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Sponsored by ITL Partner: Majesco

Underwriting is undergoing a major transformation thanks to new technologies like artificial intelligence (AI) and machine learning (ML). For decades, underwriters relied solely on historical data to assess risk and determine coverage. But in today's rapidly changing world, historical data is no longer enough.

To stay competitive, underwriters now need to leverage AI and ML to unlock deep insights from both structured and unstructured data. These technologies allow underwriters to identify risks earlier, price policies smarter, and operate more efficiently.

AI and ML are revolutionizing underwriting across three key areas: automating repetitive tasks, generating real-time insights, and evolving the underwriter's role. With intelligent automation, underwriters can reduce costs, improve customer satisfaction, and adapt to changes faster. The future underwriter will act as custodian, integrator, and collaborator to drive even greater value.

Underwriting Modernization

Insurance companies have been working to automate repetitive underwriting tasks for many years. But new technologies like robotic process automation (RPA), artificial intelligence (AI), and machine learning (ML) are taking underwriting automation to the next level.

RPA tools can be programmed to handle high-volume, rules-based underwriting workflows. This includes gathering data from multiple sources, filling out application forms, verifying information, and routing tasks. By automating these routine processes, RPA systems free up underwriters to focus on complex decision making, improving efficiency and allowing underwriters to process policies faster.

AI and ML inject intelligence into automation by enabling systems to learn, improve, and make complex choices over time. While AI extracts insights from text, ML detects patterns in data that humans might miss.

Used together, these technologies can replicate underwriter expertise at scale in numerous areas:

• Data Collection: Collect diverse data from both structured and unstructured sources to gain a comprehensive view of risk factors. By incorporating real-time data from multiple channels, underwriters can stay on top of emerging exposures.
• Insights: Generate insights from large volumes of data quickly using AI and machine learning. By detecting patterns and correlations, underwriters can better predict potential losses and model risks.
• Risk Selection: Make informed risk selection and pricing decisions tailored to specific cases using granular, AI-enhanced metrics. Automated risk rating incorporates more variables for more accurate coverage and premiums.
• Automating Workflows: Streamline routine tasks like application processing and renewals with conversational AI chatbots. Automating high-volume, repetitive workflows improves underwriter productivity and customer satisfaction.
• Behavioral Analysis: Identify potential misrepresentation and fraud earlier using AI-enabled behavioral analysis techniques. Advanced analytics improve underwriting accuracy and efficiency from the initial review stage.

By streamlining repetitive tasks with intelligent automation, underwriters gain capacity to focus on higher return tasks, operate more nimbly, and drive greater value.

The Exponential Underwriter

Advances in automation are changing the role of the underwriter. With the help of new technologies, underwriters are evolving into "exponential" professionals who can drive greater value. Deloitte calls this the rise of the exponential underwriter — “a multi-skilled professional leveraging new data sources and emerging technologies to be more efficient and proactive in defining future organizational processes.” Intelligent automation empowers underwriters to become “exponential”— adding more value than ever. 

Intelligent automation systems powered by AI and machine learning handle many of the routine, time-intensive underwriting processes including:

• Parsing insurance applications
• Assessing straightforward risks
• Screening applicant information
• Evaluating recommendations provided by underwriting software
• Gathering information from field staff
• Determining premium coverage

Adding AI not only changes the speed at which it’s done but changes the scale at which it’s managed. In fact, an Accenture study found that tech-enabled underwriters can perform higher value tasks and perform better. The exponential underwriter can:

• Complete complex risk assessments
• Evaluate ambiguous or borderline cases
• Make pricing decisions on non-standard policies
• Identify cross-selling opportunities
• Produce quicker turnaround on more standard quotes
• Access knowledge for risk-based decision making
• Rate and price risk better
• Retain existing policyholders 

The exponential underwriter will act as conductor of the underwriting orchestra — leveraging automation while focusing on high-impact areas to drive greater strategic value. AI will help underwriters predict and price risk more accurately, identify operational issues or opportunities in real time, and make more and better-informed decisions. Currently, only 30% of an underwriter’s time is spent assessing risk. The rest of the time is spent collecting, combining, and reviewing documentation for submission. New tech will free up their time enabling them to focus on high-value cases that need human insight and decision making.

The Future of Underwriting

Underwriting is undergoing a transformation driven by new technologies like automation, AI, and advanced analytics. While machines handle routine tasks, the underwriter's role will evolve.

Rather than focusing on specific policy transactions, future underwriters will be custodians of the entire underwriting process. They will oversee and optimize systems that integrate data, analytics, and software recommendations to produce accurate quotes at scale.

Underwriters will become integrated P&L professionals — using technology to underwrite policies in a way that balances risk and revenue judiciously. They will collaborate with systems and tools that enhance their capabilities.

This new, exponential underwriter will complete policies quickly, accurately, and cost-effectively by conducting sophisticated risk assessments augmented by automation. Underwriting will remain human-centered but enhanced by technology.

By embracing the possibilities of future tech, underwriters can focus on high-value analysis while systems automate the rest — driving growth, mitigating risk, and exceeding customer expectations.

Contact us if you would like to learn more about how intelligent automation can drive underwriting transformation. Download the ebook - Beyond the Bot: How AI Is Ushering in the Next Wave of Automation for more in-depth insights on the future of automation

Murray Izenwasser, Senior Vice President, Digital Strategy

At OZ, Murray plays a pivotal role in understanding our clients’ businesses and then determining the best strategies and customer experiences to drive their business forward using real-world digital, marketing, and technology tools. Prior to OZ, Murray held senior positions at some of the world’s largest digital agencies, including Razorfish and Sapient, and co-founded and ran a successful digital engagement and technology agency for 7 years.

 

 

Sponsored by ITL Partner: OZ Digital Consulting

Engaging millennial customers has been a strategic priority for insurers. With most millennials in the prime of their careers, they are increasingly focusing on long-term financial goals, health coverage, auto insurance, asset protection and so on. 

As per Statista, millennials constitute the largest generation group in the US with a 72.24 million population - a vast market that insurers cannot ignore.

But millennials come with certain demands. 

• They prefer personalized and customized solutions and products and a transparent approach to guiding them through these solutions
• They are increasingly comfortable using digital platforms and enjoy the ease and convenience it delivers
• They demand quick and seamless services

These needs are driving the transformation of the insurance industry today - with carriers deeply invested in getting their customer experience right and striking the right note with their target demographics. 

