KEY TAKEAWAYS:

--The unprecedented heating of the Atlantic Ocean is leading to storms intensifying more quickly, lasting longer and bringing more water. Nowhere in Florida can now be considered immune from the impact of hurricanes. States farther north are more in danger of both wind damage and flooding than previously. And more people keep moving into the danger zone.

--An expanding number of tools are available to set premiums at a far more granular level, exploiting both macro weather data and sources directly from a property, such as aerial photography, that can indicate, for example, whether a roof is vulnerable to storm winds. 

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In late August, Hurricane Idalia made landfall in northern Florida, wreaking havoc in counties throughout the “Big Bend” region before moving on to southeast Georgia and the Carolinas as a tropical storm.

Idalia’s path and trail of destruction provided further evidence to back the findings of the LexisNexis U.S. Hurricane Season Trends Report, which shows that major storms are moving northward, maintaining their strength for longer and expanding their impact into areas previously considered less at risk, including non-coastal states. 

The cause is clear. The unprecedented heating of the Atlantic Ocean caused by climate change is leading to storms intensifying more quickly, lasting longer and bringing more water. And the consequences are far-reaching. Nowhere in Florida can now be considered immune from the impact of hurricanes, with coastal areas likely to see greater damage and inland regions more vulnerable than ever before. As Idalia showed, states farther north are more in danger of both wind damage and flooding than previously. 

As more and more homes across the southeastern U.S. are exposed to hurricane damage, insurers already tormented by storm-related issues must now recognize and respond to the threat that is steadily advancing into new regions. 

See also: Property Underwriting for Extreme Weather

2022 season shows northward shift 

The research, published by LexisNexis in September and focusing primarily on wind losses, underlines what insurers have experienced in the field. 2019 was the only year since 2017 in which total single-home wind losses have not exceeded $3 billion; the number of named storms has also risen, and their impact has intensified. 

Analysis of the 2022 season by LexisNexis, in which four named storms hit the southeastern U.S., has illustrated the growing issues for homeowners and insurance carriers. Three storms – Alex, Colin and Nicole – resulted in relatively little damage, but two of those highlighted the growing range of storms. Colin disrupted Fourth of July celebrations when it hit North Carolina, while Nicole brought heavy rainfall and damaging winds to Georgia and South Carolina.     

The other of the four storms, Ian, made landfall as a Category 4 Hurricane north of Fort Myers and traveled in a northeasterly direction across largely empty areas of central Florida before returning to the Atlantic Ocean. Once there, it temporarily weakened before turning northward and making landfall a second time, at hurricane strength once more, in northern Georgia. It then passed into North Carolina.

The National Oceanic and Atmospheric Administration (NOAA) estimated Ian’s total damage at $112 billion, making it the costliest in Florida’s history. Single-home wind losses are estimated at $6 billion.

It could have been much worse. After leaving the populated Gulf Coast area, Ian’s path took it across an area of low population density, with an estimated 600,000 people within 10 miles of its epicenter. In the two days prior to landfall, the NOAA plotted projected routes for the storm that would have taken it farther north, placing the heavily populated metropolitan areas of Tampa and Orlando, and an estimated 2.5 million people, in its path. Damages could have been four times as high had Ian followed one of these trajectories.

Growing population in the storm path

In addition to stronger and longer storms, there is another key factor driving up the costs of named storms: population growth. Despite the increasing dangers of an intensifying storm season, and the subsequent spikes in policy costs, more and more people are moving into the danger zone. 

Florida experienced a surge in population growth between 2010 and 2021. Much of coastal Florida saw increases in excess of 10%, with some parts at double that rate. Areas east of Tampa and around Orlando grew even faster. Some of that metropolitan-area growth has dropped since 2021, as COVID considerations took hold, but coastal growth has continued. 

The northward shift of storm season, and its intensification, has been relatively quick and largely unexpected. Millions more Americans now face risks to their lives and homes that they may not have anticipated, as well as soaring homeowners insurance premiums. 

See also: Glimmers of Good News on Climate (Finally)

Data is key to meeting the challenge

As a result, carriers have more households threatened by a growing number of increasingly severe storms, all the way from the Florida Keys to North Carolina and possibly beyond. 

With the risk spreading and the safe pool of properties within which to share it shrinking, carriers face a daunting challenge; even states that may previously have been prepared for hurricanes on the coast now face threats farther inland. Some insurers have chosen to abandon Florida, others have been forced out of business, while many will be looking nervously at storm impacts farther north.

Case in point: In late September, a well-known insurer dropped more than 10,000 homeowners policies in North Carolina. Major carriers have also been taking other approaches, notifying regulators that, in some cases, they will no longer insure properties against hurricane damage (and other natural disaster risks) in certain areas, such as along coastlines.

To survive and thrive in these circumstances, carriers need to adjust their business to better understand and mitigate their exposure. This not only applies to states facing hurricane risk, but to others where fire and hail are increasing perils.

An expanding number of tools are available to set premiums at a far more granular level, exploiting both macro weather data and sources directly from a property, such as aerial photography, that can indicate, for example, whether a roof is vulnerable to storm winds. 

Such services can be deployed using automated processes backed by artificial intelligence (AI) tools that needn’t add to the administrative burden and can allow carriers to understand the real level of risk they face.

The time to act is now. The data from 2022 and weather events in 2023 suggest the climate crisis is only growing and carriers need to use every tool available to counter the threat.

Artificial intelligence (AI) isn’t new, but what it enables us to do is evolving rapidly. In the insurance industry, we’ve already seen AI transform risk assessment, data analytics and time-consuming administrative tasks. And it’s safe to assume that tomorrow’s technological advancements will have even more dramatic impacts on the way we do business. However, there is one thing we must keep in mind if we are to be successful in our use of AI: A machine cannot replace the need for human touch in our industry.

