The traditional method of lead allocation can burden one agent while depriving another of sufficient opportunities. To address this issue, sales engagement platforms have streamlined and improved lead allocation using a rule-based system. An ML-based system utilizes both the leads' and the agents' data fields to calculate a 'conversion propensity score' that finds the optimal matches for conversion. Implementing ML-based lead allocation can be a game-changer for carriers looking to sharpen their competitive edge and take the lead.

Generating quality leads through the website, social media, events, and mailer campaigns are something insurers are doing aggressively to win customers and gain market share. Alright, you get those leads - what next? You allocate it to the next available agent, via the trusted round-robin or a random allocation method. At least this is what would happen ten years ago. At best, an agent in the vicinity would be given that specific lead to chase. End of story.  Actually, the story never takes off.

With such an approach,

• There is a burden of leads on one agent, while another agent has insufficient opportunities
• A lead’s need may not be addressed enough for them to convert into a customer
• Managers need to constantly monitor this method making it unscalable

With technologies like sales engagement platforms, lead allocation has become more streamlined and intelligent. The application uses a rule-based allocation system to help allocate leads better, quicker. How does this work?

The application typically provides users (agent managers) an intuitive interface to build their parameters. For example, 

1. They could allocate new leads based on the lead source; from social media, from the website, or from a call center etc. and route it to a certain agent
2. Or, they could assign leads based on product - if it is health insurance, it can go to Jack, life insurance can go to Andy and so on
3. Agent managers could also allot their leads based on geographical locations, if in-person interactions is a huge factor in the lead journey
4. Or understand if the lead prefers in-person conversations, or not, in which case the lead could go to an agent in a different location but tenured in a specific type of insurance selling.
5. Beyond this, agent managers could assign leads to the first person who responds to the lead notification, the agent with the highest conversion success, or simply based on agent availability. 

With a combination of these parameters, based on the insurer’s requirement, a successful rule-based lead allocation system can be implemented. This method of allocating leads has significantly boosted lead allocation practices helping insurance organizations gain more conversions, with faster movement through the lead journey.

Happily ever after? ChatGPT says no, there’s more! 😈

With the world looking at AI and Generative AI applications as the next frontier, cutting edge sales engagement platforms are leveraging ML-based allocation methods to improve things further! 

ML-based rules allocation can bring in a superlative improvement in lead allocation efficiencies.

How does this work?

Here the rule-based allocation engine works in tandem with the ML-based allocation algorithm. So not only does the system comprehend lead attributes, it also recognizes the actions performed by the lead over time. As a starting point the lead passes through the rules-based allocation system that has been customized based on the parameters defined by the carrier. After filters on source, location, product need and more, the results are fed to the ML-based allocation system. 

Here’s where the magic happens.

An ML-based system uses both the leads’ and the agents’ data fields to calculate a ‘conversion propensity score’ - what is the percentage of success if lead A is paired with agent X? 

The match with the highest score also has the highest chance of conversion.

If it sounds like ‘matches made in heaven’ - it actually is!

The ML model keeps learning from each record and adjusts the algorithm to find best possible matches for conversion. 

ML-based allocation of leads assure 80% + accuracy in mapping the right lead to the agent - this can prove to be a game-changer for carriers seeking to sharpen their competitive advantage and take a lead. 

As insurance sales leaders seek ways to optimize operational efficiency in the distribution chain, lead allocation is an important area with a large scope for improvement. It is time to measure the,  

1. Leads being generated versus allotted
2. Lead allocation efficacy
3. Tools that can help in improving the efficacy  with a focus on using technology like AI and ML to optimize this further. 

Wait, there is room for a sequel too!🥁

The inherent ability of AI is to learn and improvise. With time, the algorithms gather the data presented to them, combine it with experience and are able to allocate leads with even more accuracy!  Insurance leaders can leverage this for better product positioning and faster conversions. 

KEY TAKEAWAYS:

--Insurers can access new data streams to identify new risks, such as to city infrastructure, including smart grids, intelligent transportation systems and IoT-enabled devices.

--New, sustainable and efficient transportation systems, such as electric vehicles, bike-sharing programs and autonomous vehicles, create unique risks that require coverage.

--Smart cities face increased cyber risks, and insurers should collaborate broadly with officials on new legal and liability issues.

--Insurers can encourage eco-friendly initiatives by offering incentives.

--They can also leverage telematics data collected from connected vehicles to offer usage-based insurance policies.

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Smart cities and insurance are intertwined in various ways. As urban areas around the world continue to embrace smart city technologies to improve efficiency, sustainability and quality of life, insurance plays a crucial role in mitigating risks and supporting the development and sustainability of these cities.