While they build omni-channel experiences and seamless hassle-free interactions, they are sinking deep in another crisis:

Attrition of workforce. 

83% of agents quit within the first three years! And with baby boomers retiring; the average age of an agent in the United States is 59. The insurance sector is in the race to find the right talent from the millennial and Gen Z population to steer its course in the coming decade. But it is easier said than done. According to Zippia, the insurance industry turnover for 2021 is 26%. And as Insurance Business magazine says, the industry is unable to hire fast enough to replace the people leaving. 

The reasons for attrition are many: compensation and benefits, work-life balance, the perception of insurance as a traditional, unchanging sector, the lure of attractive tech jobs and more. As insurance carriers rebuild their traditional image and rework their compensation and benefit programs, here are a few gaps they could focus on closing, in parallel. 

• Your workforce is millennial too. The newer agents that are being hired are from the same demographic. So focusing on customer experience is just winning half the battle. Insurers should revamp workforce experience as well. After using intuitive apps and platforms for day-to-day chores like ordering groceries, or books, or laundry pick up - using legacy apps and multiple platforms at work becomes a deterrent. They expect the same ease of use and seamless workflows and processes at work as well. Insurers, if not already, should invest in tech capabilities that can speed up day-to-day operations for their teams.
• Tech can play a role in work-life balance. With aggressive targets and deadlines, agents feel burned out. According to an article in the Independent Agent magazine, the second largest reason for agents to quit the industry is because of poor work-life balance. Tech can be a great enabler in streamlining the day-to-day activities for an agent. For example,
  • Giving them access to mobile-first applications so they don't need to jot down notes in a diary and come back to the office desktop to update it
  • Leveraging tools that can automatically capture their activities, take notes, set up meetings faster and allow them to share information at the click of a button
  • Nudge them on important dates, meetings, reminders etc. so they are more efficient through the day

Building a tech ecosystem that can remove laborious tasks and make them doubly productive in an eight-hour workday can help them check off items on their tasklist and get back to other priorities. 

• A learning environment builds career paths. As younger workforce steps into the industry, insurance carriers must build robust onboarding programs to help build the skills and know-how to be successful insurance sellers. Beyond onboarding, they must build an active learning environment to help agents unlock their potential and grow in their roles. As the tenured agents retire, it is important to capture their experience and best practices to cascade to the newer teams. Establishing coaching networks that pick up best practices and best next steps from the star performers in the team and share with the larger teams can proliferate best selling practices. 

As the insurance industry ushers in a decade of digital transformation, reinvention and sharper customer focus, it becomes equally important to focus on the internal stakeholders and give them a roadmap for growth. A mix of healthy organization culture, compensation, well-rounded benefit programs and the right technology support can pave the way for the ‘great retention’.

 

Sponsored by ITL Partner: Vymo

The adoption of digital technologies is driving a remarkable transformation in the auto insurance industry, which has long been associated with complex paperwork and lengthy processes. One example is the advent of digital vehicle titling to streamline insurance claims management for total losses and unrecovered thefts, offering convenience and efficiency while providing substantial economic benefits. This new digital solution, based on blockchain, is revolutionizing the industry and benefiting policyholders and insurers alike.

Same-Day Service

In an era of instant gratification, policyholders expect same-day service. Digital titling delivers just that. By automating manual processes and reducing reliance on paperwork, a digital titling platform enables insurance providers to process total loss claims and obtain sellable titles within a day, an amazing improvement from the previous standard of 60 days. Claims documents are delivered promptly, enabling policyholders to make informed decisions and stay up to date during the stressful occasions of a total loss or theft. This increased efficiency and responsiveness enhances customer satisfaction and strengthens insurer-policyholder relationships.

Without the conveniences of digital titling, insured parties are subject to the “time tax,” a phrase coined by the CEO of Champ Titles, Shane Bigelow, to describe the massive time commitment of securing a title with physical paperwork and a DMV visit.

“We need to reduce the time tax that each consumer pays as they wait in line to process paper and deal with DMVs," Bigelow says. "Because each state has unique systems, laws and procedures, a solution that can work with legacy systems but provide a powerful, flexible and, most important, digital system of record is needed.”

Advantages for Insurers

While policyholders benefit significantly from digital titling, insurers win, too. That is because digital titling isn’t just about a digital title but rather about the full digitization of the titling process, which has always been paper-ridden and slow. Costs associated with printing, mailing and physical document storage are eliminated. Automation of processes minimizes manual labor requirements, resulting in operational efficiency and reduced administrative expenses. By removing redundant tasks and streamlining workflows, insurers can reallocate resources to enhance customer service and improve innovation. As a result of carriers getting paid faster, insurance rates decrease. All parties benefit.

In addition to cost savings, insurers can encourage environmentally responsible practices by embracing digital vehicle titling. Currently, each state's motor vehicle department uses an average of 15 million sheets of paper annually for this process. Going digital not only eliminates paper usage but also streamlines end-of-life vehicle processing. Traditional titling often leads to prolonged storage of totaled cars, which may contain hazardous materials like engine fluids and battery acid. Leakage of these substances poses significant threats to ecosystems and groundwater. However, with an expedited digital titling process, carriers can recycle vehicles on the same day, reducing environmental risks and supporting material reuse.

West Virginia Clearinghouse

The launch of West Virginia’s National Digital Titling Clearinghouse on July 1, 2023, is perhaps the biggest leap in vehicle titling in over 20 years. This innovative concept, born from the West Virginia DMV, with technology created by Champ Titles and payments facilitated by Tyler Technologies, acts as a central hub for national entities like insurers and online car retailers to digitally acquire, store and transfer out-of-state vehicle titles—safely, accurately and efficiently from anywhere in the country.

West Virginia Gov. Jim Justice said, "I am excited about this initiative, which will bring millions of dollars to the state of West Virginia and position us as a leader in innovation, as we have been so many times before. I’m proud that West Virginia is the first state that’s setting the template for others to follow. My thanks go out to the legislature and to all those involved at the DMV for making this a reality.”

This groundbreaking platform not only establishes West Virginia as a pioneer in vehicle titling and registration solutions but also serves as a model for other states seeking to streamline their titling processes. By embracing this system, other states can unlock the benefits of improved efficiency, cost reduction and enhanced accessibility.