In a world where consumer behaviors and preferences are ever-changing; the human touch can be critical for successful transactions. The needs of our customers are dynamic, and a one-size-fits-all approach does not account for the complexities and constant shifts happening in their lives. We need people to serve as quality control for AI, to provide the underlying training it requires and to develop and deploy it in ways that create value for society while safeguarding against potential harm. While AI will enhance our ability to do our jobs, striking a balance between technology and a human touch will be key to ensuring it will improve the customer experience, as well.

See also: The Rise of AI: a Double-Edged Sword

Already, AI has brought some key advantages to the table for both business operations and the customer experience:

• Enhanced predictive capabilities and real-time risk monitoring – The speed and accuracy of AI-enabled data analysis has allowed insurers to obtain significantly more quantified insights into various risk factors and consumer behavior. The integration and synthesis of large datasets from multiple sources helps managers obtain real-time data for better risk portfolios. 
• Improved underwriting accuracy – Access to more data via telematics, remote sensors, satellite images and digital records helps identify less overt patterns and risks that may be overlooked by humans. The automated evaluation of risk factors also creates a more consistent, streamlined process that reduces human bias and error. 
• Personalization – Better customer segmentation helps insurers have a better understanding of their desired markets. Plus, by leveraging the digitalization of existing customer touchpoints as well as access to new data sets from digital partners, AI helps insurers provide more tailored coverages and pricing. 
• Capacity management and cost savings – AI frees up insurers' time by reducing the amount of manual and non-value-add tasks such as data entry, document processing and simple claims handling. 24/7 chatbot support for simple questions and routine items also enables customer representatives to spend more time on advice and retention. 

However, here’s where the balance between AI and human touch gets tricky. Fail to adopt new technological capabilities, and our ability to serve our customers declines. But tread too far down the road of automation, and we lose the trust generated by the expertise, institutional knowledge and empathy our insurance agencies have been building for decades.

For example, the current generation of brokers and agents have the background and expertise that allows them to solve complex and nuanced problems, improvising when necessary to address unique needs. Rely too heavily on AI, and not only does the customer miss out on a personalized experience but that level of guidance slowly erodes over time as new generations of insurance professionals miss out on opportunities to build their skills in providing advice and counsel.

Additionally, while some AI systems may be able to analyze data to provide a customer with the optimal coverage at the best price, they cannot empathize with the customer or provide reassurance in the way that a human can. For instance, AI can offer up a policy from Carrier A, but let’s say the customer would like an alternative option as her brother had a bad experience with that carrier. AI may be able to provide other options but cannot impart the reassurance around why Carrier B is also a great option, like a human can. 

See also: AI and the Future of Independent Agents

As both the insurance industry and AI technology continue to evolve, AI will not have the same level of impact across all functions. In claims management, AI will likely continue to have a significant impact on the validation, assessment and adjudication of claims but a low impact on claims litigation and claims financials. The need for experienced insurance professionals will remain. 

Some customers will adopt new technologies faster than others. Meeting customers where they are, and offering options for how to interact, including both AI-enabled and human options, is an optimal solution for navigating this new age of insurance while still reaching a broad audience of customers. 

The bottom line: As AI continues to permeate the industry and enhance our ability to serve customers, it will only be optimized through integration with the human touch.

Customers prefer simplicity speed and transparency, from quote to claim, and the adoption of multi-cloud as a strategic initiative fosters growth and innovation. It encourages collaboration among employees and improves accessibility to customer data, leading to the creation of a more holistic, personalized and relationship-building approach to customer interactions. 

Rise of Hyper-Personalized Cloud Solutions

Cloud vendors are leveraging the concept of verticalization, which incorporates offering hyper-personalized cloud solutions to customers. The cloud vendors offer customized solutions with data models, core systems and horizontal applications tailored for insurtech businesses, bringing more flexibility into their cloud environment. 

See also: Benefits of Deploying a Hybrid Cloud

Multi-cloud Chaos: A Reality

However, insurtechs can wind up with a complex, multi-cloud architecture using different proprietary interfaces, platforms and services. Many insurtechs are not able to fully leverage the benefits of their exhaustive cloud investments, and expenses can soar.

There needs to be a strategy to tame the chaos. Some insurtechs are showing deep interest in leveraging the concept of sky computing, also known as supercloud or meta cloud. 

How Meta Cloud Can Help

The super or meta cloud provides a single pane of glass for managing and provisioning resources catering to multi-cloud environments under a layer of abstraction. It provides a centralized manner to manage security compliances, costs,and data security with precision.

Let’s take a rundown on optimizing meta cloud for taming a diverse cloud architecture for insurtech businesses.

1. Bringing More Operational Visibility Into the Processes: Meta cloud brings a cohesive view of all the cloud resources used by insurtechs, the process and current activities involving end-to-end operations. A centralized, call-level interface popularly termed CLI can be leveraged to optimize the process of sharing user access to shared resources, and all this can be done simply from the compatibility layer. This interface operates as a centralized command center that offers complete visibility and control to developers over the cloud service providers’ different platforms and services using standardized application programming interfaces (APIs).

This approach has numerous benefits for effective management and maintenance of all the cloud resources. By using the benefits of automated processes for provisioning, cost optimization and scaling processes, time savings can be obtained, particularly on the tasks that are critical for streamlined operations. Therefore, cloud service automation helps to enhance flexibility and improve the capability to onboard partners smoothly. With minimized maintenance costs, it becomes easy to integrate new solutions into existing cloud architectures without making expansive modifications, leading to a simpler approach to enhancing capabilities. Additionally, using artificial intelligence and automation tools helps in a seamless knowledge-sharing process among public clouds, which facilitates making processes faster and smoother with streamlined responses.