Urban areas are growing at lightning speed. Worldwide, there are now 37 cities with populations of over 10 million, and, by 2050, around 68% of the global population will live in urban areas.

As more people move to cities, rapid technological advancements have given rise to innovative solutions that enhance the quality of life and streamline daily tasks in urban areas.

What Are Smart Cities?

Smart city initiatives focus on integrating technology and data-driven solutions to improve urban infrastructure, transportation, energy efficiency and public services. The implementation of these innovations extends beyond individual homes to encompass the entire urban landscape, affecting insurance in several ways.

Here are some key aspects where smart cities and insurance intersect:

1. Risk Management and Data Analytics: Smart cities use advanced data analytics and real-time monitoring to identify potential risks and vulnerabilities in the urban infrastructure. Insurance companies can collaborate with smart city authorities to access and analyze this data, enabling them to develop customized insurance solutions for specific risks faced by the city. For example, insurers can offer policies tailored to address issues such as infrastructure failures, cyber-attacks or natural disasters.

2. Infrastructure Coverage: Smart cities often involve extensive and complex infrastructure networks, including smart grids, intelligent transportation systems and IoT-enabled devices. These connected systems may be susceptible to various risks, such as cyber threats, equipment failures or physical damage. Insurance providers can offer coverage to protect against these risks and provide financial assistance in the event of disruptions or accidents.

See also: Smart Cities, Smart Choices for Insurers

3. Mobility and Transportation: Smart cities often promote sustainable and efficient transportation systems, such as electric vehicles, bike-sharing programs and autonomous vehicles. Insurance companies can adapt their policies to address the unique risks associated with these emerging technologies, such as accidents involving autonomous vehicles or specialized insurance products for shared mobility services.

4. Cybersecurity: With increased reliance on digital technologies and IoT devices, smart cities become potential targets for cyber-attacks. Insurance companies can offer cybersecurity insurance policies to protect against data breaches, ransomware attacks and other cyber incidents that could disrupt city operations and services.

5. Liability and Regulation: As smart city technologies evolve, new legal and liability issues may arise. Insurance providers can work with city planners and policymakers to understand these emerging risks and help establish appropriate insurance requirements and regulations. This collaboration ensures that both public and private stakeholders are adequately protected.

6. Environmental and Climate Risks: Smart cities often incorporate sustainability measures to address environmental challenges. Insurance companies can encourage eco-friendly initiatives by offering insurance incentives for environmentally responsible practices, such as energy-efficient buildings or green infrastructure.

7. Telematics and Usage-Based Insurance: In the context of smart transportation, insurers can leverage telematics data collected from connected vehicles to offer usage-based insurance policies. These policies adjust premiums based on actual driving behavior, promoting safer driving practices and potentially reducing the number of accidents.

New York City's Connected Cars Program is a smart city project that uses connected vehicle technology and IoT sensors (e.g., smart street lights and cameras) to gather real-time data about where drivers made sharp turns or braked abruptly because of traffic congestion and poor road conditions.

Leveraging data from connected devices, innovative data-collecting technologies and other smart city technologies can help officials and insurers across many lines of business better understand urban risks, allowing them to analyze trends and patterns effectively.

The McKinsey Global Institute found that smart technology can help improve quality-of-life indicators in cities by 10% to 30% -- numbers that translate into lives saved, reduced crime, shorter commutes, a lower health burden and carbon emissions averted.

More urban areas are adopting smart city approaches, many of which are aimed at reducing vehicle accidents, improving public health, enhancing the quality of life and predicting and preventing risks. Over 225 municipalities in Canada have expressed interest in exploring smart city benefits.

Overall, the integration of insurance solutions into smart cities is vital for creating a resilient and sustainable urban environment. Collaboration between insurance providers, city planners and technology experts can lead to innovative approaches that address the unique risks and challenges of the urban landscape in the digital age.

Insurance companies are grappling with a range of challenges that require them to adapt and innovate to stay competitive in the ever-evolving industry. From emerging technologies to changing consumer expectations, insurers are facing a complex landscape that demands their attention. In this article, we will explore the top 10 challenges insurance companies are currently encountering and discuss potential strategies to overcome them.

1. Embracing Digital Transformation

One of the primary challenges for insurance companies in 2023 is embracing digital transformation. The rapid advancement of technology has revolutionized the way businesses operate, and insurance is no exception. To stay relevant, insurers must adopt digital strategies that streamline processes, enhance customer experience and enable data-driven decision-making.

2. Cybersecurity Risks

The increased reliance on technology raises the risk of cyber threats. Insurance companies deal with vast amounts of sensitive customer data, making them attractive targets for hackers. Protecting this data from breaches and ensuring robust cybersecurity measures is crucial for maintaining customer trust and avoiding costly legal consequences.