As digital titling continues to evolve and reshape the insurance industry, both policyholders and insurers can look forward to a future that is more efficient, customer-centric and economically beneficial. These advancements in digital titling are ushering in a new era of streamlined motor vehicle operations and enhanced experiences for everyone involved.

 

Dr. John Sviokla is co-founder of GAI Insights. He previously was a strategic adviser at Manifold and former senior partner and chief marketing officer of PWC. He has almost 30 years of experience researching, writing and speaking about digital transformation — making it a reality in companies large and small. He has more than 100 publications in many journals, including Sloan Management Review, WSJ and the Financial Times.

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Insurance Thought Leadership:

You just put on a big event on generative AI. You also co-wrote a recent article in Harvard Business Review on how businesses should think about it. Could you start us off with an overview of how you’re seeing the key issues?

John Sviokla:

If a big part of your cost base depends on manipulating what I call WINS – words, images, numbers and sounds – your whole business is going to change.

And critical functions will change for lots of other businesses, too. Drug discovery is going to be completely different. IBM and Moderna can look at four orders of magnitude more molecules by using generative AI as opposed to a traditional computational model; instead of 100,000 molecules, they’re now looking at more than a billion. For some companies, marketing will change drastically even if the majority of their cost base is not WINS work.

Every meeting is going to completely change. For one thing, all the meeting notes will be captured automatically, as will all the to do’s and the follow-ups, and the synopsis will be generated by the AI.

We’ve reached a threshold. We haven’t completely passed the Turing test, but we’re pretty close. [Posed by British mathematician and computer scientist Alan Turing in 1950, the Turing test concerned whether a human could tell whether they were conversing with a computer or another person. If the person couldn’t tell the difference, the machine was said to have passed the test.] And everyone can talk to a generative AI, almost no matter what language they speak.

Insurance Thought Leadership:

AI always had a brain, but now it has a mouth, too. Eyes and ears, as well.

Sviokla:

In addition, every person now becomes a team, with the AI helping. And that’s huge.

I think we’re at a Henry Ford moment. A lot of people talk about his assembly line, but they forget Fred Taylor, whose knowledge management was the first large language model for business, because he broke all processes down into their tiny parts so they could then be put back together in the most efficient way. If you look at what Taylorism did, what Fordism did, it took the unit cost of everything way, way down and pushed quality way up. Generative AI will do the same.

Ford also doubled the going wage, which I think is a lesson for today’s businesses as they reap the benefits of generative AI. Surplus can go only three places: to the customer, to the investor or to the labor. I would encourage people, if they want to be more than one-and-done, to provide a lot of the surplus to labor.

Insurance Thought Leadership:

In terms of effects on insurance, I can certainly imagine the sort of thing you're talking about in, say, underwriting or claims. You're taking in all this information, and you're using the AI, and you're getting smarter. You’re also on a steep learning curve and are getting smarter about how to get smarter. Is that how you’re thinking about this?

Sviokla:

Well, yes. If you look at insurance companies, some are heavy on customer interaction and on indirect cost. All that stuff gets completely reengineered. Completely.

The chatbots we have now are lobotomized. The new chatbots have a brain. And the filtering will happen up front. You won’t have to go through a phone tree and get routed to a human when the system fails. The AI will know up-front that something is too complicated for a machine and will route the call to a human expert.

Someone at our event used the new technology to greatly speed up the handling of calls in a call center. There aren’t any third-level service reps any more. It’s all down at the first level now. Customers are happier, and he got a return on his investment in a month.

I know someone at a major health insurer that has already deployed over a dozen large language models doing things like customer support and regulatory compliance.

Then you've got all of what today is dark data: data on what people look up in FAQs, the questions they ask about how something works or what a policy means and so on. All that data will be analyzed by the AI and turned into insights that can be used throughout an organization.

You also have folks like Tomorrow.io, which is launching a new set of satellites that have fine-grained operational weather data. They're down at the fleet level, saying, Don't roll those trucks in North Texas for the next two hours because you're going to have flash floods. That kind of stuff.

AI, in general, and generative AI, in particular, enhance both sides of the Law of Computability [a formulation of John’s that he describes at length here]. The AI increases your ability to digitize through pattern matching and machine learning. It also increases your understanding of whatever you’re analyzing. The effect is like the Law of Accelerating Returns.

And what’s going to happen in health insurance when you can take the continuous analog data on my heart and combine it with data from my medical record along with the image data from my MRI? What the hell are we going to find? I have no idea. But I bet we find some cool stuff.

A lot of knowledge work is like discrete manufacturing, but you’re going to have a continuous flow. It’s like the switch to Ford’s River Rouge. Ford had to redesign everything, and we will, too.

Insurance Thought Leadership:

If you're running a big insurance company these days, how do you get started? I mean, this has to be kind of intimidating for people who are not technologists. They sort of know they need to do something, but what do they do?

Sviokla:

Scientists have a term, “inattentive blindness” [defined as “occurring when an individual fails to perceive an unexpected stimulus in plain sight, purely as a result of a lack of attention.”] It’s like what happens in the famous gorilla video. [Viewers are asked to count how many times people in white shirts, as opposed to black shirts, in a group pass a basketball to each other, and 80% fail to notice that someone in a gorilla costume walks into the scene, stops in the middle of the group to look at the camera, beats his chest and slowly walks off. If you haven’t seen it, you can find it at www.theinvisiblegorilla.com.] What drives inattentive blindness is high pressure, lots on your mind, basically overcrowding of your head. That's pretty much the definition of an executive.

So the first thing is every senior executive or every executive in the company needs to spend five hours personally using the stuff.

Then you need to expand your network, because your innovation space is really constrained by who you interact with. You do quarterly updates to make sure you’re on track. You fund the rebels to make sure you’re exploring all your options. You do future back planning – you figure out where you want to be in five years, or whatever the time frame, then ask yourself what you need to do today to create the opportunities that will get you there.

You start with customer service, where you’re guaranteed to get an ROI within a year. Then you take the surplus and put it into your talent. You want to become a talent magnet because whoever becomes a talent magnet will have a massive advantage. It’ll be hard for people to catch up if you reinvest in the talent and the data. Your unit costs will just keep getting so much better. It’ll be like Ford in the early days. Nobody can catch me. It’ll be like Toyota more recently. They walked people through the factory and showed what they were doing, but other companies still couldn’t do what Toyota did because they didn’t have the organizational capability.

