By enabling remote patient monitoring and virtual consultations, telehealth has brought healthcare to the doorsteps of patients. Underpinning this revolution are data engineering and analytics, playing a pivotal role in driving efficient, personalized services while enhancing patient outcomes.

Role of Data Engineering in Telehealth

Data engineering is the backbone of telehealth services. It involves collecting, validating, storing, protecting, and processing required data to be accessible and informative. Data engineers develop and maintain systems to ensure that the massive amounts of data generated in healthcare are systematically processed and ready for analysis. Telehealth involves various data types like electronic health records, real-time physique data, and even video consultations data. A robust data engineering framework ensures smooth management and processing of these diverse datasets.

Analytics: Empowering Decision Making in Telehealth

Once data engineering lays the groundwork, analytics steps in to drive insights from the data. It involves the discovery, interpretation, and communication of meaningful patterns in data. Descriptive analytics help with understanding the current healthcare conditions, while predictive analytics anticipate healthcare outcomes based on patterns gleaned from historical and real-time data. Prescriptive analytics goes a step further to provide recommended actions to achieve desired health outcomes.

Through the use of sophisticated techniques and algorithms, healthcare providers can leverage these insights for patient diagnosis, treatment planning, risk prediction, and cost-effectiveness. For instance, predictive analytics can forecast trends and outcomes, such as risks of specific diseases, aiding in preventive care.

Impact of Data Engineering and Analytics on Telehealth

1. Personalized Care: With the insights provided by data analytics, healthcare providers can offer tailor-made treatment plans to their patients, enhancing patient satisfaction and outcomes.
2. Improving Access: Telehealth, powered by robust data engineering and analytics, extends the reach of healthcare services, allowing providers to reach underserved communities and cater to the aging population in the comfort of their homes.
3. Cost Savings: Optimized prediction models and analytical reports can guide effective resource allocation, reducing operational costs and helping providers deliver quality care economically.
4. Efficient Service Delivery: Real-time analytics can help in the immediate detection of critical health conditions, enabling immediate intervention and thereby improving treatment results. Also, analytics helps healthcare providers promptly respond to changes in patient condition or behavior.

Data Sharing: Provider Enablement

Data sharing enhances the potential of telehealth, allowing for an improved healthcare experience for both the providers and the members. Here's how:

Enhanced Care Continuity: If providers within a network have access to the shared data, they can understand the medical history, current medications, and other crucial health-related information about a member. This ensures care continuity even when the member switches between different providers or needs to consult multiple specialists in person or through telehealth.

Improved Decision Making: Data sharing arms healthcare providers with the necessary insights to make evidence-based clinical decisions. Comprehensive knowledge of a member's medical history allows providers to accurately diagnose conditions, devise effective treatment plans, and predict potential health risks. This helps providers to prescribe the precise medicine even over telephone.

Coordination and Collaboration: Data sharing enables multi-disciplinary team collaboration in managing a member's health. Various providers caring for the same member, such as primary care physicians, specialists, nurses, and pharmacists, can access shared data to coordinate care effectively.

Efficiency and Convenience: In telehealth, providers can access shared data from anywhere, reducing the need for repetitive tests and, hence, enhancing administrative efficiency. For members, telehealth eliminates the inconvenience of repeated information sharing or undertaking the same tests.

Preventative Care: Predictive analytics can use shared data to identify early signs or risk factors of diseases, allowing for timely intervention and preventative care. Early prevention leads to better health outcomes for members and potentially reduces healthcare costs.

Trust and Transparency: When members know that their health data is being shared securely but with a purpose, the knowledge can promote a sense of trust between providers and members, leading to better engagement and adherence to treatment plans.

In essence, data sharing in telehealth signifies a move toward integrated, coordinated care, yielding improved health outcomes, cost efficiency, and member satisfaction. As we navigate the digital age, secure and effective data sharing protocols will be paramount to the evolution of telehealth services.

See also: How Cloud Tech Improves Customer Experiences

Role of Cloud Technology

The use of cloud technology has enabled greater capacities for data storage, processing, and analytics, making it crucial for the effective management of healthcare data. Here's how the cloud plays a significant role in data engineering in telehealth:

Scalable Storage: With the massive amounts of data being generated in telehealth, cloud technology offers scalable and cost-effective data storage solutions. Cloud storage can easily scale up or down based on the data volume, thereby efficiently managing storage needs.

Data Integration: Cloud platforms facilitate seamless integration of diverse health data from multiple sources, such as electronic health records (EHRs), wearable devices, and images. All relevant patient information can be consolidated in a single, accessible platform.

Real-Time Access and Collaboration: Cloud enables real-time access to data for healthcare providers, regardless of their location. This fosters collaboration among a patient's healthcare team, allowing them to provide coordinated care based on comprehensive, up-to-date information.

Data Security and Compliance: While data security is paramount in healthcare, cloud service providers offer advanced security features to protect sensitive health data. They also help ensure regulatory compliance, including adherence to rules set by the Health Insurance Portability and Accountability Act (HIPAA).

Advanced Analytics and AI: The cloud supports sophisticated data analytics tools and AI capabilities. These tools can process and analyze large sets of data, yielding insights that can guide clinical decisions and health predictions. They also support machine learning models that can identify critical health patterns and predict outcomes, enhancing telehealth services.

Speed and Efficiency: Cloud computing allows quick processing of large volumes of data, minimizing latency. This efficiency is critical in telehealth settings, where swift data access can affect health outcomes.

Reduced Costs: By using cloud-based solutions, telehealth services can avoid the high costs associated with maintaining physical servers and data centers.

Cloud technology plays an essential role in data engineering within telehealth, providing the infrastructure needed to collect, store, integrate, analyze, and securely access health data. As healthcare continues its digital transformation, the cloud will continue to deliver innovative, flexible, and secure solutions.