3. Regulatory Compliance

Insurance is a heavily regulated industry, and compliance requirements continue to evolve. Keeping up with these regulations and ensuring adherence can be a daunting task for insurance companies. Failure to comply with regulatory standards can result in fines, reputational damage and loss of business. Companies need to invest in systems and processes that facilitate compliance and regularly update their practices to meet changing requirements.

4. Customer Expectations and Experience

In the digital age, customer expectations are constantly evolving. Insurance companies need to deliver seamless, personalized experiences across multiple touchpoints to meet these expectations. From intuitive online portals to efficient claims processes, insurers must prioritize customer-centricity to retain existing customers and attract new ones.

5. Insurtech Disruption

The rise of insurtech startups is disrupting the traditional insurance landscape. These technology-driven companies leverage innovative solutions such as artificial intelligence, machine learning and blockchain to provide enhanced insurance experiences. Established insurance companies must embrace collaboration and innovation to stay competitive in the face of insurtech disruption.

6. Data Management and Analytics

Insurance companies generate vast amounts of data from various sources. Effectively managing and analyzing this data is a critical challenge. By leveraging advanced analytics tools, insurers can gain valuable insights into customer behavior, identify emerging risks and optimize their underwriting and claims processes.

See also: Top 5 Challenges Facing Agents in 2023

7. Talent Acquisition and Retention

The insurance industry is experiencing a talent shortage, particularly in areas such as data analytics and digital marketing. To tackle this challenge, companies need to invest in attracting top talent and developing strategies to retain skilled employees. Offering competitive compensation packages, providing opportunities for professional growth and fostering a positive work culture can help insurance companies overcome the talent acquisition and retention hurdle.

8. Changing Risk Landscape

The risk landscape is constantly evolving, driven by factors such as climate change, geopolitical events and emerging technologies. Insurance companies must stay ahead of these risks to provide comprehensive coverage to their customers. This requires research, risk assessment and product innovation.

9. Legacy Systems and Processes

Many insurance companies still rely on legacy systems and outdated processes that hinder agility and efficiency. These systems are often complex, expensive to maintain and incompatible with modern technologies. Implementing modernized systems and streamlining processes can help insurers overcome operational bottlenecks and drive innovation.

10. Competition and Market Saturation

The insurance market is highly competitive, with numerous companies vying for market share. In such a crowded space, standing out and attracting customers can be challenging. Insurance companies need to differentiate themselves by offering unique value propositions, leveraging technology and providing exceptional customer service.

Conclusion

Embracing digital transformation, prioritizing cybersecurity, adapting to changing customer expectations and leveraging data analytics are just some of the key strategies that insurers must employ to thrive in this evolving landscape. By addressing these challenges head-on, insurance companies can position themselves as industry leaders and secure their long-term success.

KEY TAKEAWAY:

--The main culprits are fraud and lawsuits, rapid inflation for building costs and a surge in extreme weather.

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Across the country, homeowners are facing heavier financial burdens when it comes to securing the coverage they need to protect their property and belongings. Rising homeowners insurance rates are making budgets tighter for millions of American households. And in certain parts of the U.S., the situation is rapidly approaching crisis mode (if it’s not already there). 

In recent weeks, hundreds of thousands of policy holders in Florida and California were left scrambling to find new insurance coverage due to insurance providers pulling out of both states. Farmers Insurance is the most recent insurer to stop offering coverage in Florida, joining dozens of others. Meanwhile, State Farm decided to stop writing new homeowners policies in California — at least for the time being. Louisiana has lost at least 20 insurance companies in recent months due to either insolvency or withdrawal.

There are many factors contributing to the exodus of insurance companies. Here, we break down some of the top reasons consumers are facing rate increases and difficulty getting coverage.

Insurance fraud and lawsuits

One reason insurance rates are rising is the ever-increasing likeliihood of fraud. According to the Coalition Against Insurance Fraud, the insurance industry suffered more than $300 billion in losses as a result of fraud in 2022.

Insurance fraud in Florida, in particular, grew rampant at the hands of roof replacement scam artists over the past few years. In many cases, these roof replacement schemes escalated to lawsuits, which resulted in even higher losses. 

According to a study by the Insurance Information Institute (III), 79% of home insurance lawsuits in the U.S. originate in Florida, despite insurers in the state only receiving 9% of the country’s insurance claims. The combination, along with other factors, has pushed the insurance market in the Sunshine State to the verge of collapse. 

See also: Fundamental Shift in Life Insurance?

Inflation

Insurance providers have also felt the sting of inflation for materials and labor. According to a survey by the National Association of Home Builders, average construction costs for a typical single-family home in 2022 were around $153 per square foot, a surge from $114 in 2019.