Because insurance is largely a WINS business, those that start early and keep investing in their people will be on a different learning curve, while others simply won’t progress as fast. And the scale effects here are enormous.

If you can get your organization to constantly be in dialogue with the machine to improve, that will enable a new management approach. People just aren't used to doing that. But your organizational systems will know how to learn faster than the competition, and you can amplify the living daylights out of your learning capability.

Insurance Thought Leadership:

Any parting thoughts?

Sviokla:

We’ve all seen the returns that technology breakthroughs can generate. As of three weeks ago, Nvidia, Microsoft, Apple, Amazon, Meta and Google/Alphabet had a combined market capitalization of $10.1 trillion. If you combine Japan, the world's third-largest economy, and Germany, the fourth largest, that's GDP of $8.4 trillion.

But I think generative AI is bigger than the internet. The internet lowered transaction costs and facilitated a lot of innovation, but we’re now reapportioning work between machines and humans in a fundamental way and can rethink all our management processes.

Somehow, despite a long career as a writer and editor, I never got around to reading Bill Bryson's masterful book, "The Mother Tongue: English and How It Got That Way," for, oh, more than 30 years. But I picked it up recently, and it fleshed out my understanding of and appreciation for English in all kinds of ways. 

While I knew, for instance, that England's rulers spoke a form of French after William the Conqueror crossed the English Channel from Normandy and dispatched King Harold at Hastings in 1066, I didn't realize that the rulers spoke a language different than the common people for more than three centuries, until almost 1400. With no rules about English being handed down from on high, the language developed in a sort of free-for-all of decisions about vocabulary, spelling, conjugation of verbs, etc. in pockets all over the country. Then, just 50 years or so later, in the mid-1400s, the printing press acquired movable type, publishing took off -- and there needed to be rules. 

In the crush to standardize on a language that could be printed and understood by masses of people, a haphazard approach to rules led to the mishmash of spellings and pronunciations that are tough enough for us native speakers but bedevil the many who learn English as a second or third language. As just one example: "ache" took its pronunciation from a region where it was spelled "ake" but kept the spelling from an area where the "ch" was pronounced as in Charlie.

Ruminating about the origins and development of English got me thinking about the language we use in insurance. While we talk a good game about being customer-centric, our language says otherwise. 

If we really want to put the customer at the center of everything we do, we have to start by giving up on two words: "adjuster" and "losses."

This isn't a new theme for me. I wrote this in 2015, this in 2019 and this in 2021. But the issue is so important that it's worth revisiting from time to time... until we get it right.

While there's plenty of room to complain, in general, about insurance jargon -- and I've done my share of griping -- the core of the language issue boils down to those two words, "adjuster" and "losses," because they send exactly the wrong signal to customers.

Sending an "adjuster" to process a claim tells the customer we don't trust them. Referring to payments to customers as "losses" tells them we're going to try to minimize those payments as much as possible, even though the promise of those payments is why customers hire us in the first place. 

As I wrote in 2019:

"If I'm filing a claim, I don't want it adjusted. I want it paid. Yes, I realize that processing claims is complicated and that all sorts of adjustments need to be made. I also realize that no industry simply pays when a claim is made against a company. But if you send me an 'adjuster,' you're telling me right off the bat that you don't trust me, and that's a lousy way to start an interaction. It certainly isn't any way to start a relationship, which is what insurers insist they want with customers these days. Don't trust me, if you must, but send me a 'claims professional' or simply a 'customer service representative.' Don't send me an 'adjuster.'"

The switch to a term like "customer agent" just doesn't seem that hard. Yes, the term "adjuster" has a long history, but we're still allowed to move past it, just as we've moved beyond the Middle English that prevailed in the 1400s.

To quote from my 2019 self again, this time about "losses":

"Almost as bad is 'losses,' as in 'cat losses' or 'medical losses.' How about, instead: 'payments to highly valued customers in their time of need, after years of premium payments on their part'? Does Amazon record a loss when it ships me something? Of course not. And those payments on health or cat insurance aren't losses, either; they're just the cost of doing business—people don't pay those premiums simply because they like us. So, let's look at our business through the customer's eyes and book 'payments' or somesuch, not 'losses.'"

And from 2021:

"When a bank or mutual fund sends me money I've earned, it's paying me interest or capital gains. Corporations pay me dividends. None of these firms talk about losses just because money has moved from them to me. So, why does the insurance industry refer to a payment on my behalf to a doctor as a 'medical loss'? Why is a payment to help me recover from property damage in a storm a 'catastrophe loss'?... Surely 'claims' or 'paid claims' could replace 'losses.'"

Changing the term "losses" will be tougher because it's used by accountants, who have their own rules and are loath to change. The term is also less of an affront to customers than "adjuster," because the industry mostly just talks about losses when it's talking to itself or to investors. Still, no justified payment to a client should be treated as a loss -- not if we're serious about looking out for the wellbeing of our customers. 

I realize that old habits die hard, but I'm going to keep trying -- and I hope you will, too. If we can change the way we talk about our interactions with customers, we'll be much more likely to improve the interactions themselves.

Cheers,

Paul

KEY TAKEAWAY:

--The 2023 aggregated combined ratio for personal auto insurance is expected to be 106%, but, done right, a telematics program can lower that combined ratio as much as seven percentage points on the entire auto insurance portfolio. Research shows: 

• three-point improvement at a portfolio level, achievable within months, via a structured behavioral change program;
• three-point improvement based on improved claims management and increased self-service;
• 0.5 to one-point improvement based on retention — telematics for auto insurance programs can help increase retention by about 20% compared with traditional portfolios.

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The U.S. personal auto insurance sector lost billions in 2022, but the right telematics auto insurance strategy can quickly bring a five-percentage-point improvement to your loss ratio.

Due to inflation, driver distraction and slow approval of rate changes, the 2023 aggregated combined ratio for personal auto insurance is expected to be 106%, according to AM Best’s David Blades. To rectify this sorry state, insurers must embrace every opportunity to address this disequilibrium in the personal auto segment, starting with telematics.

Telematics for auto insurance and UBI (usage-based insurance) are potentially industry-changing technologies. But the truth is their impact on profitability has been negligible. In 2022, the U.S. personal auto insurance sector lost billions of dollars and generated a historically bad 112% combined ratio.