2. Enhanced Cloud Security Accompanied by Minimized Threat Surfaces: Meta-cloud helps in taming the complications of multi-cloud architecture with centralized cloud security provisions, and accompanied by operational visibility, it empowers insurtechs to reduce threat surfaces to a considerable extent. By leveraging shared meta-resources, businesses can overcome the inherent challenges that are usually associated with multi-cloud strategies, which are often fragmented. The super cloud mitigates the complexities with the provision of standardized security parameters that are configurable for each cloud provider, leading to a more streamlined and controlled approach to handling complexities.

The key benefit of adopting the meta cloud is to avoid the siloed approach to managing stringent security parameters for each cloud service provider. It is achieved by optimizing the CLI’s standardized security parameters that are streamlined for optimal safety. Moreover, meta cloud provides the staging zone to check the security measures ahead of deployment to specific providers, which includes solutions and applications for FinOps and DevSecOps. There is strict scrutiny over vulnerability measure processes, and meta cloud incorporates the potentials for BCB, DR, failback and failover scenarios. It helps to identify and reduce unwanted services, significantly mitigating potential risks and threat surfaces and enhancing a high level of data security in the public cloud.

3. Optimizing the Available IT Resources: Amid the current shortage of IT talent, insurtechs must make the most of their in-house IT team. This can be done by empowering the cloud team to make the most with fewer resources, and meta cloud empowers insurtechs to optimize their resources with the minimal number of specialists that are required to monitor specific platforms. With automation, all processes are streamlined, and IT resources are given ample time to focus on strategic innovations by contributing to highly valuable tasks.

With the adoption of standardized meta-skills, the training time of IT staff is significantly reduced as the general skills help accelerate the learning cycles with strategic implementation of these skills throughout the cloud department. Moreover, the entire process of communication and command is thoroughly streamlined, which speeds tasks and mitigates the risks of miscommunication among the cloud teams, leading to enhanced productivity and efficiency in all processes.

See also: The Cloud: Connecting the Insurance Ecosystem

Common Challenges With Multi-Cloud Adoption and How to Manage Them Smartly

There are some challenging factors associated with the meta cloud, like enhanced complexity, a lack of support from cloud service providers factoring in commoditization and development challenges. Businesses need to consider these factors before making a strategic move toward the adoption of a layer of automation and abstraction over a multi-cloud architecture.

InsurTechs need to consider:

1. Misalignment in CLI Configuration: There can be misalignment in the CLI configuration with APIs, leading to more complexity and a rise in technical debt, slow processes and blind spots. The way to avoid this scenario is to take precautions in creating the perfect alignment with CLI’s configurations with APIs so that IT teams don’t have any challenges in smoothly navigating the compatibility layer for faster processes and streamlined operations.

2. Inadequate Vendor Support: There can be a lack of vendor support owing to commoditization, which takes away the competitive advantage. Therefore, choosing a vendor that provides dedicated support services with an expert team ensures multiple support channels to help with the quick resolution of concerns regarding adequate support. By taking care of an SLA agreement that clearly defines the level of support in times of escalations, response times can help minimize the operational complexities. If the meta cloud vendor’s existing capabilities do not suffice for business requirements, then third-party support can be sought to gain operational efficiencies. 

3. Development Challenges: There can be development constraints ,as only a few vendors provide CLI services due to commoditization, and businesses have to calibrate by opting for the trial-and-error method. This can lead to enhanced complexities as the lack of synchronization between the common points of interaction, say the interfaces of service providers and APIs, can be difficult. This limitation can be overcome with the identification of alternative approaches and tools that can help in smart management and optimal use of meta-cloud resources.

Additionally, leveraging open-source tools, adopting newer methodologies, for example, infrastructure as a code, and making optimal usage of existing resources can help businesses. It is also recommended to participate in the different vendor communities that can help in gaining relevant understanding, and collaboration with vendors experiencing similar concerns can lead to the development of fresh insights. Also, leveraging open source projects on meta cloud and CLI tools can help in developing a collaborative ecosystem to better deal with any constraints.

Moreover, optimizing automated testing can further help in overcoming synchronization matters, along with change management protocols for the meta environment that enable the smooth functioning of applications and operational efficiency.

Conclusion

Adopting the meta cloud helps insurtechs surpass multi-cloud chaos. However, it is critical to carefully analyze the challenges associated with the super cloud, and mitigating those risks is the first step toward making a clear road to the wide adoption of the meta cloud.

An IT partner with years of technical excellence and domain expertise is highly recommended. The IT partner brings a dedicated team of resources that has a portfolio of successful projects in the past and can play a critical role in navigating the possibilities for bringing more coherence into the myriad cloud architectures. Insurtechs can manage their businesses smartly and cater to their customers with tailored products more efficaciously.

The evolution of telemedicine has been groundbreaking, transforming access and delivery of medical services. What started as a means to reach patients in remote locations has blossomed into a multifaceted digital health ecosystem, encompassing virtual consultations, remote monitoring and now, the integration of artificial intelligence (AI). And this growth is not slowing down post-COVID. Today’s consumers want the flexibility that virtual healthcare provides for incorporating important services into their busy lives. 

For health insurance executives, this integration marks the emergence of AI as the fourth critical user in the telemedicine landscape, joining patients, providers and clinicians. 

The Rise of AI in Telemedicine

AI's role in telemedicine is rapidly expanding, offering solutions ranging from diagnostic assistance and treatment recommendations to patient engagement and administrative efficiency. These advancements are not just additive; they are transformative, reshaping the very fabric of healthcare delivery and insurance operations.