Natural disasters

Of course, you can’t discuss the home insurance crisis without examining the impact of extreme weather and natural disasters. Wildfires, hurricanes, winter storms and other types of severe storms can devastate homeowners and result in enormous financial losses for insurance companies.

According to the National Centers for Environmental Information, the U.S. has experienced 90 billion-dollar, weather-related disasters between 2018 and 2022 — an average of 18 per year. By comparison, in the 2010s, there were an average of 13 events per year, and just seven per year from 2000 to 2009. 

The bottom line

No single event is sending the home insurance industry into crisis. Instead, a combination of factors are putting homeowners insurance providers under extreme financial pressure, especially in certain parts of the country. And as insurers become insolvent or opt to pull out of certain states, it’s ultimately the homeowners in those regions who pay the price.

The insurance industry has historically been highly data-led. As computing capability has expanded, the ability of data science to turn traditional insurance problems from descriptive, backward-looking views to highly accurate, predictive insights has advanced.

Today, insurers continue to gain deeper insights captured more quickly than previously possible. For example, there has been fast-growing interest in using machine learning to improve claims operations via informed call routing decisions, or the ability to spot emerging problems early on and trigger the engagement of human intervention for remediation. 

In the turbulent markets that the U.K. personal lines industry currently faces, data science can, when combined with experienced decision makers, deliver a compelling advantage to ride the "perfect storm" more effectively. 

Yet, although insurers are increasingly using data to generate value, firms have so far done this with varying degrees of success. At the executive and senior leadership level, there is concern that significant investment in data science teams -- and the technology infrastructure required to deploy these methods -- are not delivering the practical, pragmatic business change or value they would like or expect.

The grace and favor once afforded to executives around data science as an “R&D” activity has passed, and the expectation of clear value from the investment is now being demanded. Close observation of the market has revealed five of the biggest drivers of underperforming data science teams:

1, Trading off accuracy and value creation

Insurers face potentially conflicting challenges between how data scientists have been trained to work and the actual needs of the business. Where model accuracy and predictiveness might be the ultimate focus for data scientists, many insurance leaders are keen to see swift and actionable insights that can result in material change and measurable value. They are also -- within limits -- more than prepared to compromise on predictiveness. 

The trade-off between model predictiveness and value continues to be a well-socialized issue. How leadership balances both requirements is not an easy problem to solve, and the time required to allow this challenge to find its natural equilibrium is not always palatable – or indeed practical or desirable.  

2. A lack of technical challenge

This is a situation that occurs with leadership who have not used advanced analytics techniques in their earlier careers -- for example, those who may have cut their teeth on GLMs and do not understand these new methods as deeply. Therefore, their ability to challenge model performance or outputs effectively is reduced. This can manifest as an inability to identify and therefore steer the team away from pitfalls and, as a result, the data science function failing to deliver sufficient commercial value. It can also present as a reluctance or slowness to apply these methods, due to fear or lack of understanding, that may affect future commercial prospects. 

See also: Healthcare Data: The Art and the Science

3. Naivete

There is a certain level of naivete in the approaches taken by data science teams, which stems from a lack of understanding of the very specific, niche problems faced by insurers. Model instability, for example, is where data science techniques are able to create an inherent variability (more so than with historical methods), which when deployed in an insurance context can lead to unintended and detrimental outcomes. What date scientists choose to model is sometimes misguided, so it is imperative that insurance specialists and data scientists work together, sharing goals to achieve the best outcomes for their business.

4. Managing massive model real estate

For organizations that have great data, the opportunity to model is enticing, and with well-built models the value is unquestionable. However, models need maintenance and attention as neglect risks leading to poor insight and decision making. So, with a large model real estate, it is easy for skilled pricing resource to spend a disproportionate amount of time on being glorified handle turners, rather than spending the time in generating material insights from models to create genuine business change and value. 

5. Insufficient governance and control

Data science teams can lose sight of appropriate governance. It is critical to bring together data scientists and subject matter experts to design systems that offer greater visibility of what models are doing, with more transparent governance that is sufficiently understood by the wider business and external stakeholders. The excuse of data science methods being opaque and uninterpretable is no longer an option, with the best having good control over the impact of their models. 

The U.K. insurance market is seeing an explosion in the use of data science, with both winners and losers. Bad data science is often clever people doing clever things with data, but they all too often fail to filter through the organization to drive real change and generate no commercial value. This results in poor return on investment, but more importantly a weight around the ankles of data science teams that results in reduced productivity and attrition.

