For more than a century, carmakers and automobile insurers have largely kept to their own lanes. That was before data ruled. In 2022, data and technology have inspired the automobile industry to get more involved in the insurance side of the ledger, prompting an increase in the number of inter-industry partnerships and more.

For auto insurers, partnerships and other steps car manufacturers have taken to edge their way into the insurance industry offer a way to gain and maintain market share in the highly competitive personal auto space, AM Best Senior Director Richard Attanasio said. Offering products directly and at the point of a vehicle sale brings carriers an avenue of distribution with potentially lower expense levels and additional insight that can help set rates, he said.

Insurers working closely with manufacturers agree that they benefit from access to new data on driving behaviors, and seeing how they affect losses as automation advances and interest in electric vehicles surges. And carmakers that establish their own insurance operations can acquire a “natural feedback loop on driving patterns, effectiveness of safety features, etc., which allows them to further hone their product to meet customer expectations,” Attanasio said.

Points of entry vary by manufacturer and even by country. For instance, Tesla progressed from broker to fronting agency partner to insurance subsidiary. Swiss Re and BMW collaborated to craft a vehicle-specific insurance rating parameter for primary carriers globally to calculate premiums. Some carmakers, such as Toyota, are building out insurance brokerages. Others teamed up with carriers on embedded products.

Toyota overtook Ford as the leading car brand in the U.S. last year, based on 1.9 million vehicle sales, according to market and consumer data company Statista. Ford had 1.8 million, followed by Chevrolet's 1.5 million. Nationwide has partnered with the top two, as well as startup electric “adventure” vehicle Rivian.

Nationwide gains knowledge and strengthens trust by expanding original equipment manufacturer partnerships, said Senior Vice President of Corporate Development Angie Klett, creating “a relationship within their ecosystem that builds upon the customer having the say, the power and determining the path of an experience.” Carmakers and insurance partners today take a customer-first approach that varies from company to company, Klett said. Choose a manufacturing partner carefully, she advised, with an eye on aligning values and strategies.

Each side decides direction for the insurance product, such as if, how and when to embed insurance in the buying process. Klett said embedding is most strategic for manufacturers with a niche market, where customers think a company like Tesla or Rivian has a better handle on the needs of their vehicles' owners. “They're direct-to-consumer OEMs. The buying, the servicing is different. It's not the same as a Ford or Toyota,” Klett said.

Specialized manufacturers, such as Rivian, are notably invested in streamlining the entire car-owning experience, said Sarah Jacobs, Nationwide vice president of personal lines product development, and will lean into the process.

See also: 3 Tips for Improving Customer Loyalty

Toyota Financial Services is an owner of independent property/casualty insurance agency Toyota Insurance Management Solutions (TIMS), which distributes product from multiple carriers. Will Nicklas, president of Toyota Insurance, acknowledged manufacturers' earlier reluctance to enter the highly competitive auto insurance market in the U.S.

“But I think when we decided that cars were going to be connected, and there were going to be a lot more services that we could provide to customers, it made a lot of sense,” he said. “When you think about how insurance plays a role in car ownership, every six months, maybe every 12 months, a customer is renewing an insurance policy. We saw a gap in the ownership experience.”

Nicklas thinks of TIMS as “this new, connected tissue, or this glue that's bringing these two industries together” for a “really powerful collaboration.”

According to the TIMS website, working with Toyota companies and external partners allows the broker to harness data and technology to “improve safety and convenience and save customers time and money.”

The Counterpoint

Some insurance industry experts think the partnerships are helping carriers and manufacturers, but they doubt Tesla will inspire other carmakers to become underwriters. They cite the complexity of regulatory approvals, particularly in the U.S., and profit and loss swings in auto, even among large, legacy insurers.

Risk Information Inc. Editor Brian Sullivan put it bluntly: “There is no advantage at all to a traditional auto manufacturer owning a traditional insurance company.”

Jacobs said regulatory work can't be underestimated. Insurance is “very challenging to break into.”

