerful insights into risk, but implementation often takes a backseat to ongoing IT projects, including the costly modernization of legacy systems and efforts to clean up fragmented data. For insurers, deferring predictive modeling due to IT prioritization bottlenecks can be costly, as competitors that leverage advanced risk assessment capabilities surge ahead. The rise of no-code predictive modeling tools, however, provides an efficient and low-resource alternative, empowering insurance companies to derive immediate value without overburdening IT. 

The Costly Modernization Bottleneck

Most insurance companies are heavily dependent on legacy systems, many of which are decades old. Modernizing these systems is a complex and costly endeavor. For example, the replacement of a core policy administration system could cost an average of $15 million for mid-sized companies, with expenses for large insurers reaching well over $100 million. These transformations also take significant time, often stretching three to five years. According to Bain & Company, insurance transformation projects regularly run at least 25% over budget and far beyond initial timelines.

These large, resource-intensive IT projects are essential, but they consume a disproportionate share of IT bandwidth, leaving little room for new, high-impact initiatives. As a result, high-value solutions like predictive modeling often get sidelined or postponed until “someday”—a day that may never come, given the perpetual backlog of modernization needs. A McKinsey study reports that 60% of insurers see legacy technology as a major barrier to innovation, restricting their ability to respond to market dynamics and address emerging risks effectively. This creates an operational drag that limits agility and hinders competitiveness.

The Data Challenge in Insurance

To make predictive modeling effective, insurers need reliable, high-quality data—a challenge in itself. Insurance companies have access to vast stores of data but struggle to harness it due to fragmentation and lack of integration across legacy systems. McKinsey highlights that insurers only make use of around 15% of their data effectively, which constrains their ability to leverage insights for proactive decision-making. Cleaning up and organizing this data is often viewed as a prerequisite to predictive modeling, leading to years-long delays in implementation.

The intense focus on data cleanup detracts from the more strategic objective of predictive modeling, which could provide actionable insights into future risk, underwriting, and pricing. With the cost of poor data estimated to be around $3.1 trillion in the U.S. alone, insurers stand to benefit greatly from platforms that can leverage existing data while mitigating the need for extensive cleanup. Here again, no-code predictive modeling offers a solution that sidesteps these constraints by simplifying the integration of fragmented data into models that deliver meaningful insights.

Predictive Modeling and Its Strategic Benefits in Understanding Risk

Predictive modeling has proven indispensable in understanding risk and enhancing underwriting precision, but insurers that delay its adoption risk losing their competitive edge. With predictive analytics, insurers can identify patterns and trends that inform decisions on policy pricing, risk exposure, and fraud detection. McKinsey estimates that predictive analytics in underwriting alone could unlock as much as $1.3 trillion in annual value across the insurance industry by 2030. Further research by Deloitte reveals that insurers using advanced predictive analytics in underwriting have improved loss ratios by up to 20% and enhanced customer retention through more accurate pricing and personalized offerings.

Predictive modeling enables insurers to approach risk assessment with increased sophistication, analyzing factors like climate change, shifting demographics, and economic volatility. This approach not only improves the accuracy of pricing models but also equips insurers to anticipate and mitigate potential claims spikes. Companies that implement predictive modeling can rapidly adapt to these changing conditions, setting themselves apart from competitors still reliant on traditional risk assessment methods.

No-Code Predictive Modeling: A High-Impact, Low-Resource Solution

For insurers burdened by IT resource constraints, no-code predictive modeling platforms offer a compelling solution. These platforms empower non-technical teams to build, test, and deploy predictive models with little or no IT support, allowing insurers to harness data science capabilities efficiently without disrupting ongoing IT projects. According to Gartner, by 2025, 70% of new enterprise applications will use no-code or low-code technologies, underscoring the growing preference for rapid, scalable solutions.