Insurers that are pulling ahead of the pack are the ones thinking about how they can create the structure and culture to empower data science teams to deliver value. They also have a strategy and clear vision around team structure, what to model, deployment and maintenance, as well as having the technical expertise to ensure the implementation is robust and real business value is unlocked from data science, targeted at solving meaningful problems. Those who are successful in navigating these challenges are seeing significant tangible returns.

Way back in 2014, Wired magazine co-founder Kevin Kelly wrote, "The business plans of the next 10,000 startups are easy to forecast: Take X and add AI." Boy, was he right.

That prediction was far bolder than it looks in retrospect. For the preceding nearly 60 years, an AI revolution had been much promised but was always just over the horizon. Even proponents acknowledged that there was "an AI winter." 

But Kelly saw a convergence of new forms of computing power, plus big data and better algorithms, and declared the winter over.

And here we are: A form of AI, best-known through its incarnation in ChatGPT, has captured the world's imagination, and not only every startup but just about every established company is figuring out how to fit generative AI into its business plans. 

But if there's one thing I've learned over my many years of following technology -- beyond that Kevin Kelly is a smart fellow -- it's that caution is in order whenever a new technology is supposed to take the world by storm. Events rarely play out as expected, and mistakes get made in the rush for the gold.

So, I thought I'd share thoughts based on an insightful column I recently read on the 10 biggest mistakes companies make when trying to implement AI. The column doesn't focus on ChatGPT and its rivals, which I know is the topic du jour, but the broad lessons could save a lot of us a bunch of time, effort and money.

The column, by Bernard Marr, which I recommend reading in its entirety, lists these 10 as the biggest stumbles with AI that he's seen in his extensive experience:

• Lack of clear objectives
• Failure to adopt a change management strategy
• Overestimating AI capabilities
• Not testing and validating AI systems
• Ignoring ethics and privacy concerns
• Inadequate talent acquisition and development
• Neglecting data strategy
• Inadequate budget and resource allocation
• Treating AI as a one-time project
• Not considering scalability

I'd highlight these four: 1) lack of clear objectives; 2) failure to adopt a change management strategy; 3) overestimating AI capabilities; and 4) treating AI as a one-time project. 

Lack of clear objectives

From what I've observed, the biggest issue is that every company -- certainly, every public company -- is being peppered with questions about what its AI strategy is. Not having an AI plan would be like not having a website in 2000 during the first internet boom or not having an app in the 2010s, after Apple made smartphones ubiquitous. So, every company has some sort of AI strategy -- at least, a major AI project. 

But AI is often a technology in search of a problem, and that rarely works, no matter what technology is involved. Companies need to start, as usual, by defining a business problem to be solved. Then, if appropriate, AI can be applied. Just deciding to sprinkle some AI on a business unit or process rarely accomplishes anything, and can be distracting.

For me, two of Marr's other "top 10 problems" -- lack of data strategy and not considering scalability -- fit under this umbrella. A clear AI plan for, say, auto insurance claims needs to start by looking at how AI can streamline the process. But the plan also needs to envision from the get-go how the data gathered fits into the overall corporate data strategy -- such as by being fed into the underwriting process or, perhaps, being shared with car makers so they can improve safety or lower repair costs. In addition, the AI plan needs to map out how the initial work can be scaled. Otherwise, the AI work is more show than substance.

Failure to adopt a change management strategy

Everybody likes change -- except for the change part. And AI, done right, produces major changes in how people work. So, any AI strategy of any scope needs to prepare for the retraining that will have to be done and for resistance to appear. That means those driving the change need to communicate, communicate and communicate, then communicate some more. 

Executives will also need to model the new behavior. Don't expect others to use ChatGPT, for instance, if you don't.

I remember when IBM was selling enough email software in the early 1990s that the CEO decreed that paper memos were out and emails were in. The idea made a lot of sense. In Silicon Valley, the approach is known as eating your own dog food. You get a sense of what your customers are experiencing. But IBM executives -- who mostly didn't know how to type -- had their secretaries type memos as usual, then simply put them in email form. Subordinates weren't fooled, and the mandated move to email fizzled.  

Overestimating AI's capabilities

How easy is it to fall victim to this problem? So easy that even Kevin Kelly got caught, to an extent, in that brilliant article from 2014. He opened the piece caught up in the glow that AI achieved when IBM's Watson beat Ken Jennings at Jeopardy! in 2011 and reported at face value IBM's plans to "send Watson to medical school." But Watson, in its initial incarnation, turned out to be a one-trick pony. It was great at the sort of natural language processing that a contestant needs to do to decipher the clues on Jeopardy! but never came close to deciphering medicine. Kelly also predicted that Google would become so good at AI that, "by 2024, Google's main product will not be search but AI."