Barriers are a little easier to clear in some global countries, particularly with a carrier partner. Volkswagen Autoversicherung AG was founded in 2013 as a joint venture between Allianz Versicherungs-AG and Volkswagen Financial Services AG. Volkswagen Autoversicherung AG offers auto insurance in Germany as a primary insurer. In about 30 other countries or markets, VW is an insurance broker, the company said.

“The technology of the cars, especially the car data, gain an increasing importance for the development of our motor insurance products,” a Volkswagen spokesperson said. “For example, in Germany, the safety features of the cars have a direct influence on the motor insurance pricing.” The company hopes to gain telematics experience and integrate insurance offers into VW on-board systems.

Brandy Mayfield, senior vice president and managing director, digital economy for Aon, said partnerships between manufacturers and insurers offer an attractive middle ground.

“As manufacturers build differentiated products, they want to make sure carriers have capacity to insure newer/different technology. Manufacturers also want to minimize friction in the insurance purchase journey and create continued revenue streams from their buyers,” she said.

On the other hand, she said, “shifting from acting as a broker to an insurer presents a significant leap in terms of regulatory complexity, capital intensity and moving the brand into a new category with mixed views from consumers.”

“For original equipment manufacturers to make that investment, there will have to be a clear opportunity to differentiate from traditional insurers or meet truly unmet needs in the marketplace,” she added. “Carmakers must determine what they're solving for by setting up their own insurance structure: more clients, a differentiated insurance product, etc. Many also want to capitalize on profits from the insurance space.”

Carriers can grow a book for certain auto types more rapidly than in the traditional market, she said. Customers may get improved access to parts and repair services, increasing satisfaction with insurers and carmakers. Doubly important for newer vehicles with limited production is “a network to quickly obtain parts and repair,” Mayfield said.

Tesla's push into insurance was reported to be motivated by reducing the cost of ownership. Repair costs ran higher because fewer technicians are familiar with the connected, electric vehicle. Tesla was known for supply chain challenges even before the pandemic, extending repair times.

“Other manufacturers could take a similar approach and offer insurance directly,” Attanasio said, although it would require a significant amount of industry knowledge and infrastructure, including a high level of product/pricing sophistication and policy administration and claims capabilities.

Entrepreneur Elon Musk drew distinctions between how automaker Tesla Motors' insurance operations cover auto risk compared with the traditional insurance industry, which he said suffers from too many players extracting part of the premium along the insurance value chain. “Insurance is quite significant,” Musk, Tesla's chief executive officer, said recently. “The car insurance thing is a bigger deal than it may seem. A lot of people are paying 30%, 40% as much as their lease payment for the car, in car insurance.” Tesla said its real-time insurance is based on measurable driving behavior.

Technology Roots

Twenty-one years ago, when OnStar was collecting vehicle usage data in 34 of General Motors' then-54 models, a spokesman said the onboard automobile information system was working on partnering with insurers. OnStar's inducement included cost savings because insurers wouldn't need to develop data-gathering equipment and then get it into vehicles.

That was three years before Progressive Corp.—which has since become the third-largest private passenger writer in the U.S., according to AM Best data—piloted a usage-based insurance program to research driving habits. In 2008, Progressive started offering customers the option of tying driving data to premiums.

Telematics adoption lagged through the years even as the ease improved from the early days, when consumers were required to install dongles to access UBI. Now that smartphones are common, telematics options from multiple carriers are just an app away. The amount of information an insurer can gather comes close to carmaker-installed monitoring systems, Sullivan said.

Mayfield, however, raised a prime consumer concern: data privacy. “Dealership agents should be prepared to answer a similar line of questioning from consumers: What information from their vehicles will manufacturers plan to share with insurance companies?”

That's a problem for carmakers because the distribution system encourages salespeople to sell vehicles as quickly and with as little friction as possible, Sullivan said. “All a salesperson wants is to get the car off the lot. Anything that might get in the way of closing the sale immediately will be ignored by sales and finance people in dealerships. Insurance is far more complicated than selling rust protection add-ons.”