See also: Moving From Legacy Systems to the Cloud

Conclusion

Data engineering and analytics play an integral part in telehealth, turning vast amounts of data into actionable insights. They allow the efficient delivery of healthcare services, increase patient satisfaction, and cut costs. As telehealth continues to grow, advancements in data engineering and analytics will usher in a new era, promising improved outcomes for both healthcare providers and patients.

In the never-ending pursuit of customer satisfaction, more merchants are finding they can delight customers by offering insurance right alongside products.

But while businesses are excited over the growth potential of embedded insurance, one industry is feeling the pressure: insurance. 

There are huge opportunities for insurers and insurtechs, but they must face these hurdles head-on:

HURDLES

1. Outdated UI/CX 

While the world is busy building modern apps, many insurers are still stuck with complex systems due to lack of resources and complicated regulations. Consumers will have a smooth user interface (UI) until they are redirected to the insurance part. Jargon-filled insurance policies and complex claim processes often turn off customers. 

Although insurers have started to improve, there’s still a lot to be done. Until then, you can use these strategies to improve the customer experience (CX):

• Minimal Integration: Use the merchant’s platform to show basic policy information and claim status, but anything complex? Redirect to the insurer website.  
• Dedicated Customer Support: Support customers to navigate the complex platform through email and chat. 
• Third-Party Integration: Until you fully modernize, integration with third-party digital tools can help cross the UI/CX hurdle.

For example, consider a customer renting a car online. The process is quick and seamless, but when the insurance part hits, the experience often becomes fragmented. Your company can make a difference here by letting the rental company offer only basic insurance details online while you assist customers with complex claim processes. 

Insurers such as Geico and Lemonade have already started to improve their customer service with advanced support systems and third-party AI chatbots. You can use similar solutions to help your customers navigate through complex processes and enhance their experience.

2. Poor Communication With Merchants

When insurance products get bundled with the merchant's, ensuring a seamless customer experience is on insurers. You need to establish clear communication with the merchant to avoid misalignment while framing the terms and conditions for the product.

• Merchant Support: For instance, Tesla is supporting its insurance provider State National Insurance by handling most of the insurance queries on Tesla’s platform.
• Co-Branded Marketing: Market jointly to build brand awareness and trust.
• Data-Sharing Agreements: Maintain customer insights by creating data-sharing agreements with the merchants. 

If you are a travel insurer, you can partner better with merchants like online booking platforms. Imagine a customer booking a flight and being presented with a personalized insurance offer that covers everything from trip cancellations to lost luggage, all without leaving the booking site. This not only enhances the customer experience but also increases the likelihood of a purchase.

You and the booking platform can also promote your products together. This makes the insurance offering an extension of the travel booking process, which can drive sales.

3. Data Privacy and Customer Knowledge

As embedded insurance involves data-sharing between merchant and insurer, it's the insurer’s responsibility to safeguard customer data and educate customers about the terms and conditions. Here’s how you can do it:

• Regulatory Compliance: Adhere to data privacy regulations such as GDPR and CCPA.
• Standard Privacy Policy: Provide clear privacy policies that outline data collection and usage practices.
• Customer Education: Offer all the must-know information about the insurance products on the merchant’s platform.

As an insurer, you know that transparency and trust are crucial to customer satisfaction. Imagine the impact of a seamless integrated experience where a customer is provided with all the necessary data about the privacy policies and the risks associated with it while booking a flight ticket or getting employee-provided health insurance. 

This level of transparency not only builds customer loyalty but also strengthens your brand's reputation in an increasingly competitive market. You can enhance the customer experience by keeping them informed, protected, and engaged about how their data is being used by the insurer.

See also: Is Embedded Insurance the Wrong Idea?

NEW INSURANCE MODELS

The advent of connected devices such as wearable devices, IoT, and smart home devices opened a new era of possibilities for the insurance industry. New insurance models can attract users to move from the old way of paying annual premiums. Data generated through the connected devices further helps insurers cater to more specific and personalized products. 

1. On-Demand Insurance

This model will give the convenience of using insurance for your immediate needs and help avoid unnecessary costs for coverage you don't use. 

Uber, a ride-sharing service, offers its customers and drivers the option to purchase insurance coverage on-demand. This means that both parties can use insurance protection only during the ride. Uber leverages connected devices like GPS and telematics to accurately track their vehicles, ensuring that both riders and drivers are protected precisely when they need it.

2. Contextual Insurance

Ever thought about insurance that adapts to your real-time needs? Contextual insurance makes this a reality by providing coverage based on the context of the user’s activities. 

Imagine you’ve just installed a smart home system that can analyze data like occupancy patterns, appliance use, and even environmental conditions. This data offers a clearer picture of potential risks in your home, allowing your insurance coverage to adjust.

This approach ensures that you’re not paying for unnecessary coverage but are instead getting precisely it when you are in need.

Google Nest’s extended warranty is a prime example of contextual insurance. At the time of purchase, you’re offered a plan that not only covers accidental damage but also uses data from your thermostat to offer protection to your home. 

3. Pay-Per-Use Model

Why should consumers pay heavy premiums for products they use occasionally? The pay-per-use model offers the convenience of integrating insurance into existing services or products and the flexibility to use it only when it is needed.

Consider purchases of high-tech products. Merchants offer a standard warranty period for that product. If customers wish, they can opt for an extension only to some specific parts of the product, like extending the warranty period only for the motor of a washing machine.

See also: Beyond the Hype on Embedded Insurance

THE FIRST STEP

Customer experience is not just about keeping customers happy—it’s about acquiring and retaining them with minimal costs. 