While large personal auto insurers have adopted telematics-based programs, they’re only scratching the surface of the potential benefits. Total market penetration of telematics for auto insurance is a fraction of the risks insured.

While much has changed, there’s more that insurers can do to increase adoption. By converting current customers to telematics for auto insurance programs, insurers can carve a fast path to sub-100 combined ratios.

Telematics for Auto Insurance Fills the Current UBI Business Model Gaps

The research shows that most people like mobile-based insurance telematics. When presented with app-based telematics for auto insurance program that provide rewards for safe driving, emergency/crash assistance and other services beyond monitoring driving behavior, more than 50% of U.S. respondents in a recent Swiss Re and IoT Observatory survey (10,000 worldwide/2,000 U.S.-based) said they would recommend the program to a friend.

By many measures, insurance telematics is a success in the U.S. market. According to TransUnion’s “Insurance Trends and 2023 Outlook Report,” the number of people who opted into a telematics program for auto insurance now sits at 60% of those presented with an offer. Some insurers, such as Progressive and Allstate, have achieved UBI penetration rates of 40% to 50% in certain channels.

But even with the increase in adoption experienced by these large insurers, telematics for auto insurance is not pervasive in insurance books.

The IoT Insurance Observatory, an insurance think tank that has aggregated data from more than 120 insurers, reinsurers and tech players over its seven annual editions, estimated that about 9 million UBI insurance policies transmitted some telematics for auto insurance data to an insurer in the U.S. in 2021, which translates to total market penetration of about 5% for telematics. Given the amount of time that telematics has been in the market, this penetration rate is very low.

Telematics for Auto Insurance, Success and Switch-and-Save

The current telematics for auto insurance business model is one reason penetration has lagged. Insurers have mainly used UBI products, accompanied by the promise of steeply discounted premiums, to lure customers from competitors. This typically happens at renewal, when policyholders are inclined to compare quotes.

However, only a relatively small portion of people — slightly above 10% — switch carriers at renewal, even when confronted with an increase in premium. In fact, a recent survey by Swiss Re and the IoT Insurance Observatory found that almost half of U.S. policyholder respondents said they’ve been with the same insurer for more than five years.

See also: Video Telematics Transforms Road Safety

A New Telematics Business Model: Value-Added Services for Current Policyholders

Given that inertia, insurers should begin offering telematics to current policyholders, wherever they are in the policy lifecycle.

European insurers, including Fidelidade in Portugal and Generali in Germany, have already successfully introduced mobile-based telematics for auto insurance programs focused on services — that have no impact on insurance premiums — and are offered to everyone in their auto portfolios.

Ohio-based Progressive is following the same path. In a February 2023 earnings call, Progressive sent a clear message to the U.S. insurance industry that it would do the same.

“Over the last couple of years, we’ve experimented with offering a service to detect and respond to major accidents to some of our Snapshot customers to learn if they value the service and to better understand how it could be useful in handling claims,” said Tricia Griffith, Progressive’s chief executive officer.

“[T]here’s a large segment of customers who don’t want their insurance premium to be based on their driving data. That means that if we limit this just to our Snapshot customers, we’d be leaving out a lot of others. So, in March, we plan to start making accident response available to all of our auto customers, not just those who are in Snapshot,” Griffith said.

All Progressive’s policyholders have the telematics for auto insurance services constantly available within the app, regardless of the auto insurance product chosen.

To better understand which services would offer the most value to drivers, the IoT Insurance Observatory and Swiss Re asked survey respondents to pick the services they’d like to include in the app. The three things that U.S. customers want most are rewards, automatic emergency assistance in the event of a crash and anti-theft support. Driver conveniences, such as weather information, car location finder, car maintenance reminders and claims support are also of interest.

Providing these services to all of the customers in the current portfolio is a clear and addressable opportunity. There even examples of tech players, such as Bouncie in the U.S., that are scaling based on telematics for auto insurance services sold to drivers. Insurers such as Discovery Insure sell participation in its UBI program – which includes rewards like vouchers and other perks for safe driving – for the equivalent of $5 a month.

The Urgent Need to Expand Telematics for Auto Insurance Uptake

There is much evidence that frequent, tangible and inexpensive rewards can improve driving behaviors, help drivers avoid accidents and reduce loss ratios. Within the first 30 days of joining Vitality Drive, an incentives-based driver-behavior program from South Africa’s Discovery Insurance, drivers have a 15% improvement in driving behavior on average, according to company research.

Some insurers outside the U.S. have also used telematics for auto insurance data in their processes and achieved benefits such as reduced average claim costs and litigation, increased fraud detection, faster claims processing and improved customer satisfaction.

Moreover, insurers with hundreds of thousands of mobile-based telematics for auto insurance policyholders experienced an increase in customer retention and a decrease in cost for customer service due to an increase in self-service within the app.

Research by the IoT Insurance Observatory, which has covered more than 80% of the telematics in auto insurance policies worldwide for the past six years, shows that well-executed mobile-based telematics for auto insurance programs have a large and positive impact on combined ratios, and estimated the following as achievable impacts on a U.S. auto insurance portfolio:

• three-point improvement at a portfolio level, achievable within months, via a structured behavioral change program;
• three-point improvement based on improved claims management and increased self-service;
• 0.5 to one-point improvement based on retention — telematics for auto insurance programs can help increase retention by about 20% compared with traditional portfolios

This means that the potential benefit to insurers is as much as a seven-percentage-point reduction on the combined ratio of the entire auto portfolio.

See also: Insurtech Startups Are Doing It Again!

Making the New Business Case Work

Two main challenges that insurers face when expanding their telematics for auto insurance program are cost effectiveness and the IT architecture needed to support continuous monitoring of all their portfolios.

Cost Effectiveness

The benefits described above require a set of digital tools and a structured behavioral change program with rewards. The cost of this new approach includes the telematics for auto insurance software development kit (SDK) inside the insurer’s app for constant monitoring and the IT architecture to manage the data and the reward mechanism – estimated by the IoT Insurance Observatory to be about two percentage points on the insurance premiums.

Adding the telematics for auto insurance SDK into the app of all the policyholders – whichever auto insurance contract they signed – is the best way to quickly unlock the telematics benefits at the portfolio level, which will significantly improve the insurer’s bottom line.