See also; 5 Trends to Watch in Commercial Auto

Connected carmakers are already collecting enormous amounts of data on how vehicles are driven and maintained. That can give them an edge, albeit a minor one, as telematics becomes more widely accepted, according to Sullivan, even as many car buyers opt to retain a degree of privacy, or at least the right to decide when and who has access to their personal movements and habits.

Ford affiliate American Road Services Co. offers Ford Insure, underwritten by Nationwide Mutual Insurance Co. and its affiliates. Ford Insure customers employ FordPass App (compatible with smartphones) and FordPass Connect (an optional feature on some of the carmaker's models) on newer vehicles to transmit data on miles driven, hard braking and accelerating and stop-and-go and night driving.

Ford's insurance messaging mirrors that of partner Nationwide's for the general public. “While that discount is being calculated, you automatically get a 10% discount just for signing up,” Ford Insure notes on its website, promoting auto insurance discounts as high as 40% and potential additional savings by bundling other vehicles, home or pet insurance with the auto coverage.

Jacobs thinks 70% of new customers will opt in to UBI plans within five years, based on current trends.

Sullivan isn't surprised, seeing the day when drivers who decline to use telematics are presumed to be high-mileage or high-risk policyholders. Even if they're not, they will pay more for the privilege of privacy, he predicted.

This article initially appeared at AM Best. 

Inflation has been top-of-mind since early 2021, and for good reason. For most Americans, the impacts have been inescapable - from the grocery store to the gas pump to the car dealership. Inflation is the highest we’ve seen in 40 years. Auto insurers are struggling to maintain profitability given increases in labor, materials and vehicle replacement costs. And those challenges are only expected to deepen as medical bill inflation accelerates.

History Repeats Itself?

This isn’t the first time that medical care inflation has reacted during periods of high overall inflation. For historical perspective, we need to go all the way back to the recessionary periods of the mid-1970s and early 1980s. Though the specific economic conditions may have been different, we can still draw a few key insights: In general, overall CPI (Consumer Price Index) inflation spikes were followed by similar medical inflation spikes within one to two years (Figure 1), and (Figure 2) the medical increases also stayed elevated for longer periods than overall inflation, which dropped off steeply after peaks.

Figure 1: Consumer Price Index Trend for All Items and Medical Care in the U.S.

Now, let’s examine this trend in the context of the current landscape. Similar to the historical trend, medical care inflation has trailed overall CPI inflation, mainly because contracts between medical providers and major payers (i.e. Medicare/Medicaid, private health Insurers) are typically negotiated years in advance. This disconnect has placed major financial strains on medical providers locked into reimbursement contracts negotiated prior to the recent inflation increases. But, while medical care inflation reacts slowly compared with volatile items such as food and energy, make no mistake, it is catching up (Figure 2). As of August 2022, U.S. medical care inflation was 5.4% compared with 2021, with a steeply increasing trajectory.

See also: What to Do About Rising Inflation?

Figure 2: Consumer Price Index Trend for All Items and Medical Care Jan 2021 -— Present

These medical care price increases land heaviest on auto casualty billing because pricing and reimbursement are not tied to major payer contracts. Case in point: We have observed notable billing severity acceleration in Q3 2022 within both our first- and third-party medical bill review data, with first-party average bill severity up 4.2% and third-party average bill severity up 11% compared with Q3 2021 (Figures 3 and 4).

Figure 3: 1st Party Casualty Average Billed per Line

Figure 4: 3rd Party Casualty Average Considered (Billed less Duplicates) Per Line

Benchmarking to Level the Playing Field

Given the accelerating medical care inflation challenges, it is now more important than ever for auto insurers to leverage benchmarks when evaluating reasonableness of charges, as well as to have them readily accessible to the adjuster. This is easiest in states regulated via mandatory fee schedules, such as Pennsylvania or Oregon, but most states do not use a fee schedule. One option is the use of Fair Health, which maintains a large database of all charged amounts by procedure and geo ZIP code. While effective, a drawback of using methodology based on charge amount is that rapidly rising prices in a region will also drive up associated benchmark values.