That’s why many businesses are shifting toward embedded insurance. To stay in line, insurers are relentlessly working to overcome challenges and deliver personalized customer experiences. The emergence of new insurance models is a clear sign they are on the right track.

The future of insurance is undoubtedly embedded, and your current progress will benefit insurers, merchants, and, most importantly, your customers. 

Half of Americans have life insurance; half do not. Is this a glass half full? Or a glass half empty?

The truth is: It doesn’t matter. We are here to help everyone who needs life insurance get coverage. But how do we move the needle? The answer is social media. But let me back up a bit. 

First, we have to be laser-focused on addressing the reasons people tell us they are not buying life insurance, or more of it. That seems logical, but many companies bypass that step for marketing strategies rooted in complex internal politics. 

Why People Don’t Buy

So, why are people not buying coverage? The 2024 Insurance Barometer Study, which the nonprofit Life Happens does in conjunction with LIMRA, delves into just this. I’ve worked on the study since its inception 14 years ago, and the reasons people have for not buying life insurance have stayed consistent.

• People think it’s too expensive. 
• They say they have other financial priorities.
• They are not sure how much or what type to buy. 

Let’s take the top reason.  People think life insurance is too expensive. But, according to the study, 72% overestimate the true cost. In fact, half the population think it’s two-and-a-half times as expensive than it is. How are they estimating what it would cost? More than half (54%) say they use either “gut instinct” or “a wild guess,” according to the study. 

Many admit they are not knowledgeable about life insurance (44%), and half of women say they are not (51%). Income is not a huge predictor of people being more confident about what they know: 41% of the middle market ($50K-$149K) and 39% of top income earners ($200K+) say they are not knowledgeable about life insurance.

Whatever their income level, people are not going to buy what they don’t understand and what they think they can’t afford. 

There is a key step that can’t be missed, and that’s to educate them about what life insurance does, about why it is important for them, and that there is a policy (or policies) that fits their needs—and budget. 

See also: Insurance Is Not a Commodity

The Importance of Social Media

The “how” of reaching people and educating them is becoming a very interesting subject. Increasingly, the focus is social media. We just released a supplement to the Barometer Study called “Reaching New Insurance Buyers,” which delves into social media usage in our industry.

This year, six in 10 people say they use social media sites when seeking information on financial or insurance products. In 2019, that figure was just two in 10.

I’ll let you sit with that information from a minute. 

Now, ask yourself: What is my company doing on social media to provide this educational information about the products we sell and then turn people into customers?

I can probably guess the answer: not enough.  

What’s happening with social media and our industry is a seismic shift. Over the years I’ve been doing the Barometer Study, I don’t remember any shift of this magnitude in such a short time.

To stay relevant, we need to pay attention to this shift whether we want to or not. Whether compliance wants us to do it or not. Whether our blinged-out traditional advertising campaign wants us to or not. Being set up for success means being on social media to educate and help consumers feel confident purchasing the solutions your company has.

There are a number of companies that are 100% homed in on social media, have advanced strategies, are working with influencers, and are advertising on social as well as growing their organic following. These are the folks that are going to eat your lunch. 

See also: Revolutionizing Life Insurance Uptake in Younger Markets

How can you start or make some significant steps to enhance your social media? Here are some suggestions:

Make internal changes—even if they are incremental. Educate compliance. Having to follow compliance rules is no longer an excuse. How do we know? Just look at your peer companies that are doing social media well. They are working within the same legal framework you are.

Don’t keep social siloed. One of the most perplexing things I see is huge companies with one, maybe two people working on social media, but off in a dank, dark corner. I exaggerate, but not by much. The social team should be 100% integrated into your marketing strategy and team—it should be so enmeshed that you don’t make a move without questioning: How do we integrate social with this? 

Just start and then be consistent. Making mistakes is fine—we all do. The beauty of social media is that it is fast-moving, and you can course-correct easily. It may take time to build a following, but as you can see from the Barometer numbers, things can also move with lightning speed. 

Stop doing what doesn’t work. One of the biggest mistakes I see is using corporate social media for “vanity” postings. We all know what this is. Stop it. 

Know that great educational content wins the day. At Life Happens, we’ve been doing social media for more than 15 years and know what works (and what doesn’t). We rely heavily on statistics to inform our content strategy. For example, stats showed that we had 153 times more organic engagement on Life Happens’ Facebook than the average of the biggest life insurers. Our Instagram also outpaces what companies are doing. 

Celebrate success and communicate it. Remember that you have internal audiences that you will have to convert. Be the champion of your social media, articulate your wins well internally, and win ever-increasing budget to support that success.

Honestly, we as an industry make social media more complicated than it is. 

Educate consumers about the products that can help them build a strong financial foundation, and then show them solutions they can afford. Do this where consumers spend time—on social media—and in a way that is engaging (think the opposite of what compliance would have you do or say). The key is to effectuate change internally to help you reach that goal. Good luck!

Fifteen years ago, I was working for a company with offices in California, New York, and London. I was at a conference in the U.S. My wife was back in London, with our three young children while overseeing the renovations of our 300-year-old house.  

While I was away, the kitchen ceiling collapsed, bringing down with it an old water tank and soaking everything in dirty water. The au pair quit the same day. I hadn’t been in touch for 48 hours. My wife didn’t even know where I was staying. 

So she sent an email to Matthew Grant – expressing some frustration that I hadn’t called and needing sympathy for the troubles at home. An hour or so later, she received a response:

“I’m sorry but I don’t know who you are.”

She saw red. 

How dare I pretend not to know her because I was at work and she was having a hard time? She shot back a response:

“You B@st@rd, how dare you ignore me just because I’m having bad day. Who do you think you are?” 

A few minutes later, she got a reply

“No, I really don’t know who you are. I am not your husband. But I do hope you sort out your troubles.”