AI can be marvelous stuff, but it's really just smart computing. Yes, it can beat Jennings at Jeopardy!, overcome Garry Kasparov at chess and perform all sorts of other marvels in structured environments. But it isn't a better soccer coach than I am -- and I don't even coach soccer. 

It's crucial to focus not just on what AI can do but on what it can't. AI isn't magic.

Treating AI as a one-time project

AI is a funny beast. It isn't really a technology, at least not in the sense that, say, telematics or blockchain is. Historically, AI has always been whatever you could imagine as possible but couldn't quite do yet. When computer scientists conquered whatever the problem was, their work became plain, old computing, and AI was defined as some new aspiration. 

When I first had people start bragging to me about the potential of AI, some 35 years ago, the sorts of things we take for granted weren't even in the realm of possibility. Siri? Are you kidding me? Google Translate? Yeah, right. 

Now, while there's plenty of work being done to keep improving Siri, Google Translate and other such tools, AI has moved on to figuring out how to estimate car damage from photos a driver sends, how to price risk for a life insurance policy without requiring a doctor's appointment and the taking of fluids, etc.

Basically, AI is a treadmill. Once you get on -- as everyone should -- you can't get off. It never stops moving.

Marr's other four points are certainly important -- not testing and validating AI systems; ignoring ethics and privacy concerns; inadequate talent acquisition and development; and inadequate budget and resource allocation -- but I think of those as downstream issues that can be addressed if the strategic umbrella is right.

I came across a great quote the other day in a book about how much Abraham Lincoln did as president to lay the foundation in the U.S. for the development of science. Lincoln wrote: "We always hear of the successes of life & experiment, but scarcely ever of the failures. Were the failures published to the world as well as the successes much brain work & pain work--as well as money & time would be saved."

As usual, I'm with Honest Abe. I recommend we learn as much as we can from the failures to date on AI projects, to clear the way for the many successes that are possible.

Cheers,

Paul

KEY TAKEAWAYS:

--Client organizations must implement regular security assessments, vulnerability management and continuous monitoring.

--They must set up controls, such as multi-factor authentication, to make it harder for criminals to compromise privileged identities in corporate networks.

--Clients need to prepare well-defined plans to respond to any cyber incident.

--And they must build strong relationships with insurers and regularly discuss industry trends.

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In the face of continuously evolving and increasingly prevalent cyber threats, organizations have recognized the importance of cyber insurance as a crucial risk management tool. However, a recent survey conducted by Delinea shed light on one prominent challenge organizations encounter when seeking cyber insurance coverage – fluctuating costs.

The survey revealed that 75% of respondents said cyber insurance premiums were increased with their last renewal. U.S. cyber insurance premiums reportedly surged 50% in 2022.

The jump is primarily driven by the rising demand for coverage in light of frequent and costly cybercrime incidents. In 2022, the FBI reported that businesses had lost over $43 billion through business email compromise attacks since 2016.

With the frequency and sophistication of cyberattacks on the rise, insurance providers have been compelled to raise premiums and impose stricter requirements to maintain their economic viability. Some companies have reduced coverage caps or limited the number of policies they offer. Consequently, client organizations face greater challenges when attempting to secure the necessary coverage.

But cyber insurers have evolved and learned from past cyber incidents, which means policies are improving and risks are better understood.

To do their part, client organizations must understand that cyber insurance is a financial safety net and not security itself. Organizations need to adopt an aggressive and comprehensive approach to cybersecurity. Cyber insurance does not make your cybersecurity better, but it may force you to reduce your risks to meet the insurance requirements.  

See also: Cyber Insurance Market Hardens

Combat Rising Cyber Insurance Premiums

Here are a few strategies organizations can implement to combat rising cyber insurance premiums:

Proactive Cybersecurity Measures: These include regular security assessments, vulnerability management and continuous monitoring. 

Privileged Access Management (PAM): Insurers are increasingly emphasizing the importance of PAM in cyber insurance evaluations. Compromised privileged identities are the most common cause of data breaches, making securing privileged access critical to reducing risk. Implementing PAM controls, such as multi-factor authentication, password management, access control and least privilege, helps organizations secure privileged access and reduce the risk of data breaches. 

Incident Response Planning: Having a well-defined incident response plan is crucial for organizations to minimize the impact of cyber incidents. Insurers may consider the effectiveness of an organization's incident response capabilities when determining premiums. 

Engagement With Cyber Insurance Providers: Building strong relationships with insurers and regularly discussing industry trends and risk mitigation strategies can help organizations gain insights and negotiate more favorable terms. 

As cyber threats evolve and organizations increasingly rely on cyber insurance for financial risk management, the rising costs of cyberattacks pose challenges for the insurance industry and organizations alike. By demonstrating a commitment to risk reduction and implementing comprehensive cybersecurity strategies, organizations can manage financial risks associated with cyber incidents. Together, organizations and insurers can combat the escalating costs of cyberattacks and ensure the availability of comprehensive cyber insurance coverage now and in the future.