See also: How Digital Health, Insurtech Are Adapting

Understanding the AI Impact

AI systems require vast amounts of data to learn and make accurate predictions. For insurers, this raises critical questions about data management and privacy. How will patient data be collected, stored and protected? Insurers must navigate these waters carefully, ensuring compliance with regulations like HIPAA while leveraging AI's potential.

AI can revolutionize claims processing by enhancing efficiency and accuracy. AI's prowess in identifying patterns can also be pivotal in detecting and preventing insurance fraud, a perennial challenge in the industry. However, AI necessitates a reevaluation of existing systems to integrate its capabilities seamlessly.

Predictive analytics can enable insurers to create more personalized insurance policies. By analyzing vast datasets, AI programs can identify specific risk factors and needs of individual patients, allowing for tailored coverage plans. This personalization, however, must be balanced with ethical considerations to avoid discrimination and ensure equitable access to insurance.

Cost management AI programs can help insurers by predicting healthcare trends and patient needs. This foresight can lead to more effective resource allocation and potentially lower healthcare costs, benefiting both the insurer and the insured.

AI is increasingly becoming capable of assisting in medical diagnoses and care delivery, especially in virtual health settings. While current regulations may limit AI's role in direct diagnosis, technology is being designed that will provide preliminary diagnoses based on patient intake information, assisting physicians and advanced practice nurses in decision-making in the near future. 

AI-driven chatbots are already providing mental health services, offering support and guidance to patients. This emerging capability of AI necessitates a reconsideration of its role in healthcare delivery, highlighting the need for insurers to anticipate and adapt to these advancements.

Engaging with Clinicians and Providers

The integration of AI in telemedicine is not just a technical challenge; it's a collaborative one. Health insurers must work closely with clinicians and healthcare providers to ensure that AI tools are effectively integrated into clinical workflows. This collaboration is essential for realizing the full potential of AI in improving patient outcomes and operational efficiency.

See also: Streamlining Medical Record Reviews Via AI

Navigating New Ethical Terrain With AI in Healthcare Delivery

As AI begins to take a more active role in healthcare, particularly in diagnosis and care, health insurance executives are faced with a complex ethical landscape. A crucial concern is safeguarding patient autonomy and informed consent. It’s imperative that patients are fully informed about the role of AI in their care and consent to its use, ensuring they understand how AI influences their diagnosis and treatment. Alongside this is the need for accuracy and reliability in AI diagnoses. The insurance industry must establish robust protocols to verify AI-generated diagnoses, ensuring they adhere to medical standards and do not perpetuate existing biases.

The integration of AI in healthcare also raises questions about bias and fairness. AI systems can inadvertently perpetuate existing healthcare biases related to race, gender or socioeconomic status. Therefore, it is essential for these systems to be trained on diverse data sets and regularly audited. Transparency in AI decision-making is vital, especially when these decisions affect patient care and insurance coverage. Insurers must ensure that AI systems are explainable and subject to human oversight.

Another area of concern is the determination of liability in cases of AI errors, which poses a complex challenge. As AI assumes more responsibilities in care delivery, it is crucial to establish clear guidelines on accountability, whether it pertains to AI developers, healthcare providers or insurers. Additionally, with AI handling an increasing volume of sensitive health information, reinforcing data security measures is critical to protect patient privacy. Finally, insurers must consider the impact of AI on the roles of healthcare professionals, understanding how it might alter dynamics in healthcare delivery and necessitate changes in training and responsibilities.

By addressing these ethical challenges, health insurance executives can ensure that the integration of AI into healthcare services is not only innovative and efficient but also responsible and patient-centric. This approach will help in maintaining trust, ensuring safety and complying with regulatory standards in the rapidly evolving healthcare landscape.

See also: A Road Map for Generative AI in Insurance

Conclusion

The integration of AI into telemedicine represents a significant shift in healthcare, with profound implications for health insurance. As the fourth user in this ecosystem, AI offers opportunities to enhance patient care, improve operational efficiency and drive innovation in policy design. However, it also brings challenges in data privacy, ethical application and regulatory compliance.

For health insurance executives, the key to success in this new landscape is adaptability. Embracing AI's potential while navigating its complexities requires a delicate balance of technological savvy, ethical consideration and regulatory awareness. By achieving that balance, insurers can not only adapt to this new reality but also lead the charge in shaping a more efficient, equitable and innovative healthcare future.

It’s the rare organization these days that has not at least begun to contemplate using generative AI (GenAI) to unlock new opportunities and enhance workflows. There is good reason for this excitement; GenAI represents game-changing possibilities across industries, including insurance. But, like any new technology, it also comes with potential pitfalls that organizations are well-advised to address before proceeding.

Admittedly, it’s hard not to be enthusiastic about many of the potential benefits of GenAI, including its ability to elevate customer experiences, refine performance in certain areas and drive operational efficiencies. But wise executives will nonetheless proceed with caution when it comes to integrating these new tools into their technology ecosystem. 

If your insurance organization is considering GenAI integration, here are four key questions to ask yourself first.

Does this technology align with your organization’s ethical, security, data and client consent standards? 

Implementing trustworthy GenAI is paramount for business optimization, improved outcomes and reputation protection. Before adopting the technology for any business use case, it's essential to identify and address potential concerns tied to ethics, culture, human factors or change management. 

Once you have confirmed alignment with your organization’s standards, proper training and comprehensive risk assessment are crucial. To bolster trust and responsible AI deployment within your business, it's imperative that GenAI – like any new technology – undergoes thorough security and data privacy vetting to help ensure its ethical use and strategic incorporation. 