Another excellent benchmarking option is the use of Medicare reimbursement schedules. As the single largest payer of medical bills in the U.S., Medicare is well-known to medical providers. It is extremely useful to reference the amount Medicare has agreed to pay for any given procedure by state venue. Because this benchmarking methodology is based on reimbursement versus charge, it is less affected by rapid charge increases.  

To better understand these differences in methodology, let’s use CCC billing data to examine the charge and recommendation values for some typical procedures that have already seen notable cost inflation (Figure 5). With a Fair Health benchmark configured at the 80th percentile for the procedure in the applicable ZIP code, the average reimbursement recommendation lands at 52% of the submitted amount. The Medicare benchmark value lands at just 6% of that same submitted amount. Even at 5x the Medicare reimbursement, the benchmark value lands at 32% of the submitted amount.

See also: Social Inflation: A Claims Perspective (Part 4)

Figure 5:  CCC Charge vs Benchmark Recommendations

These benchmark values are most useful for third-party liability negotiations when integrated into medical bill review applications and supported by robust configuration options. With CCC’s 3rd Party Bill Review Application Injury Evaluation Solutions (IES), insurers can cascade and display multiple benchmark methodologies while also setting rule configurations such as Medicare multipliers by venue or bill type. The adjusters can review all available benchmarks and adjust as a batch or all the way down to bill line level as needed based on the right fit for the claim (Figure 6)

Figure 6: Sample Bill Ingested via IES with Fair Health and Medicare Recommendation Values

Medical inflation is here, escalating quickly, and likely to persist for an extended period based on historical indicators. Insurers who have integrated a comprehensive, consistent, easy-to-use benchmarking strategy into their workflow will be best-equipped to indemnify their casualty claims.

With more than 33 million small businesses in the U.S., their insurance needs become more complex and competitive each year. Small-business owners require various insurance policies – from commercial auto and property to employee liability.

While the personal-lines insurance market started digital transformation years ago, commercial insurance was much slower to automate due to the complexity of the transactions and the reliance on independent-agent distribution models.

Overreliance on more manual processes can result in a poor customer experience, inefficient operations and a lack of usable data for underwriting and pricing. Moreover, TransUnion research shows that 82% of business insurance customers are open to obtaining a policy quote through online channels without agent assistance.

Cutting-edge technology and advanced data capabilities offer commercial insurers an unprecedented opportunity to boost profitability, elevate product sophistication, enhance customer experience and reduce costs.

Insurers that don't embrace digital transformation may risk losing market share to competitors using automation to gain an advantage in the small-business market and add to their bottom lines significantly. To realize higher profits and performance, small-business insurers must prioritize the many digital technology options available and have a clear strategy for automation.

Algorithm-Based Underwriting

When shopping for insurance policies, small businesses oftentimes seek providers offering them the most affordable policy quickly and efficiently. Any delay in receiving the quote will likely result in the customer going to a competitor instead. 

Automated underwriting relies on robotic process automation (RPA), artificial intelligence (AI) and machine learning to find new information sources, glean new insights from existing data, establish consistency in evaluating risks and achieve greater efficiency.

Using externally available data to prefill necessary information at quote facilitates algorithm-based underwriting processes, enabling customers to see a price quote within minutes instead of days. Additionally, data is captured and validated in online forms, significantly reducing the probability of human error and policy mispricing.

Historically, commercial insurance underwriters have reviewed and evaluated most risks because legacy data systems could not process complex small-business transactions and data sources were fragmented, causing significant delays in the quoting process. Additionally, due to the high policy volume, underwriting departments were large, which can drive up costs.

On the other hand, with the proliferation of available data and improved data accuracy, algorithms analyze risks quickly and effectively, enabling insurers to optimize underwriter workloads to focus on more complicated policies and use their time more efficiently. Also, automation can lead to greater worker satisfaction by enabling underwriters to focus on more challenging work.