An insurer with millions of policyholders and a strong brand can reach relevant efficiencies and help lower this cost by cutting good deals with retailers for their reward system.

IT Architecture

The goal of telematics for auto insurance architecture is to build real-time/near-real-time data ingestion and data processing pipelines to process data from IoT devices into the big data analytics platform for the insurers.

The architecture provides a well-defined data flow process, optimized storage and processing components and consumption workloads for telematics for auto insurance data. This architecture does not aim to ingest data into the core insurance platforms. Rather, it integrates auto insurance data with data from core insurance platforms in a “model and store” data storage for analysis and consumption.

The diagram below depicts the data flow and components that drive the architecture for telematics for auto insurance. The goals of this architecture include:

1. Supporting and collecting high-throughput, real-time streaming data from telematics for auto insurance service providers
2. The ability to capture data from millions of devices and vehicles on a real-time/near-real-time basis for the entire life of the insurance coverage
3. Capabilities to perform analytics on the telematics for auto insurance data to make inferences on driver behavior, promote driver safety, provide incentives, encourage customer retention, etc.
4. Provide comprehensive and scalable system-to-system integration capabilities for the telematics for auto insurance data and downstream enterprise applications.

This cloud-based, scalable and robust architecture enables data-driven insights based on telematics for auto insurance data provided by insured vehicles. This architecture strives to support applications that enable business use cases for telematics for auto insurance while providing insights into drivers, vehicles and critical incident response. This will be a foundation for developing insurers’ intellectual property about auto risks in the future.

The key components of the architecture include:

• Vehicle: The vehicle generates and transmits telematics for auto insurance data on a real-time basis, which is stored and managed in a telematics server for real-time data access. The telematics for the auto insurance provider enables transmission of data from their servers to the cloud/MFT infrastructure of the insurer for data processing.
• Data Collection and Ingestion: Telematics for auto insurance data is collected and ingested on a real-time basis using cloud-native services and stored as raw files in the cloud. This enables the collection and staging of raw transactional data for pre-processing. Data access to the raw layer will be enabled in this region.
• Processing and Transformation: Telematics for auto insurance data is enriched, de-duplicated and processed, and key data attributes are selected for transformation to enable modeling and storage of data. This stage enables the right attributes to be selected for use cases to be implemented.
• Modeling and Storing: This critical step ensures that the telematics for auto insurance data is integrated with other critical insurance data elements. These now can include data from core insurance systems for policy, claims and billing, etc., as well as data from external partners, such as MGAs and TPAs, and third-party data, such as geospatial and demographics. The goal is to provide a comprehensive view of data attributes across multiple domains and enable seamless data consumption for visualization, data analytics, system-to-system integration and enterprise applications.
• Analysis and Consumption: This step provides the consumption workloads, which are based on business use cases for telematics for auto insurance. If an insurer offers UBI, this layer handles consumption patterns for enterprise applications. It analyzes correlations with expected claims to define risk models and informs actions on the UBI portfolio. This ensures the applications get the right and accurate telematics for auto insurance data for unlocking the different use cases along the insurance functions.

See also: 4 Ways Telematics Is Improving Car Safety

The current architecture differs from previous telematics architectural patterns significantly, as the use cases for telematics for auto insurance are continuously evolving to meet various business needs. In addition to classic use cases such as UBI and understanding driver behavior, insurers are continuously providing additional services to customers, such as rewards for safe driving behavior, accident response, claims handling, maintenance alerts and mechanical failure diagnostics as part of their telematics for auto insurance offerings. Hence, the data infrastructure needs to be significantly scaled up to meet these needs.

ValueMomentum is seeing customers increase their telematics investments by 30% to 50% to consume, process, store and analyze telematics for auto insurance data. This evolution fully exploits IoT’s potential for the auto insurance business: to continuously sense, understand, learn and act in near-real time. This is a one-time investment to build tech capabilities, and insurers then move into Run-the-Business (RTB) mode after they are implemented.

This architecture helps insurers with two critical business goals:

1. As telematics vendors move toward providing real-time streaming data, insurers need this architecture to consume and process the real-time data and to extract valuable insights for the different insurance functions. In addition, this architecture helps insurers scale. For instance, if an insurer wants to expand telematics to its personal and commercial lines portfolio, implementing a cloud-based scalable infrastructure is critical to a successful expansion.
2. Providing flexibility on how long the telematics data is stored for a customer. The customer’s preference is to have all telematics data erased when they exit the program. Having a modeled storage layer makes it reliable and easy to identify and remove the raw customer data as per business needs, without missing the intellectual property developed (e.g., risk models).

Expanding Telematics Is Urgent to the Current State of U.S. Personal Auto Insurance

Even after accounting for the cost of setting up the new telematics for auto insurance model, there’s still five percentage points of combined ratio benefit to insurers, representing an extremely precious U-turn opportunity.

Given that insurers obtained on average 1.8% profits over the past 10 years in the U.S. — according to the January 2023 “Report on Profitability by Line by State in 2021,” by the National Association of Insurance Commissioners — offering telematics for auto insurance to policyholders in this new, value-added model can increase profitability by driving down combined ratios.

Even a carrier at the early stages of its digital journey, let’s say with a take-up of their auto app on only one-third of the portfolio, will find the potential profits significant. Moreover, this would be obtained without asking a penny more from the policyholders, providing instead the services customers are asking for.

The technology for telematics in auto insurance is mature, and drivers clearly see benefits in additional services. Insurers with the vision, the will and the skills to act and bring change to telematics for auto insurance in insurance will reap the benefits that come with an expanded market.

Property reinsurance rates are at their highest in 17 years. Factors such as shifting weather patterns, record losses and economic uncertainty have damaged reinsurer confidence, leading to steep mid-year rate increases. To unlock future growth and earn reinsurer trust, insurers must: leverage innovative technology to manage evolving risks and adopt a philosophy of radical transparency. Those that don’t will be left behind.

Innovation: An Urgent Opportunity

Reinsurers want to have full confidence in the risks they underwrite. However, with increased losses and rising replacement costs, they have no choice but to be more selective. To bolster confidence, primary carriers must prove their ability to adapt amid a challenging risk climate. The first step? Master new technologies quickly.