Implementing AI into core business functions also demands rigorous testing and validation. Take the challenge of bias in an image detection AI model. When AI is trained on skewed data, it can produce biased performance, which in turn can distort applications that end up relying excessively on these biased sources. To mitigate this problem, companies should diversify and balance their training data, adopting continuous monitoring and retraining practices and seeking diverse stakeholder input during regular audits. 

How will this tool lift up operational, customer and employee experiences?

Executives across the insurance industry are eager to see how GenAI tools can improve processes and increase efficiencies across enterprises. But as tempting as it may be to forge ahead with the technology, the integration of GenAI should always begin with the assessment of current processes. 

Insurance companies should consider GenAI as a tool to enhance processes, not replace them. While technology empowers the business, optimizing workflows is crucial – after all, existing flaws might be amplified by integrating technologies like GenAI. Identifying the pain points in existing workflows can help businesses understand where technology implementation is needed and what technologies can reduce, or in some cases remove, a problem.

See also: 3 Key Uses for Generative AI

In the backdrop of varied legal landscapes, are you adhering to regional laws? 

Given the evolving nature of data privacy laws and the high stakes surrounding client consent, organizations must approach GenAI with utmost caution. A misstep could result in significant legal and reputational repercussions.

Like all evolving technologies, the innovation that GenAI brings comes with its own set of concerns. Large language models produce human-like content but face challenges like misinformation, malicious use and opaque decisions. Unlike deterministic systems that predictably respond to set rules, GenAI operates probabilistically. As a result, it sometimes generates content that is disconnected from reality. In addition, its immense scale presents potential issues with interpretability, bias and control. 

A core concern is AI's potential for delivering results that, while appearing structurally sound, may not always be factually accurate. It's important to note that while AI systems can process vast amounts of information swiftly, their outputs require rigorous validation. Insurance companies should consider implementing a robust AI governance framework so they can harness AI's potential while ensuring that the trust and reliability that their clients expect remain uncompromised.

While deterministic AI provides consistent results and probabilistic AI embraces uncertainties, neither can fully capture the nuances of human understanding. This is where the "human in the loop" approach comes in. Humans bring empathy, ethics and contextual understanding to the assessment of AI outcomes that machines may overlook. This collaboration ensures that AI technology serves as a complementary tool, fostering decisions that are balanced, fair and contextually relevant.

See also: 5 Ways Generative AI Will Transform Claims

Is your GenAI investment calibrated for optimal ROI, and does it fit with your growth goals?

With a more complete understanding of when and how to use GenAI effectively and responsibly, your company will be in a strong position to enhance efficiency and effectiveness through the integration of this technology. Careful consideration of your organization’s goals and objectives and regular assessment to ensure tangible ROI will round out the successful planning and evaluation process for your GenAI investment. 

Remember, for effective AI integration, it's vital to test consistently, prioritize explainability and maintain robust performance to quickly address potential model or data discrepancies. It's also crucial to establish governance policies for assessment and strategy, culminating in a responsible AI framework solution.

With all of these pieces in place, your organization will be in a strong position to unleash the power of GenAI. 

Then -- a history lesson

Raise your hand if you remember when agents faxing hand-written applications to underwriters was commonplace. It wasn’t that long ago, was it?

Now that we have you yearning for the days of yore, let’s go back even further, to the early fire insurance industry. The only sensible way to underwrite a building was for the underwriter to personally visit each prospective property to assess the risk. This approach mostly did the job but was woefully inefficient. 

Daniel Alfred Sanborn began producing and selling fire insurance maps to underwriting companies in the late 19th century. Sanborn Maps provided troves of underwriting details like construction type, fire walls, premises boundaries, natural features, occupancy and proximity to hydrants, fire departments and other structures and hazards. It became possible to underwrite properties without leaving the office. (Don’t listen to the naysayers; insurance has long been a hub of innovation.)

As we progress into the Y2K fears (let’s resist the urge to reminisce about those doomsday prophecies and their accompanying policy exclusion forms), seemingly overnight every insurance professional had a computer. Electronic applications could be completed and emailed quickly to multiple underwriters with a few clicks, and the carriers could expand their distribution more easily.

As insurance companies grew, they fostered heightened competition and developed products that were increasingly customizable to individual risks. 

See also: Underwriting in the Digital Age

One problem persists: The underwriting primarily happens prior to the policy term and is largely based on the historical performance of the insured and other risks of similar makeup. This dynamic is particularly prevalent in small commercial insurance. Small businesses are complex enough to warrant assistance from a trusted adviser to help them get the right coverage at a fair price but small enough in revenue and loss volatility for the carrier to not afford much expense for continuing maintenance. Often, the carrier even waives their right to audit the policy’s exposures.

As we explored in Part 1 of this series, the set-it-and-forget-it strategy for small commercial underwriting has been the standard. Grow the book, ignore it, watch the loss ratio creep, re-underwrite the book. Rinse and repeat. Consumers are at the carriers’ mercy, only hoping they aren’t tabbed to be part of the portfolio getting chopped.

Now -- a case study

Through continuous underwriting practices, we today possess access to data to be smarter underwriters. Through automation, we can scale to benefit more consumers.

To demonstrate how these strategies can be implemented, let’s examine the insurance cycle for Billy Bob’s Bistro (a play on the authors’ names, if you’re paying attention). 

After Behemoth Insurance Company was too aggressive and had to exit the restaurant insurance space, Billy Bob’s Bistro’s insurance agent must find replacement coverage at renewal. Through only a couple simple inputs, the new carrier can instantly learn more about Billy Bob’s than ever before.

As with souped-up Sanborn Maps, the underwriting community consumes assessor data and geospatial imagery to determine construction type and condition, proximity to hazards, fire protection and other underwriting characteristics like replacement cost estimation. We have advanced insights to help us understand probabilities of both natural (flood, hail …) and manmade disasters (crime, nearby underground storage tanks and their last reported leak …). 