She looked at the email address she had used. She had indeed sent her original email to Matthew Grant at Hotmail. Just not to the right Hotmail address. 

We laugh about it now, but we both learned something about communication that day.

See also: Life Insurers' Communication Problem

Don’t be a stranger…

I was reminded of the comment, “I don’t know who you are,” when looking this week at the backlog of all the people who have asked to connect to me on LinkedIn. 

I have a something of a love/hate relationship with LinkedIn. 

We use it a lot at InsTech for sharing information about what we are doing and what our clients and members are up to. It’s a great way to share longer-form articles, event information, and links to our podcast episodes.

But none of us can control what the LinkedIn algorithm does with its content. Despite being selective about whom I open up my network to, I still get too much irrelevant information in my feed. And I get a lot of strangers asking to connect. 

LinkedIn favors people from our connections when it shows us content, so one way to manage the quality of what we see is being thoughtful about whom we agree to connect with. 

I also still believe in the original concept of LinkedIn, that connections are people in your network whom you know and whom you have a connection with in the real world outside of the platform. 

I realize this may be more of a personal pet peeve, but I suspect I am not alone. Some people value the quantity of their connections on LinkedIn in the same way others value the size of their bank accounts. I’m not judging that approach if it works for you. For me, personally, I have a bias toward the quality of my connections.  

I tend to get four types of people asking to connect. You probably do, too:

Type One – People I know. A pleasure to be connected, thank you.  

Type Two – People I don’t know but who send a message explaining why they want to connect. I almost always respond to those and accept. 

Type Three – People from companies that are already clients or who are likely to be buyers or users of our services, or the services of  our clients and members. If you work for an insurer or a large technology company, or are an existing client, or perspective client, we need more people like you in our network. Thank you. 

Type Four – People I don’t know and whose employers I don’t know. These people offer no message or explanation about why they want to contact. If I don’t know you, how do I know how I can I help you, or what I have I done that brought you to me? Sorry, but that will be a “decline.”

See also: 3 Steps for Insurers to Keep the Human Touch

Managing the communication overload

We are overwhelmed with the need to communicate today. Sometimes we spend too much time in “receive” mode and neglect the effort we spend in “transmit” mode. We respond to what is present, not what is important. We can write quickly, but we suffer regret slowly. 

Can I offer some advice that might help make sure you are not the stranger and may reduce the likelihood of a small communication catastrophe?

Lesson One: Double check the email address before you send. 

It’s too easy to send an email to the wrong person. I suspect you have done it more than once. I have. Most email tools use text prediction to complete the email address you start with. There are lots of Matthews out there. Are you sure you have the right one?

Lesson Two: Never say anything in an email you write that you wouldn’t want the person you are writing about to read. 

Assume that any email you write about someone will be sent to that person. Because that will happen one day. It’s Murphy’s Law. If something can go wrong, it will. Harsh words said in haste can’t be taken back, and they are really hard to recover from.

Lesson Three: Who are you? Introduce yourself when requesting a connection on LinkedIn. 

If you want to get someone’s attention on LinkedIn, take the time to say hello and introduce yourself. Tell them what they have done that has caught your attention. Don’t make the first contact be a pitch to sell something. 

Lesson Four:  If you travel and have someone waiting at home, check in every single day. 

A phone call is best. Otherwise a text, WhatsApp, or email at least shows you remember them. It’s not every day something troubling happens, but each day does have something worth sharing, the good and the bad. These are the small parts of our day that may be forgotten or seem not important 24 hours later but which form the broader tapestry of how we experience our lives. That daily contact is part of the vital glue that holds us together. 

I am still married to Mrs Grant. Occasionally, the other Matthew Grant does still get emails. I’m assured the offer to book flights to go on holiday in Bermuda that he received was entirely accidental.

Four suggestions. Take them or leave them. If you have read this far, then this is one communication that has succeeded. That’s good enough for me.

How Digital Trust Strengthens Customer Loyalty

In today’s fast-paced world, insurance companies face the challenge of balancing customer trust with quick service. Technology allows quick decisions and real-time service but also makes customers more impatient and demanding. Insurance carriers must meet these needs while being careful, especially to prevent fraud.

Good processes help maintain customer trust. But trust can also slow down services. This blog looks at whether customer interest and care can work together. 

The Shift from Distrust to Trust Automation

In the past, carriers unfortunately had little options than to operate based on distrust, focusing on controlling damage. There are many rules to manage risks and avoid losses. Now, the industry is moving towards each customer’s experience. Modern carriers want to build personal relationships. They use technology to meet each customer's needs. The market is less about groups and statistics and more about serving one unique person well.

Trust. Automated. A Solution for Modern Challenges

Automating trust processes helps carriers manage lots of interactions smoothly. If you can automate who to trust based on real-time data, carriers can act quickly and focus on high-risk cases, while speeding up low-risk ones. By using advanced analytics and machine learning, the platform spots patterns and odd behaviors. 

Key Parts of Trust Automation

1. Primary Verification: Checking personal details, income, and more to ensure accuracy. This step ensures that the information provided by customers is correct, which builds a foundation of trust.
2. Text Mining and Anomaly Detection: Analyzing large amounts of data to find unusual patterns and potential fraud. This helps in identifying risks early and avoiding potential losses.
3. Social Network and Voice Analysis: Mapping social links and analyzing voice signals to judge trustworthiness. Understanding relationships and how people communicate helps in assessing risk more accurately.

Benefits of Trust Automation

Automating trust brings significant benefits for both customers and carriers. 

Customers value quick and efficient service. With trust automation, carriers can offer expedited claims processing for trustworthy clients. This reduces wait times and improves customer satisfaction. Happy customers are more likely to remain loyal, refer others, and even purchase additional policies.