The rapid advancement of technology, such as recent developments in generative artificial intelligence (AI), is transforming every industry, including insurance. A 2022 report from Accenture found that 65% of claims executives plan to spend more than $10 million on AI, and 80% believe these technologies offer value. Munich Re’s Patrick Greene says in a recent Reinsurance News article that reinsurers hold this view. Greene emphasized the importance for insurers to integrate AI immediately for efficient claims and underwriting processes. He also stressed that these technologies will soon not be optional for carriers – at least, not if they want to obtain reinsurance. 

Still, insurance tends to trail other industries in embracing new technology. While the cost of many AI solutions has decreased and the number of insurers reporting financial benefits from AI has increased from 10% to 20%, the same Accenture report found that fewer than half of those polled said their organization was advanced in automation.

Amid this technological shift, insurers can distinguish themselves from their more conservative competitors. By enthusiastically adopting AI, machine learning (ML) and computer vision (CV), carriers can prove to reinsurers they are forward-thinking and adaptable. Many insurers are already doing exactly this to:

• Accelerate claims: For instance, Allstate uses a conversational AI bot, Amelia, to speed up claims. As of last year, Amelia was handling 250,000 conversations each month and was used by 75% of Allstate call-center employees.
• Optimize inspections: Virtual inspection tools from companies such as JMI Reports and Plnar enable insurers to massively reduce total inspection budgets and the time it takes to perform an inspection.
• Automate underwriting: AI-powered platforms can automate underwriting, rapidly identify property condition and provide straight-through processing for low-risk policies. Munich Re’s Lee Sarkin said in an interview that these systems enhance underwriter efficiency without replacing them.

These examples highlight how AI can boost efficiency and reduce expenses, which is crucial for carriers partnering with reinsurers. But perhaps even more compelling is the predictive power of AI – its ability to anticipate and even avoid future claims.

See also: 5 Ways Generative AI Will Transform Claims

The Predictive Power of AI

Catastrophic and severe weather events, coupled with a 50% increase in catastrophe rates at July renewals, underline the urgency for reinsurers to address increased losses. The problem isn’t just natural disasters, either. Secondary perils like convective storms have also become a significant loss driver, accounting for 68% of all catastrophic loss dollars in the first half of 2023, Swiss Re reported. In response, some major carriers have stopped writing business in high-risk states such as California and Florida. This is not a long-term solution, though, and certainly does not endear these companies to their policyholders. 

The better path forward is to harness the predictive power of AI. AI models can identify properties vulnerable to damage and even estimate potential damage, proving paramount in rebuilding reinsurer trust. Most impressive of all, AI-powered risk insights show what steps can be taken to reduce or avoid losses entirely. At a time when government weather models are viewed as increasingly outdated, insurers need to prioritize investing in predictive AI.

The ideal AI models should analyze both regional hazard data and property-level vulnerability:

• Hazard describes the likelihood that a specific region will experience a catastrophic or severe weather event. This information is largely based on historical losses but can also be determined by increasingly sophisticated catastrophic (CAT) models powered by the latest advances in supercomputing.
• Vulnerability describes the likelihood that a property will be damaged during an event. It can also quantify the amount of damage the property will sustain. Based on risk factors such as roof condition and defensible space, the most accurate vulnerability data is based on CV detections applied to high-quality imagery. 

Carriers can and must use predictive AI to mitigate losses, proving to reinsurers that they are a safe investment. Imagine a carrier providing coverage in a coastal region prone to floods and hurricanes. Because roof staining, roof material and tree overhang are strongly correlated with hurricane losses, carriers can flag properties exhibiting those factors while using straight-through processing on ones less vulnerable to damage. Carriers can then contact the policyholders in advance of an event to notify them of their risk level. 

If a policyholder simply repairs their roof or trims some vegetation, they could significantly reduce their vulnerability, potentially avoiding future losses. In another example, an insurer could use CV-powered CAT models to monitor and predict the path of a wildfire, notifying policyholders in real time whether they are at risk. When AI is used as a predictive tool, everyone wins. Insurers reduce the possibility of a major loss, policyholders attain a competitive premium and – most relevant for this article – reinsurers trust the risks they are underwriting.

See also: Insurtech: Not Dead but Different

The Imperative of Transparency 

Yet, while embracing digital transformation, particularly the predictive capabilities of AI, is the best way for insurers to regain confidence from reinsurers, that is not enough on its own. If they really want to succeed, carriers must marry innovation with a philosophy of radical transparency.

Reinsurers value transparency. Carriers must not only claim AI use but also transparently show its application, avoiding "black box" technologies. In other words, reinsurers need AI to be explainable. Can insurers show confidence scores and accuracy levels for the AI models they use? Can they pinpoint the exact property attributes that contributed to an overall risk score? Reinsurers want to know if their primaries have a sophisticated and explainable system in place for managing risk. The more they can look at the nuts and bolts of this system, the greater their trust.

Embracing innovative technology and prioritizing transparency is key for insurers to foster stronger ties with reinsurers. Moreover, this dual focus doesn't only benefit business-to-business relations; it ripples out to instill greater trust in policyholders. In essence, confidence in an insurer's process naturally boosts faith across the entire insurance ecosystem.

KEY TAKEAWAY:

--Whether you're implementing AI algorithms or large language model (LLM) tools like ChatGPT, rushing to implement tools without expertise can hurt claim accuracy, data security and confidentiality. As with any new product or service, solutions need to be developed with those who have vast industry knowledge, specific to users’ needs, and must meet the high standards our industry requires for data integrity, confidentiality and the trust our customers expect.

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Growing mainstream use of GenAI tools like ChatGPT have supercharged the desire to adopt this technology in every industry, including P&C. GenAI enables interfaces that allow users to engage with AI through natural language, dramatically improving usability. 

GenAI has prompted the technology community to invest significantly in more powerful computing solutions, creating a powerful, virtuous cycle that is very exciting. Tech leaders are now being pressed to deliver programs that integrate generative artificial intelligence into their claims workflows. 

Innovation With Industry Expertise

While our company is equally excited and encouraged by the opportunities GenAI offers, as industry veterans we have the responsibility to make sure AI fear of missing out (FOMO) does not lead to technology implementation without proper due diligence.