Much of this could be considered traditional underwriting data, just accessed instantaneously. But in that same instant we’re now also harnessing additional data. Through Billy Bob’s Bistro’s public profiles on search engines and social media, we can assess things differently than ever before. Reviews alert us of food poisoning concerns, online ratings can show when customer sentiment is decreasing to indicate potential heightened moral hazard, photos can identify the children’s playset that is not properly maintained, the menu’s cuisine points to the equipment maintenance requirements based on the types of cooking performed. 

See also: The Promise of Continuous Underwriting

These insights coupled with a continuous underwriting process are advancing small commercial underwriting into a more sustainably profitable space. With such robust data available, there’s no need to set-it-and-forget-it any more. Now, sophisticated software can monitor changes to individual risks and portfolio trends without increasing overhead.

When Billy Bob’s changes their closing time to 2am and begins promoting live bands, the underwriter knows. When they expand from paninis to deep fried gator bites, the underwriter knows. Because modern technology can make these observations for us, we gain efficiency through monitoring processes.

One source of frustration for Billy Bob was always his term-ending audit. Now, he can register for a pay-as-you-go (PAYG) policy that integrates directly with his point of sale (POS) system. The U.S. restaurant POS market is projected to exceed $10 billion by 2030, thanks in part to the proliferation of food delivery apps integrating into these systems. Because the underwriter now receives Billy Bob’s detailed sales reports in real time, the need for an audit is eliminated, saving Billy Bob time so he can focus on his own customer service. Additionally, because his business has seasonal shifts, the PAYG policy automatically adjusts his premium to reflect his true exposures, a cash flow benefit.

To capitalize on their more popular recipes, Billy Bob’s Bistro buys a van for catering. They add a usage-based auto insurance (UBI) policy to cover the auto exposure while tracking the driving behaviors through a telematics app. Because the trips are infrequent and within a small radius, the UBI policy automatically lowers the premium - but this is more than offset because of Billy Bob’s lead foot.

Billy Bob secured replacement coverage quickly that automatically lowers his premium during slower months and doesn’t force him into a time-consuming audit. His agent has substantially reduced the amount of information he has to provide during the application and renewal processes, allowing the agent to focus on additional revenue generation activity. The insurance company’s PAYG and UBI policies provide an updated risk profile that more closely reflects the desired appetite, removes the risk of premium leakage and guards against insurance fraud with independently verified exposure data.

Most excitingly, these practical examples are all available today with concepts applicable in all industries, including service, retail, hospitality, contracting, healthcare, manufacturing and transportation. With the rapid development of AI, the possibilities for additional insights and predictive models will continue to increase the application effectiveness of continuous underwriting.

Because we are able to process more data more quickly and precisely, we are less reliant on human observation to make every decision. Now those underwriter’s eyes can evaluate a risk with far more granularity. Done properly, this can provide benefits to stakeholders on all sides of the insurance transaction.

Continuous underwriting is here to stay and already proving its worth. Capabilities will only continue to expand as new data sources are brought into the underwriting workbench. Done properly, continuous underwriting can provide benefits to stakeholders on all sides of the insurance transaction and advance the practical evolution of underwriting and insurance, even if it’s hard to hang on the wall next to your firemark Sanborn Map and Y2K exclusion. 

The insurance industry is facing a talent shortage several years in the making at a time when business expenses and policyholder expectations are higher than ever. Many insurance companies are burdened with inefficient billing and premium collections technologies that complicate payments.

Insurers must utilize digital transformation to overcome the workforce shortage, transform the policyholder experience, and improve organizational efficiencies allowing staff to focus on other critical tasks.

Download The Digital Bridge: Closing the Insurance Talent Gap and learn how to:

• Leverage technology to bridge the industry workforce gap
• Enhance the policyholder experience by automating the most frequent touchpoint
• Ease staff workload, reduce costs, and retain policyholders

Sponsored by ITL Partner: InvoiceCloud

Robert Marshall is the founder and CEO of Whisker Labs. Whisker Labs, a spinout of Earth Networks, delivers next-generation home energy intelligence technology to realize the full potential of the connected home.  In 1992, Marshall co-founded AWS Convergence Technologies, the company that would become Earth Networks, by pioneering the networking of weather sensors and cameras using the internet. By developing groundbreaking technology to find "signals" — valuable, meaningful intelligence — in big-data "noise," Marshall improves people's lives and protects their livelihoods. He has appeared on CNN, BBC World News and ABC Nightly News and has been quoted in major news outlets that include the New York Times, the Washington Post, Nature and Scientific American.

Insurance Thought Leadership:

How is Whisker Labs technology making homes and communities safer? 

Bob Marshall:

Whisker Labs' breakthrough technology, Ting, protects homes and communities from fires by detecting arcing, the precursor to most electrical fires. A single, plug-in sensor monitors an entire home and is proven to prevent four out of five electrical fires. To date, Ting protects nearly 450,000 homes and has saved over 6,500 families from electrical hazards that could have led to devastating fires. Outside the home, Ting is advancing how grid resilience is measured and monitored as it detects electrical faults along the utility grid, helping protect communities from catastrophes such as wildfires.  

Insurance Thought Leadership:

Can you discuss some of the challenges associated with integrating IoT technology into insurance and risk management? 