I've spent most of my career helping organizations build comparative advantage at the intersection of strategy, technology and innovation. I now believe both individuals and organizations should aim even higher.

We have the good fortune and awesome responsibility of sitting at the inflection point of the Fourth Industrial Revolution. For better or worse, ever better and cheaper technological building blocks, including pervasive connectivity and computing, AI, robotics and genomics, are blurring the lines of the physical, digital and biological worlds. They are already reshaping industries and societal patterns, and the transformation is accelerating.

Our individual and organizational opportunity is to guide our little slices of the universe toward the better, and away from the worse, potential outcomes. In doing so, we can help build a collective future with greater health, sustainability and prosperity. We can build a world we can proudly leave to our children and their children.

In this post, I am sharing the video and slides from a recent webinar where I explored this theme and offered three lessons drawn from my recent book, "A Brief History of a Perfect Future," written with Paul Carroll and Tim Andrews. (Thanks to Zoom for hosting and sponsoring the webinar as part of its monthly "Work Happy" series.)

Here are the three lessons, in brief:

1. Make a Third List. 

In addition to the daily and weekly to-do lists that many keep, develop a "third list" of your biggest, most ambitious goals. These are the audacious goals you and your colleagues want to accomplish in the next five, 10 or even 20 years. They might even be goals that can't be realized during your tenure. But they should be goals you are always on the lookout to materially advance in your time, whenever possible.

In my presentation, I talked about how Rahm Emanuel and his predecessors as mayors of Chicago had the restoration of the Chicago River on their third list. Through a combination of long-term master planning, patient zoning, opportunistic development and political savviness, they shepherded a decades-long transformation of Chicago's slimy, concrete-entombed downtown riverfront into the magnificent Chicago Riverwalk.

See also: 6 Words to Focus Your AI Innovation Strategy

2. Embrace the Laws of Zero. 

Seven technological building blocks—computing, communications, information, genomics, energy, water and transportation—are advancing exponentially in capability and, on a relative basis, headed toward zero cost. That means we can plan on being able to throw as much of these resources as we need to at any problem to address it intelligently. Success in doing so would bring us closer to what my coauthors and I call the Future Perfect.

But the building blocks are not the buildings. It is easy to imagine these capabilities being used to exacerbate societal problems in areas such as health, equity, civility, privacy and human rights.

3. Write Your 'Future History.' 

As the saying goes, "If you don't know where you're going, you might not get there." "Future histories" are narratives that help illustrate and crystallize a desired future scenario. Rather than predicting some abstract or fantastical future, they aim to describe an ambitious yet attainable scenario by a specific date. The target date should be far enough out so you don't worry about short-term noise, constraints and implementation details (yet) but near enough to allow realistic estimates of what is technologically possible. Working backward to today, you can chart the possible paths to that future.

For example, when President John F. Kennedy declared in 1961 that the U.S. would put a man on the moon by the end of that decade, he rallied the nation to achieve a complex challenge that might otherwise have taken many decades. Kennedy’s narrative was a magnificent example of a “future history.” With vivid strokes, JFK described an ambitious yet attainable goal by a specific date. His narrative captured public imagination and support and, as he said, “served to organize and measure the best of our energies and skills.” Working backward from Kennedy’s future history, an extensive public/private partnership laid out the path to invent the future Kennedy envisioned. This included, in no small part, developing and integrating a host of new technologies and capabilities, such as in materials, propulsion, guidance, control, communications and safety.

Here's a video that further explores future histories.

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Today, the world finds itself facing challenges much more daunting than going to the moon, such as in climate change, war, health, equity and poverty. But we also have near magical building blocks and tools to augment our human ingenuity. It is the opportunity of a lifetime.

ITL: 

A couple of years ago, there was loads of enthusiasm about embedded insurance, but that seems to have lessened, at least in some quarters. To start us off, could you please tell us where you think we stand at the moment? 

Chris Bassett: 

Embedded insurance isn’t particularly novel. There have long been forms of insurance that are made available at the point of sale. There’s bancassurance, for instance. You buy coverage when you’re hopping on a plane. There’s the whole warranty model.  

You're capitalizing on the endorphins associated with making a purchase and the perception of the risk of losing that asset. What’s interesting is that a study found that consumers perceived the risk of loss of a particular item was around 7% while the actuarially calculated risk was 3% or 4%, so consumers may well be overestimating the potential for loss, which leads them to consider purchasing a warranty. 