For carriers, the benefits extend beyond customer satisfaction. Trust automation streamlines operations by handling large volumes of data and interactions consistently. Carriers can standardize verification processes, ensuring that all customers are treated fairly. This consistency builds trust and helps in maintaining a positive reputation.

Detecting and Preventing Fraud

As data grows, systems get more complex. This affects decision-making speed and makes fraud harder to spot. Trust automation helps normalize and standardize many processes. It lets skilled staff check exceptions when needed. Examples include:

• Text Mining: Analyzing large amounts of text for patterns and tendencies. This involves scanning through documents, emails, and other text data to find inconsistencies or red flags.
• Anomaly Detection: Identifying items or events that don’t fit expected patterns. This could be unusual claim amounts or irregular policyholder behavior.
• Social Network Analysis: Mapping social structures to find hidden connections. This technique helps in identifying relationships that may not be apparent but could indicate collusion or fraud.
• Voice Analysis: Using voice analysis to catch signals of someone’s state of mind. Voice patterns can reveal stress or dishonesty, providing additional clues during claims processing.
• Financial Risk Analysis: Charting the financial risk of a company or person using public info. This step helps in assessing the overall risk profile based on financial stability and history.

Conclusion

Fraud prevention and customer trust can work together. By using technology to automate trust, carriers meet modern customer needs while maintaining care. Trust automation boosts customer loyalty and makes operations efficient. It is a valuable tool for the insurance industry. As the insurance world changes, carriers must use new solutions like trust automation to stay competitive. Building trust through technology helps carriers create lasting customer relationships and ensure long-term success. 

In summary, trust automation is not just about preventing fraud; it's about creating a more efficient and customer-centric insurance experience. Technology is reshaping the industry, and those who embrace these changes will lead the way in customer satisfaction and operational excellence.

Sponsored by ITL Partner: FRISS

Paul Carroll

From your perspective, how is the role of agents changing?

Chase Tarkenton

We're seeing a paradigm shift with the onset of AI. Automation has taken such a big leap forward that it’s freeing agents to focus on their clients and provide more thoughtful, more personalized interactions and focus on higher-value activities. The agent can provide a better experience, which is ultimately what the consumer is looking for.

For years, technology has been integral to supporting agents, but a lot of new use cases are coming up every month, where you can trust that the technology will execute and do so at scale and in a compliant fashion. It's really exciting to see.

Paul Carroll

If I'm an agent, how might my day look different now?

Chase Tarkenton

Automation is a big topic, so it can touch lots of different areas of the agent workflow.

With the onset of self-service, more and more customers are coming in already educated on different insurance policies. Now, when an agent is interacting with those clients, some of those conversations have already started, and maybe the agent has been provided a summary of how the customer has been interacting with the organization and what their situation is. By the time the agent speaks with someone, whether it's over the phone, over email, or through digital channels, the agent already has context, and they can pick up the discussion midstream.

At the end of the interactions, the paperwork that the agent has to fill out can be automated. Or a survey can be automatically sent to capture the client’s feedback.

Ultimately, customers can self-serve, and if they don't want to automate, there's that human in the loop. That human is the agent, who can have higher-quality interactions and more interactions on a daily basis than they could previously.

Paul Carroll

I’ve been an advocate for self-service for decades, but in my experience the handoffs to humans from the AI can sometimes be messy. How do you tackle those problems?

Chase Tarkenton

Obviously, the quality of the AI matters. So does the quality of the partner providing the AI to the agents and brokers. Unfortunately, there are a lot of technologies out there today that cannot deliver on some of the promises that are made.

Anyone considering a chatbot system should talk to reference customers and do a pilot to spot where problems might arise and to figure out in advance how to handle any issues.

Paul Carroll

I assume that this sort of technology, like most I've covered over the decades, has a progression to it. What are the sorts of interactions you started using AI with? And where are you now?

Chase Tarkenton

Twenty years or more ago, chat and voice virtual agents, as we call them, just handled basic FAQs [frequently asked questions]. What’s your phone number? How do I find a branch location? The agents were useful, but I wouldn’t say they were high value.

Fast forward from there, with the emergence of natural language understanding and the incorporation of machine learning and AI, many of these technologies can now handle more sophisticated use cases. Customers can fully resolve requests without having to call and speak to a human—though it’s an important design consideration to make sure they can reach a human if they want.

For example, look at first notice of loss. If your car breaks down on the side of the road, you could pick up your phone, call your insurance provider, and fully authenticate yourself through voice automation. They could leverage geo tracking to know where you are on the side of a busy highway and call a tow truck to come get you. They could capture information automatically and complete first notice of loss without any need to escalate or require a call center or an agent to support them.

That's a great use case that we're seeing come up again and again, and insurance organizations are executing on it. Their NPS [net promoter score] stays consistent with the NPS of a human interaction, which is what we want.

Paul Carroll

Where do you think you can get in two or three years?

Chase Tarkenton

Things are moving so fast that it’s hard to say what’s two or three years out in the AI space, but we’re clearly going to see generative capabilities produce some very exciting outcomes.

We're seeing models produced by some of the bigger generative providers that can reason and handle complex interactions. Open AI’s latest model does complex reasoning in a scientific setting. Those are breakthroughs that will allow for more intense use cases within insurance.

While we can all get excited about technology, though, what matters most is: What does the customer want, and are we doing everything we can to help them be successful? A close second is: How do we make the agent as successful as possible in their role? How do we make them efficient? How do we make them effective? How do we help them to scale?

I think using pre-trained models that are then trained on the data of the insurance organization, with generative capabilities blended in, can drive hyper personalization, which is what the industry is asking for.