Bob Marshall:

Successful integration of an IoT program relies on overcoming several fundamental challenges to fuel program growth and a robust ROI: 

1. The program must be strategic and have executive buy-in. Support for the IoT technology must start at the top. From there, all stakeholders should see the technology as a strategic investment for their customers and for the long-term benefits it will provide. I should add that, while early forays into IoT largely fell flat, they still yielded lessons on what doesn’t work. In any case, these past projects should not serve as reasons for hesitation. It is a different time, and IoT is here to stay.  
2. The solution must be compelling. Homeowners must be inherently interested in the value proposition the IoT technology offers. In the case of Ting, for example, we find that people rightly have an innate fear of fires. The offer of electrical fire prevention naturally resonates throughout the homeowner community. This drives successful enrollment rates. 
3. There must be a robust, yet turnkey marketing program. To further fuel interest, marketing strategies must be implemented to spread awareness and make signing up easy. An IoT business partner should make it simple for insurers to unlock required marketing channels such as landing pages, email, direct mail and agents engaging with the program. Clear and consistent messaging across all touchpoints helps earn homeowner trust. 
4. The technology has to be super simple for customers. Once successful enrollment is established, ROI hinges on a high activation rate. The technology must be easy to understand, low (no) maintenance and highly compatible in all homes. Ting, for example, is a DIY install that takes just two minutes, which helps drive activation rates over 85%. It turns out our focus early on to wrap the complexity of the technology into such a simple customer experience was even more critical than we first thought – the results validate our approach. 
5. Operations and data integration must be seamless. Behind the scenes, data flows via API integrations must be in place to ensure IoT providers and insurers can seamlessly connect and communicate through all stages, from fulfillment to claims avoidance, and ensure a stellar customer experience at every step. 
6. Finally, ROI must be demonstrated. This first means that the technology has to do really well at its fundamental function. Equally important, IoT partners and insurers must be fully aligned on success metrics, and ultimately the program must demonstrate an ROI that supports long-term success. This, in turn, requires proven technology and a willingness on the part of the insurer to expand rapidly – and the ability of the IoT partner to support that scale – to demonstrate the true impact the technology can deliver.   

Insurance Thought Leadership:

What are the pros and cons, from your perspective as an IoT service provider, of the new software standard Matter? Will it accelerate consumer adoption of devices like Ting? 

Bob Marshall:

We welcome any industry-wide effort that has the homeowner’s best interests in mind. However, all signals point to a slow start for the standard. It needs more time, and it will be a while until other device classes are incorporated and adoption becomes mainstream. At that point, it could help accelerate the adoption of devices that the consumer otherwise would have avoided from the inconvenience or complexity of splitting command and control across more than one hub/interface/app. With Ting, however, fire avoidance is such a compelling value proposition – and there are no command-and-control dependencies – so we don’t anticipate Matter having a material impact on Ting's adoption. 

Insurance Thought Leadership:

Whisker Labs has been able to use data to show utilities the power of analyzing an electrical grid for potential issues. It’s a good example of commercial risk management applications for your device. Can you describe how that use case is going? 

Bob Marshall:

We're finding the use case is even more compelling than we first thought.  Frankly, it is alarming that millions of homes and businesses are being supplied with dangerous levels of power from an aging, deteriorating and often-over-tasked electric system. This problem is only amplified by climate change and increases in electrical demand and grid complexity. The Ting sensor network is the largest network to ever measure and monitor the electrical grid, revealing first-of-its-kind data on its health and state of resilience. The beauty of that for all stakeholders is that the network is already deployed. 

Ting empowers utility companies to think about prediction and prevention; in the same way, the insurance sector has adopted this model. Currently, Ting data is being used to detect and repair home fire risks that originate from faulty utility equipment. But the potential impact is much greater. Data from the Ting network can help to prevent catastrophic events like wildfires and grid meltdowns, as it detects faults along the entire grid and can help direct investment to the areas where it is needed most. 

Insurance Thought Leadership:

Are there other risk management use cases for Ting that you see in the next five years? 

Bob Marshall:

We are laser-focused on helping to protect customers by preventing fires in homes and throughout communities. Ting technology does, however, present several opportunities beyond fire prevention. For example, Ting is already capable of temperature-sensing, which has resulted in cases of frozen pipe avoidance. Similarly, Ting prevents water losses as it detects electrical hazards with sump pumps, water heaters and other devices and appliances that can lead to leaks.  

In the future, Ting will have the capability to predict problematic equipment failures with HVAC systems and other key appliances. The technology can also be expanded beyond the home to commercial applications for insurance partners. Ting’s ability to monitor and spot power quality problems holds great promise for commercial equipment resiliency. Perhaps most compelling is the opportunity to grow and integrate the data sets Ting is collecting with existing insurance risk models to help sharpen and expand how our partners think about and evaluate power and fire risk.    

Insurance Thought Leadership:

Insurers need data to evaluate what reductions in premiums could be offered to those with smart home devices. Has that been a challenge to generate, or is there an easy case to make for Ting? 

Bob Marshall:

Our model with Ting is to provide the technology to homeowners for free. Economic ROI for our insurance partners is clear, and marketing benefits are off the charts, with a constant stream of incredible testimonials from customers who are grateful that the insurance partner, via Ting, has helped to protect their home and family. The benefit of risk reduction thrills customers today even without lowering premiums. It is still a little early for insurers to inform reductions, but with Ting at scale, they will be able to offer more tailored premiums that will likely result in a reduction due to Ting protecting the home and to substantially lowered claim costs. 

Majesco’s latest research report reveals the top 10 trends in 2024 that are shaping the insurance industry and why insurers need to move beyond legacy and adopt new technology that can pave a way for a brighter future.  

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Sponsored by ITL Partner: Majesco

Vehicle makers have long complained they don't generate the revenues and profits they deserve. They design, build, market and sell the world's cars and trucks, creating a massive market -- but a huge percentage of the value flows downstream to others.