The benefits of no-code predictive modeling for insurers include:

1. Reduced IT Dependency: With no-code platforms, actuaries, underwriters, and other insurance professionals can directly engage with predictive models without waiting for IT resources to free up. This accelerates the implementation of high-impact solutions while allowing IT teams to continue focusing on long-term modernization initiatives.

2. Faster Time-to-Insight: Traditional predictive modeling projects often take months, if not years, to complete. No-code platforms enable insurers to build and deploy models in a matter of weeks, providing quick access to insights that can guide decision-making. This agility allows insurers to respond to emerging risks and evolving market conditions more effectively.

3. Cost Savings: No-code platforms eliminate the need for custom coding and specialized data science expertise, reducing the costs associated with developing predictive models. In contrast to the high costs of traditional development, no-code tools are typically more affordable and generate faster returns on investment.

4. Data Accessibility and Utilization: No-code predictive modeling platforms are designed to integrate seamlessly with existing data sources, allowing insurers to work with their current data assets without requiring exhaustive data cleanup efforts. This capability helps insurers to leverage their data more effectively and make informed decisions based on actionable insights.

5. Empowering Business Teams: By enabling business users to work directly with predictive models, insurers decentralize data science capabilities, allowing departments like actuarial and underwriting to lead predictive analytics initiatives. This not only accelerates innovation but also aligns model-building efforts more closely with business needs and objectives.

Staying Ahead of Competitors in Risk Precision

No-code predictive modeling not only accelerates the pace of innovation but also sharpens an insurer’s competitive edge. The insurance landscape is becoming increasingly data-driven, with companies that leverage advanced analytics gaining a distinct advantage. Deloitte’s research shows that insurers with strong data science capabilities are 30% more likely to experience growth in customer acquisition and retention, and McKinsey predicts that companies with advanced analytics capabilities will see a 20–30% improvement in efficiency. 

Insurers that proactively adopt predictive modeling will be better positioned to offer competitive pricing, identify and mitigate risks, and respond to regulatory requirements. This positioning is crucial as emerging risks—from climate change to cyber threats—create new complexities in risk assessment. By leveraging the latest predictive modeling capabilities, insurers can shift from reactive to proactive risk management, differentiating themselves in a market that values risk expertise.

Aligning IT Prioritization with Strategic Goals

For insurers, delaying improving the accuracy of their risk precision due to IT backlogs presents an opportunity cost that can no longer be ignored. Rather than viewing predictive modeling as secondary to system modernization, senior executives can adopt a balanced approach to IT prioritization, enabling short-term high-impact projects to move forward while longer-term initiatives remain on track.

By championing no-code predictive modeling, senior executives can enable their companies to maximize value from their existing data assets without compromising IT goals. No-code solutions bridge the gap between current operational demands and future data-driven objectives, enabling insurers to gain immediate insights and maintain strategic momentum.

The Takeaway For Carriers

In a competitive market, the ability to understand and predict risk is indispensable. For insurance companies, no-code predictive modeling platforms offer a powerful and practical way to harness predictive insights without waiting for the resolution of long-standing IT projects. By incorporating no-code solutions into their digital strategy, insurance executives can avoid costly delays, empower their teams, and stay ahead of competitors in risk understanding and mitigation strategies.

Now is the time for insurers to act. As McKinsey estimates, the potential value of predictive analytics in underwriting and risk assessment is monumental, and turnkey solutions provide a path to capturing that value today. By leveraging this technology, insurance leaders can not only enhance their companies' understanding of future risk but also secure their positions as forward-thinking leaders in an industry poised for transformation.

Shannon is a Tedx speaker and has coached dozens of data and Insurtech startups, advising Fortune 500 clients on analytics strategy as head of Client Management for a national health-tech company and Co-Founder of BetaXAnalytics, a company that pioneered emerging data science techniques using AI to remove the barriers to transparent and actionable data. She also spent 12 years with Amica Insurance running branch sales and service operations across the country. She holds a Master of Science degree in Insurance Management from Boston University.

Sponsored by ITL Partner: Pinpoint Predictive