Paul Carroll

Where do you see the breakdowns at this point? Where are people most likely to say, "Okay, I’m done with the chatbot. I want to speak to a human?"

Chase Tarkenton

In the design of an automated experience, there are a couple of things that are just a hard stop for consumers.

Number one, if the bot doesn't understand what you're saying, we have very low patience and will immediately drop out of those channels and want to speak to a human.

Number two: Sometimes a partial experience is not what consumers want. They're looking for full resolution. If I want to check what my premium is and can only do part of that before I’m escalated to a human, where I have to repeat myself, that's a non-starter, right?

Any time an interaction is being escalated to a human, it’s critical that the context and information already gathered is transferred to that agent in advance, so it's not being repeated.

Paul Carroll

Are there any issues that I should have gotten into that I didn't?

Chase Tarkenton

AI and automation are a huge topic. The only other areas that might be worth getting into are security and compliance.

Paul Carroll

Great question. Glad I thought of it. How do you handle security and compliance?

Chase Tarkenton

The good news is that the tools exist to mitigate hallucinations and to make sure that any AI-based agent will speak in a fully compliant fashion. If an agent or a customer asks the bot a question, it can quickly recognize whether responding would violate compliance.

Those tools are being used today. At Boost.ai, we’ve got hundreds of financial institutions, many in insurance, that are leveraging those tools to get the results they’re looking for: a better customer experience that they know is ironclad, protecting their reputation and their brand from a security standpoint.

Paul Carroll

Thanks, Chase.

Succession Planning for Agencies

Severe Weather Needs Innovative Insurance

About Chase Tarkenton

Chase Tarkenton is the SVP and general manager of boost.ai, North America. He’s focused on partnership growth, helping insurance firms leverage AI technology in personalized customer experiences.

While generative AI has been a phenomenon over the past couple of years, most uses have stayed pretty close to home — gathering information to make underwriters, adjusters, and agents more efficient, producing first drafts of reports or communications to clients, that sort of thing. 

But some recent articles suggest that AI may be getting ready to break out into much more sophisticated uses, including showing up as part of insurance products.

The article that most caught my eye was one in Fortune about how the CEO of Honeywell intends to use AI for competitive advantage. I had just finished another article in Fortune about a long list of impressive things that Honeywell was doing with generative AI at the operational level and was startled that the CEO belittled the effort. He said the work had to be done, because all his competitors were doing it, but said all the gains in efficiency would be competed away. To gain a sustainable advantage, he said, Honeywell needed to be bolder.

The CEO, Vimal Kapur, mentioned three areas where Honeywell is focusing, all of which strike me as being opportunities for the insurance industry, as well.

First, he talked about using AI to address his industry's talent gap. That issue sound familiar to anyone in insurance?

Kapur said, "If historically somebody said, ‘This job requires 12 years or 15 years experience,’ well, maybe you’re going to be able to have someone do it with seven years experience, and a supplementer [based on generative AI].... What’s a Plan B? There is no Plan B. There’s no humans to replace the humans who have left the workforce.”

He's talking mostly about engineers, so a different pool of talent than the insurance industry requires, but the issues strike me as very similar. AI can provide tremendous aid to help newer employees operate at more sophisticated levels.

Second, he talked about the area that intrigues me the most: actually adding AI to products, not just using it in the background. 

The article says: 

"He gives examples of supermarket checkout scanners, of which Honeywell is a major producer. Today, these scanners work well for bar-coded products. But if you get to the counter with an individual piece of fruit or vegetables where the bar-coded sticker hasn’t been applied or has fallen off, then the cashier must manually look up the price, or the customer is forced to go and weigh the product individually on a separate digital scale, often holding up the line. Kapur says that integrating cameras and computer vision directly into Honeywell’s checkout scanners would enable the scanner to recognize the item and charge the customer appropriately, without delaying the process."

Again, not an insurance example, but it illustrates the kind of thing that's possible for insurers, especially as more companies adopt a Predict & Prevent business model.

AI is already getting built into insurance offerings: the computer vision that monitors the road and the driver and offers warnings in real-time that can prevent accidents; the sensors that detect water leaks and alert homeowners before major damage can occur; Whisker Labs' Ting, which plugs into a wall socket and detects electrical anomalies and warns policyholders before a fire can start. 

But I can imagine a host of other opportunities, particularly in risk management. Who wouldn't like to have a smart adviser whispering in their ear that a risk is developing. Generative AI can be that adviser on issues as complex as cyber or as mundane as the approach of a hail storm, some crime trend, the need for a roof inspection, or certain home maintenance. 

You don't just sell a policy. You provide some continuing intelligence based on AI that is constantly learning.

If you do this right — delivering insights that are truly smart, that are useful, and that are delivered in the way that policyholders want to receive them — then you open up the sort of line of communication with customers that insurers have long craved. Insurance companies complain that they only interact infrequently and in cursory ways with most customers, when they make their monthly, semi-annual, or annual payments. Adding an AI channel would change all that. 

Finally, the article says another area where Kapur will use AI to Honeywell's advantage "is in providing engineering solutions to customers. In many cases, Honeywell doesn’t just sell an off-the-shelf product. It sells a system incorporating several of its products. These are usually built to a customer’s specifications, a task that requires a significant amount of time from the 5,000 engineers Honeywell employs for this work. 'We write the solution or spec for the project every time and we do tens of thousands of projects in our business every year,' he says.

"Kapur says Honeywell wants to build a large language model that can streamline this spec writing process, so that what currently takes as long as a month could be completed in just minutes—with engineers checking the output for perhaps a few additional days to guard against the risk of AI 'hallucinations.'” 

What he's describing sure sounds like it could be applied to the writing of complex insurance policies.