Car makers may capture revenue from financing and perhaps warranties, but others service and repair the vehicles, provide the gasoline, develop the navigation apps and, yes, sell the insurance. The auto makers would dearly love to capture more of that downstream revenue and seem to have especially aggressive plans on insurance.

But a recent article on Tesla's problems developing an insurance business, on top of Goldman Sachs' face plant in consumer banking, suggests that the auto behemoths may not be as threatening as insurers once thought. 

The big original equipment manufacturers (OEMs) have premised their push into insurance on data. At a time when consumers are increasingly open to having premiums priced based on how much, when and how they drive, insurers need to be able to collect that data -- and the OEMs already have access to it.

Because of all the sensors and communications capabilities they build into their vehicles, OEMs don't have to install a telematics device, monitor a driver's behavior for a month or two and wait to generate an appropriate premium for a new customer. The OEM already knows what that car has been up to in prior months and can generate a personalized premium from the get-go. 

But a recent article in Reuters about Tesla's move into insurance shows the difficulties that come when a manufacturing company that touches consumers only indirectly, through dealers or repair shops, tries to set up a direct-to-consumer service business. A consumer business comes with, well, consumers, and those consumers demand service. 

Reuters reports:

"Complaints about Tesla Insurance are drawing scrutiny from state regulators and the plaintiffs’ bar. The Ohio Department of Insurance at least twice this year determined that Tesla had violated the state’s insurance regulations in handling claims, including for a lack of timely communications with a policyholder.... Phil Fioresi Sr., a stonecutter in South San Francisco, California, told Reuters it took about 15 calls to reach someone at Tesla Insurance after his daughter’s car was struck by one of its policyholders in September....

"The insurer wouldn’t divulge the current number of claims adjusters. But the dozen or so adjusters who started handling California claims in late 2021 were quickly so swamped that resolving cases took weeks or months, the people familiar with the operations said. At the time, Tesla insured more than 50,000 vehicles in the state, according to California Department of Insurance records....

"Working out of a Tesla office in Draper, Utah, the initial adjusters sometimes had to take on hundreds of claims each, far more than at other insurers, according to the sources with knowledge of Tesla Insurance’s operations. Unlike competitors that often have separate call centers to take claim reports, Tesla’s adjusters had to answer the phones themselves while also handling claims."

Tesla is also facing a class action that alleges it overcharges insurance customers because its data-gathering is faulty and unfairly generates reports of dangerous driving, as this article in Forbes explains. 

Tesla's CEO, Elon Musk, has been known to fly by the seat of his pants at the many companies he runs, and we will surely see a more disciplined approach from GM, which has announced big plans for insurance, and Ford, which is rumored to have a major initiative in the works. 

But Goldman Sachs still offers a cautionary tale. It has the same sort of strong brand that GM and Ford do and is known for relentless management. It even did a deal with Apple, maybe the strongest brand of them all these days, to offer a credit card. Yet Goldman Sachs has announced it is leaving consumer banking and has taken billions of dollars in losses -- losses totaled $1.2 billion as of August 2022, according to Fintech Nexus, and have continued, to the point that Apple is seeking to end its partnership with Goldman Sachs. 

The problem wasn't lack of customers. Goldman amassed more than $100 billion in consumer assets, and more than 6 million signed up for the credit card. The problem was that, while investment banking and consumer banking are both financial businesses and have similarities when viewed from 10,000 feet, they are very different when viewed at street level, where those consumers live.

Fintech Nexus reports, "One of the main areas of focus of [an investigation by the Consumer Financial Protection Bureau] had been Goldman’s handling of credit card disputes, the level of which the company had been reportedly unprepared" for. 

Goldman Sachs also misunderstood a key issue about consumer banking -- it thought physical branches would fade in importance faster than they have, creating an opening for a generally virtual presence by Goldman. In addition, the firm found that "robo-advisers" weren't nearly as effective in a mass market as it had hoped. 

GM and Ford aren't Goldman, and insurance isn't consumer finance, but Chunka Mui and I found in our years of research into corporate failures for "Billion Dollar Lessons" that planned moves into adjacent markets are one of the seven strategies most likely to lead to disaster. The reason: exactly the sorts of issues that Goldman faced and that GM and Ford may well find. Markets that seem adjacent at a high level can have unforeseen and crippling complications.

Already, there is reason to doubt whether the OEMs will have as big a data advantage as they seem to think they do.

There is a battle shaping up about who owns that data. Insurers act as though they do, whether individually or shared through Allstate's Arity unit. Car makers act as though they do. But I think consumers own the data, and regulators are increasingly siding with me.

The regulators, especially in Europe, are reining in the indiscriminate collection of consumer data by Google, Meta and others and giving control to consumers. And it's hard to imagine that limits set for Big Tech won't filter their way into other parts of the global economy, including car insurance. 

If GM has to ask me for the use of my data rather than just collecting it through OnStar, the equation changes.

Tesla, GM, Ford and perhaps others will, of course, face firmly embedded competition, too. As Barron's reports, "While the opportunity is big, making it a reality won’t be easy. State Farm, Allstate (ALL), Progressive (PGR), and Geico, which is owned by Warren Buffett’s Berkshire Hathaway (BRK.A), control roughly 50% of the U.S. market and won’t cede share without a fight."

Barron's adds that "the data coming from the cars may not be as valuable as the auto companies think. For the most part, auto insurance... shouldn’t be all that complicated. Calculating the number of cars that will get into an accident and what it costs to fix them is relatively easy.... The data coming from cars, while helpful, may not be all that necessary.

“'Seventy percent to 80% of drivers are what are called clean, meaning they just haven’t really had any accidents in five years,' [a research analyst] says. 'You don’t need user [data].'”

Cheers,

Paul