With the influx of fintech startups promising to automate claims overnight, it’s easy for companies to take shortcuts in implementing AI and risk damages to carriers and their customers. Often, these companies lack the intricate knowledge and experience in claims management to understand the complexity, or the long-standing partnerships needed for connectivity across the entire workers’ comp or auto claims landscape. Whether you're implementing AI algorithms or large language model (LLM) tools like ChatGPT, rushing to implement tools without expertise can hurt claim accuracy, data security and confidentiality.

Cigna, for example, currently faces a class action lawsuit over charges that it illegally used an AI algorithm to deny hundreds of thousands of claims without a physician’s review. The case illustrates why giving AI too much authority right away may not be the best first step. New tech, we believe, shouldn’t replace human judgment where it’s needed; instead, it should be used to augment expertise and prioritize human experience and intervention.

This is the premise behind the development we have done in our auto physical damage team with the Mitchell Intelligent Solutions portfolio. Mitchell Intelligent Review, for example, combines cloud computing, Mitchell-generated vehicle data and the company’s machine-learning and computer-vision models to scan photos of collision damage and automatically evaluate the labor operations entered on the estimate. The artificial intelligence (AI) then flags problematic estimates that require a closer look by a trained appraiser. Automating this traditionally manual, time-consuming and resource-intensive task is intended to help carriers increase estimate accuracy, ensure quality and pinpoint workflows or areas of the business in need of improvement. The new approach also gives insurers the ability to review every estimate written and then assists them in identifying the specific appraisals they should focus on to accelerate settlement times for policyholders. 

When it comes to the hype around LLM and GenAI, casualty industry professionals need to be even more diligent in using this technology, especially when it comes to privacy concerns for claims processes. You wouldn’t want to place a claimant’s medical or personal identifiable information (PII) through a public system like ChatGPT without knowing where the information is going and who is securing it. Ethical questions about how and where to implement these technologies can only be determined by those with sufficient experience and expertise in the industry to know where the opportunities lie, while proper usage must be trusted to those with appropriate security and technology infrastructure.

See also: 5 Ways Generative AI Will Transform Claims

Opportunities Abound

The good news is there are many practical application opportunities for AI in our industry. These include customer service, triage, potential fraud and property damage and bodily injury applications, just to name a few. My company is looking at these areas and others, using our experience in auto and workers’ comp claims and our proficiency in advanced technologies to provide guidance on where these technologies make the most sense across auto and casualty claims. 

AI continues to provide a powerful opportunity to leverage data (be it medical billing data, repair information, photos of damaged vehicles or images from litigation demands) to improve task automation and enable advanced decision support to claims professionals as they seek to help individuals return to work, achieve optimal health or get back on the road.

As technology leaders, we’re excited about the potential of LLMs and GenAI technology. As with any new product or service, however, solutions need to be developed with those who have vast industry knowledge, specific to users’ needs, and must meet the high standards our industry requires for data integrity, confidentiality and the trust our customers expect. Meeting these demands won’t be easy, but I believe, with the right mix of experience and innovation, the opportunity of GenAI is even better than the buzz.

In the early years of digital transformation, life insurers vying for top market position leaned on the newest technologies to boost the speed and operational efficiency of their services. 

Given the near-ubiquitous nature of digitization, the effectiveness of this approach may have run its course. Advanced technology, particularly AI, has become such a permanent fixture that it has leveled the playing field, no longer serving as the ace in the hole it once was for competing life insurers.

But AI is not a perfect solution for every pain point, and relying on it risks overshadowing the value of human touch. Insurers and agents can understand and address the varied needs, concerns and circumstances of individual customers empathetically – and for now, that remains beyond the realm of artificial intelligence.

After all, life insurance must be handled delicately. Beneficiaries who interact with insurers often do so at an especially sensitive time in their lives, which algorithm-driven systems are not yet best equipped to handle. Therefore, a life insurer’s true differentiator should be one that encompasses the best of both worlds, by leveraging technology that complements the human element of agent-to-customer interaction.

The Pivotal Role of AI

Considering its enhanced analysis and performance capabilities, AI has a pivotal role to play in simplifying and streamlining the complex processes that define the life insurance industry.

Filing insurance claims traditionally involved a series of cumbersome steps, from submitting information through navigating assessments, up until the payout stage. Likewise, underwriting for life insurance usually involves a drawn-out manual assessment of various factors such as age, gender, medical history and lifestyle choices.

Now, AI can significantly streamline risk assessment and pricing, allowing insurers to stay competitive while enabling policyholders to choose from an array of options tailored to their needs.

By simplifying the process, life insurers make coverage more accessible to a broader audience, which increases the likelihood that people will acquire a policy in the first place.

The Human Element

While innovative technologies have excelled in expediting insurance processes, their true success depends on the insurer’s capacity to use those tools to offer personalized support for families and inspire generational loyalty, a concept that would have seemed unimaginable just a few years ago.. 

Life insurance is a uniquely sensitive space because it encompasses so many highly personal matters: mortality, family well-being and financial security. For many, life insurance is more than a mere financial transaction – it signifies a particularly emotional and mournful time in a beneficiary’s life.

But it can also present an opportunity for insurers to foster emotional connections with the bereaved by helping to arrange grief counseling, plan funerals, write obituaries, settle financial affairs and navigate probate. To that end, traditional insurance agents have often been the most effective vendors of this service. Even amid the rise of digital processes, agents are guiding and comforting individuals and families through the difficult bureaucracy of policy purchasing and claims.

Although AI-driven chatbots and virtual assistants can provide responses and clarification instantaneously, they cannot fully replicate human compassion and sincerity.

For these reasons, some life insurance companies continue to offer policyholders the tried-and-true model of agent-customer relationships throughout the claims process, even as they roll out new digital-first offerings such as apps and platforms that redirect users to services for counseling, funeral planning and probate management, among others.

This personable, tech-blended approach not only reduces the workload for agents but does so without automating every interaction, thus preserving the vital human touch.

A Benevolent Blend

For all its bells and whistles, technology within the life insurance industry cannot operate in a vacuum. Digital processes still need that human touch to establish and maintain relationships and make policyholders and their families feel heard, seen and supported.

As marginal differences in speed and efficiency become less of a competitive factor, life insurers will have an opportunity to seek an alternative approach that strikes a balance between AI-driven automation and human interaction. This will ultimately result in a more comprehensive and supportive industry that not only meets the emotional needs of its policyholders but also drives an entirely new line of competition – one based on empathy and compassion.