The challenge is: How can we make insurance a natural part of an overall transaction? We shouldn’t just say there’s a pull at the point of sale that we can capitalize on. Embedded insurance shouldn’t just be a bolt-on. The idea behind the “point of design” approach is to find a way to weave the insurance into a purchase and make a meaningful connection. 

ITL: 

Where do you see opportunities to do that? 

Bassett: 

The first thing is to think about the design of the insurance product. Then we should also think about the long-term experience for the buyer. 

We were looking at this with a large jewelry firm that sells through boutiques. They sell very, very high-end watches and jewelry. We explored completely redesigning the purchasing experience, including the idea that an insurance component was bundled in as part of the warranty and would tap into specific emotional triggers associated with the purchase.  

You could actually take a step back and say, What if we redesign the entire product with insurance and potentially other financial services in mind? You want there to be such an obvious fit that it wouldn’t make sense to purchase the product and the insurance separately.  

An example of this might be, say, Nike shoes that have health and wellbeing coverage built in. The design questions that you have around what makes a really appealing sort of sportswear are similar to the sorts of questions that underwriters can draw from in thinking about what this particular risk profile looks like. And there would be a natural affinity among people who bought the shoes, so you could build a community aspect around them. 

You could also go beyond the point of sale. Let’s say you buy a car, and insurance is bundled in at the point of sale. There could also be an on-demand component. Maybe you later see the value of adding coverage, perhaps for long-term disability, and then take advantage of additional safety features in the vehicle. Maybe you tie home insurance together with risk prevention services and let people turn their coverage on or off, depending on whether they’re there.  

So, from a “point of design” perspective, you can design a new sort of insurance product, you can weave an asset or service together with an insurance product in a way that aligns their value propositions or you can do a combination of the two and possibly include an on-demand component.  

ITL: 

Let me ask about some of the objections I’ve seen raised about embedded insurance. I’ll start with agents and brokers. Don’t they get cut out? Won’t they block the trend or at least slow it greatly? 

Bassett: 

Yeah, absolutely, that’s an issue. But there is the potential to create enormous value for brokers and agents. We've looked at this in terms of vehicle telematics, more sophisticated home-related sensors and so on. The amount of information that agents and brokers now have access to about policyholders allows for a very different relationship. 

To give you an example, we looked at small commercial truck fleets and saw that, beyond just helping policyholders improve their driving, brokers and agents could work with them to improve their general risk profile. Agents and brokers can become kind of a risk management coach and help clients reduce premiums. There's also the potential to gather a lot more personality-based information, about how people are living, how they’re behaving and so on. That provides an opportunity to look at different products that might interest customers. 

Embedded products can create a continuing relationship that allows for data collection and engagement. That creates long-term opportunities even if there’s a short-term cost. 

ITL: 

That’s an interesting way to look at the issue. What about the complexity? What happens when I go to a jeweler who wants to sell me insurance, but I already have a homeowner’s policy that covers my belongings? 

Bassett: 

You’d look to design complementary coverage. With the jeweler, for instance, we were dealing with rings that cost hundreds of thousands of dollars and that weren’t covered under homeowner’s policies, even though many buyers thought they were. 

ITL: 

How about an objection that somebody raised in an article published with us recently? He wrote that, if the insurance becomes simple enough that I can just purchase it as I buy the ring or the plane ticket, then it becomes a commodity, and the airline or jewelry chain could easily swap out my insurance and swap you in. That would mean all the leverage in the relationship would go to the retail partner and make for a bad deal for the insurer. 

Bassett: 

We spent a lot of time looking at travel insurance, and, yes, there’s an enormous amount of control in the hands of the cruise lines and airlines as distributors. The key for insurers is to capture more information around clients’ personalities, around their risk preferences, and then reverse engineer to look at their value systems and build risk profiles.  

You say, Okay, you bought travel insurance with us, and we know from the cruise line which events you chose to go on and sort of the nature of your movements on the ship, so that leads us to believe that you might be interested in these types of coverages or these types of services that we can offer.  

This comes back to the difference between thinking about the point of sale and thinking about the point of design. Embedded insurance has got to be more than just the transaction. 

ITL: 

Any final words? 

Bassett: 

Outside of automotive, where some initial partnerships and exploration are happening, I haven’t seen a lot of activity. But I think there's a compelling opportunity for insurers to start working with technology firms to look at emerging technologies and see how they might enable embedded insurance offerings.  

I also think all organizations, including insurers, need to start building partnership muscles. The ability to identify the right kind of partners to work with and to build strong connections with them tends to be fairly weak. Companies need to work at better identifying partners and at codeveloping solutions.  

ITL: 

People don't think of developing partnerships as a skill that has to be developed. But it is, and building strength takes time. I think your description of the need for partnership muscles is spot-on. 

Thanks, Chris.