Another recent article tees up the possibilities of what's sometimes called agentic AI. which I think is a bit further out than the sorts of things Honeywell is pursuing but which could certainly be a profound advance for generative AI. The basic idea is that the AI wouldn't just have the authority to gather and work with information. The AI would also have the ability and authority to work with apps and have them execute tasks on behalf of the user. 

In other words, you wouldn't just tell the AI to gather tips on how to build a website. You'd tell the AI to build a website, and it would. 

The article's author, Bernard Marr, offers a range of areas where agentic AI could make a big difference. For instance:

"Business Operations: Agentic AI could revolutionize how businesses handle day-to-day operations. These AI agents could autonomously manage supply chains, optimize inventory levels, forecast demand, and even handle complex logistics planning. By processing vast amounts of data and making real-time decisions, they could significantly improve operational efficiency and reduce costs."

Or:

"Healthcare: Agentic AI could revolutionize patient care by serving as round-the-clock health assistants. These AI agents could engage with patients daily, monitoring their mental and physical health, adjusting treatment plans in real-time, and even providing personalized therapy support. By analyzing vast amounts of medical data, they could also predict potential health issues before they become serious, enabling truly proactive healthcare.

He also describes opportunities in software development, cybersecurity, human resources, scientific research, and finance. 

Again, I think agentic AI will take a while to take hold, just because a hallucination could lead to such big problems if the AI has the authority to act on its own. But the premise is certainly provocative and should be watched.

We're still in the very early innings in terms of what generative AI will do.

Cheers,

Paul

Let’s consider the $300 billion U.S. auto insurance industry. 

Driven by severe technician shortages, rising labor costs, repair complexities related to in-car technologies, and parts delays, the average cost of repair has more than doubled since 2019. 

Independent body shops are declining in number as VC-backed multi-shop operators (MSOs) proliferate. MSOs are raising rates and limiting their participation in insurer direct-repair networks. 

Capital formation in legal services and changing public sentiment on justice and fair settlement have increased the number of insurance claims going to litigation by 47% over the last three years. 

Medical costs show no signs of slowing, and new Medicare Secondary Payer rules will shift more costs to auto insurers moving forward.

These developments feel more structural than cyclical. The same can be said for the homeowners and broader commercial markets, where climate perils loom and new species of cyber losses (e.g., CrowdStrike) evolve as fast as technology itself.   

Insurance leaders are asking themselves the same questions they always have:

• Are our people working on the right things?
• Are our people working to the quality standard that’s needed?
• How fast are we cycling, operating, executing?
• How fast are we innovating? Moving with the market?
• What are our real costs related to all this?

What feels different these days is the urgency to find answers--concrete, quantifiable answers, in real time. Everyone has an opinion depending on their department, their patch. The goal is to generate a single source of operational truth based on how people are really working. 

A new crop of software has emerged generating valuable operational intelligence without having to master query languages, statistical modeling, or the command line. Offerings from the likes of Skan, Mimica, Fortress IQ, Soroco, Automat, and Arkifi require minimal involvement from the IT department to install and create zero technology dependencies.  

Installing this software on worker machines is way more philosophical and cultural than it is technical. The point to make is that it seems increasingly essential. As a baseball leadership team shunning player and positioning analytics operates at a disadvantage, so, too, an insurance leadership team.  

There are three modes of implementation with this stuff. The first is load, don’t tell. Most employment agreements permit this. The second mode is load and tell--that is, let workers know it’s there, period. The third mode is load and share. Again, the baseball comparison is apt. Real metrics ranking team members published in real time can be a powerful motivator.    

In the now, meaning the current quarter, the controllable input metrics generated by these tools are incredibly helpful driving efficiency and optimizing for, typically, cost. Here are examples in three key areas, working customer-back:

And then there’s enterprise AI. Most insurers are rightly reluctant to use open frontier models like ChatGPT and Anthropic’s Claude to do real work. Many carriers are moving in the direction of building custom models trained on their own data. Operational intelligence tools build a “digital twin” of the humans and systems they’re observing, useful for model training whenever it comes. Scaling laws state: the more data, the better the AI. 

When the board asks, “What are we doing in AI?”, “building a digital twin of our operation for eventual in-house proprietary AI training” is never a bad answer. Especially when that same data drives operational gains paying for its creation.  

Even as large language models (LLMs) have made their way into almost every new mainstream product, some industry sectors have been slow to adopt AI. Risk management is one of them. Fortunately, that is starting to change.

According to a 2023 Deloitte study, only 1.3% of insurance companies had invested in AI. But data from this year indicates a shift is underway. In Conning’s 2024 survey, 77% of respondents indicated that they are in some stage of adopting AI somewhere within their value chain. This may sound a bit nebulous — some stage, somewhere — but it represents a sizable jump from the 61% of respondents the prior year. Additionally, 67% of insurance companies disclosed they are piloting LLMs.

We also found evidence of change with our own survey, one specifically tailored to risk management. We polled 1,000 risk managers and almost half indicated that they expect to adopt generative AI (GenAI) tools within the next three years. These increases collectively speak to the potential for AI to affect our industry.

Before we look at where we could go with AI, let’s look at the challenges that have slowed adoption in risk management.

See also: Providing Coverage for AI May Be Huge Opportunity

What’s Holding Us Back?