See also: How to Use Social Media Data in Underwriting

Range of Discretionary Pricing Is Shrinking 

Because algorithms and data were not as readily available and accurate as today, underwriters have historically had more latitude to change prices. For example, they could lower the price from what the algorithm produced by 20-40 points in percentage terms or even raise it by a similar amount, if necessary. 

As the result of improvements in commercially available and scalable third-party data, small business insurance rate plans are increasingly capable of pricing the risk more accurately, and the need for discretionary adjustments to price is shrinking.

For example, previously, a rating algorithm might price a premium at $1,000, but an underwriter could raise or lower it by $500. However, with more precise data, algorithms are more capable of pricing risks, and there's more confidence in the ability to set the price. As a result, the underwriter might still need to change the price, but only plus or minus 10%, rather than 50%. In many cases, underwriter discretion may be eliminated entirely.

For certain less complex small-business risk segments, most insurance companies have a great deal of data. For example, residential plumbers present a common risk. In the past, underwriters may have reviewed these policies and made pricing adjustments, which is a disruptive and time-consuming process. But now, an algorithm can access the risk more precisely and in a much shorter time. As a result, what may have once taken several hours to evaluate can now be completed in a matter of minutes.

Online Transactions

With digital transformation, more small-business insurance policies are transacted online instead of through an agent, reducing costs and improving the cycle times considerably. For example, instead of waiting three days for a quote, customers can receive it in 10 minutes.

When small-business owners evaluate policies, they likely request quotes from several insurance providers. Assuming their policy needs are typical and not complex, the process takes less time because they enter the policy information online instead of visiting an agent's office, which generally takes a significant amount of time and can therefore lead to an unsatisfactory customer experience.

For example, a landscaper looking for insurance coverage has relatively standard risks. The customer is self-employed, so they may only need a policy for the vehicle and a general liability policy. Instead of spending all day in an agent's office, the landscaper can spend 20 minutes online and receive quotes from 10 different insurers of his or her choosing. 

If the customer has a large, complex or medium-sized business, an automated process might not be appropriate; but, for small businesses with standard risks operating in higher-volume business segments, transacting online streamlines the underwriting process and maximizes efficiencies.

See also: 3 Must-Haves for a Self-Service Portal

Transformation Is the Key to Profitability

Small business insurance profitability has been a challenge for providers due to a multitude of reasons including operational inefficiencies, high expenses and inaccurate pricing. 

However, with a substantial increase in data availability, small commercial insurers are realizing the benefits that automation brings to the underwriting and pricing processes, including algorithms, precision pricing and online transactions.

Today's customers expect to receive all types of information immediately and want insurance companies to follow suit. If small business insurers don't join the digital transformation across the industry, they risk being left behind or becoming obsolete.

Following this year’s InsureTech Conference in Las Vegas, it's clear we are operating in a new environment. Here are some thoughts that may be helpful in guiding stakeholders.

We have entered a hardening market amid rising interest rates, soaring inflation and continuing CAT challenges. That means we can expect price increases for insurance across the board. 

Venture capital investment is slowing, and insurtech valuations are moderating. Investors will no longer finance companies that spend a dollar to make 90 cents. Rather, they are looking for insurtech organizations that can achieve strategic efficiencies in both the types of products offered and the distribution methods for those products. The best companies will find investors,  but they may very well receive lower valuations compared with the past few years.

Besides funding constraints, insurtech startups will see a limited appetite for capacity, as major carriers deal with losses from major weather events and rising interest rates.

While several high-profile insurtechs have had a rough year in public market valuations, there are still many bright spots for startups in this marketplace.