Based on our years of work developing AI systems for insurance and researching the needs within organizations, we’ve identified a few key issues:

• At the organizational level: Change is hard, and many companies struggle with integrating AI technologies into their existing infrastructure. Risk management systems often involve complex legacy systems that are not easily compatible with outside tools. They also may not have a clear line of sight into exactly what they want AI to accomplish or how to measure it. Seamless integration requires a lot of forethought and the right partner to ease the transition and ensure that the system is doing what an organization wants or needs.
• At the IT level: The technical complexity of AI tools requires specialized knowledge and skills. In risk management, as with many sectors, there can be a shortage of in-house expertise to manage and leverage new technologies effectively. Companies need to carefully consider their hiring strategies to account for this or perhaps look into outsourcing options to achieve the best results.
• At the adjuster or claims rep level: One of the big impediments has been that many people still don’t know exactly what AI does, how it works, or what its limitations are. Couple this lack of knowledge with mainstream messages about privacy concerns or AI taking people’s jobs, and hesitancy is understandable. Comprehensive education and training programs would help overcome the hurdle in terms of grasping AI’s potential to make their work easier and more efficient while staying within regulatory guidelines. Workers will also become more comfortable as they increasingly use models like ChatGPT and Gemini in their personal lives. They will see that AI is not intended to replace humans but rather augment their capabilities, arming them with unprecedented insights they can use alongside their own experience and judgment for the best outcomes.

Why Do We Want AI?

With so many considerations, it raises the question: What exactly can AI do for risk management that is so great? A lot! So much, in fact, that the technology can no longer be ignored. Here are some of the biggest and most successful active use cases we see:

• Claims processing: AI is being used to automate tasks like reviewing medical records and legal documents, streamlining the claims process, and reducing errors. Not only is it making claims adjusters more efficient, but it is also removing the drudgery and manual processes of their work so they can concentrate on what matters most.
• Fraud detection: We all know fraud is a big problem in claims. AI algorithms can analyze historical data to identify patterns that spot potentially fraudulent items, alerting adjusters and triggering a human review. Identifying and eliminating fraud, especially early in the claims process, can save organizations millions of dollars each year.
• Reserving: On the flip side, some claims simply require more. AI systems can predict potential cost overruns for these complex cases. This allows risk managers to adjust reserves and prioritize cases accordingly so claims don’t run up any more or last any longer than necessary.
• Underwriting: AI can also be leveraged to analyze vast amounts of patient data — far more than a human could possibly consume — to create more accurate risk profiles almost instantly. With better risk profiles, costs go down. This leads to fairer and more competitive pricing for healthcare policies, which is, in turn, an enormous benefit to patients.
• Knowledge transfer: It’s no secret that risk managers and adjusters are aging out. There will be a massive talent drain when they retire, taking institutional knowledge with them. AI-powered tools can capture their expertise, preserving that invaluable knowledge for future generations.

See also: Cautionary Tales on AI

The Road Ahead

Embracing AI presents an opportunity to advance our industry through improved efficiency, reduced costs, and ultimately, better patient care. These outcomes are worth the effort spent to overcome challenges. The time is right to move the industry forward and to usher in a modern era of risk management.

The insurance sector has been looking for ways to fast-track claims processing to foster better customer experience and maintain revenues. Adopting technology like AI is not just a trend but a must.

See also: Insurance: An Industry Embracing AI

Let’s understand how AI streamlines each step-in claims processing:

1.  Empowering Decision Makers

With AI entering claim processing, the ability to evaluate claims has leveled up. AI can make judgments based on large historical datasets and flag fraudulent activities missed in manual investigations.

2.  Speeding Processing

AI-powered systems drastically speed up processes by automating most of the manual tasks. AI can tackle claims at initial levels and categorize them based on urgency. It can further escalate the conversations to the right authorities in real time. 

Let’s now explore some of the top advantages of claims automation:

1.  Smooth Data Analysis

Replacing manual processes with automation leads to higher employee morale. Automation addresses challenges like miscommunications between departments and other delays in claim resolutions. Automation reduces manual data entry, while processing and analyzing unstructured data.

2.  Customer Satisfaction  

Customer satisfaction and loyalty are closely tied to the speed and accuracy of claims processing. Automated workflows enable insurers to deliver faster resolutions, which translates to happier customers. 

For example, with automation, customers can access the real-time status of their claims via chatbots, while freeing agents to spend time on developing meaningful relationship. McKinsey has found that using the right technology can increase customer satisfaction by 20%. 

3. Compliance

AI can be used to spot potential problems that escape human scrutiny and enhance regulatory compliance.

See also: Cautionary Tales on AI

A Real World Example

A national healthcare insurance provider faced challenges in handling claim errors and corrections. Their system required human intervention for a few items. Subject matter experts (SMEs) reviewed emails, created correct copies, and resubmitted, requiring significant time and resources while adding complexity.

A claims automation solution saved over 1,300 monthly work hours for local claims and reduced processing time to 2.5 minutes per claim. For ITS claims, the system saved over 1,600 monthly work hours, with processing time cut to just one minute.

The robotic process automation (RPA) solution also enhanced accuracy by automating the error correction process.. It deployed bots to read and validate claim data, identify errors, and automatically correct them based on predefined business rules. The solution integrated seamlessly with the company’s existing claims management system.

Future Expansion 

As AI and automation evolve, their impact on the insurance industry is expected to grow.

1.  Advanced AI Models  

AI will likely lead to even more sophisticated models. These models will be capable of understanding nuanced contexts and handling complex claims, such as assessing damage from natural disasters or predicting long-term healthcare costs. 

2.  Integration With IoT and Telematics 

Insurers can gain insights into risk factors with real-time data collection. This allows for more accurate assessments and personalized policies.

3. Ethical AI and Transparency  

There will be a growing emphasis on ethical AI and transparency. Insurers must ensure their AI systems are fair, unbiased, and explainable. This will maintain trust with customers and regulators as AI becomes more integral.

4. Expansion of Automation Across Insurance Functions  

Beyond claims processing, automation will expand into underwriting, customer service, and policy administration. This holistic approach will enable insurers to create more efficient and customer-centric operations. 

AI technology is set to create endless opportunities in insurance.