See also: Insurtech Success Stories: Still Waiting for Godot

Investors are taking a closer look at startups and emerging growth insurtechs that can clearly demonstrate efficiencies in underwriting, products and distribution—and a realistic path to profitability. By specializing in a niche, MGAs have become more attractive investment vehicles, especially as they are more agile and can leverage advances in technology and data management that larger entities often struggle with. For example, my team and I have created an emerging growth MGA whose online product offering is transforming the experience of buying jewelry insurance throughout the U.S. I brought years of experience in the retail jewelry business, while my co-founder complemented that with in-depth expertise in insurance fundamentals -- the kind of combination that created considerable buzz at ITC this year.  

Our industry has learned key lessons from its past and is finding the best path forward. The market can’t rely on endless venture funding alone to grow. Insurance business doesn’t work that way. But, as seen at ITC, our industry is adapting and improving and is here to stay. 

We hear a lot of talk these days about the virtues of data-driven organizations. That’s certainly reasonable up to a point — but what does it really mean? When it comes to routine operational decisions, in particular, the current bias seems to favor increased automation over human judgment. The data doesn’t lie — or so the story goes — so we’re better off deferring to programmatic decision models.

That notion may be reasonable for some situations, but when you’re operating in a complex and nuanced domain like casualty insurance claims, that highly automated decision paradigm can begin to fall apart very quickly. Thousands of different variables come into play. Medical records and accident reports contain subtle details that provide vital clues about potential risks. To complicate matters, important minutiae are often buried deep inside the narrative content.

An experienced claims manager can pick up on that nuance, provided they have adequate time and attention to devote to reviewing the documentation. Can an algorithm accomplish the same thing?

The short answer is yes, but that comes with a vitally important caveat. In complex domains, advanced data analysis should not drive automated decisions; it should inform and empower human beings to make more effective decisions. The most effective artificial intelligence (AI) initiatives in place today are doing exactly that.

Data-Driven vs. Data-Informed

The distinction here is critically important. The data-driven paradigm is about automation. It’s about shifting decision-making responsibility away from human actors and trusting the algorithms to take their place.

A data-informed approach, in contrast, empowers and assists people to make better decisions by flagging potential risks, highlighting anomalies, and monitoring for changes that may indicate a need for attention. It’s a helper, not a replacement.

For claims managers, this approach has powerful implications. Imagine, for example, that an injured worker has missed three consecutive appointments for physical therapy. What does that mean? If the employee no longer feels a need for treatment, then it may be a sign that they’re ready to return to work, but it could also be an indication that the case has taken a turn for the worse. In either case, an adjuster should be made aware of the situation so they can make a proper assessment.

In a complex domain like claims management, this data-informed approach holds tremendous potential for transforming organizational culture and processes.

Consider how claims are handled today at most organizations; adjusters typically follow up with cases on an “as needed” basis. Depending on the individual adjuster, that might involve a diary notation, a running to-do list, or a collection of sticky notes. Inevitably, though, it means manually reviewing medical bills and records as they come in or when the adjuster’s schedule permits.

In a data-informed organization, claims adjusters focus on meaningful decisions. Because they no longer need to spend their time scanning records in search of salient information, they have sufficient bandwidth available in which to apply their professional judgment on high-priority cases. AI does that legwork for them.

Data-informed organizations can apply their valuable resources toward predictive severity-based workloads. They can focus on claims that need attention today — based on real-time data. Incoming documents are reviewed and scanned by AI, and claims adjusters are notified when a case requires their attention.

See also: The Data Journey Into the New Normal

The Business Opportunity for Insurers

The data-informed approach is already operational in a number of leading companies around the world. It’s transforming processes and driving cultural change — but not in the way that many AI skeptics have predicted. Data-informed organizations aren’t dehumanizing their processes. On the contrary, they’re empowering and elevating their claims professionals by enabling them to focus on meaningful work.

The data-informed paradigm is about focusing on the right claims at the right time. It’s about spotting correlations and anomalies, identifying potential risks and bringing those to the attention of an experienced claims manager.

The result? A data-informed organization has a shorter claim duration and lower-than-average total claim costs. Not surprisingly, workers at data-informed organizations also enjoy substantially higher job satisfaction. These companies are generating high ROI — not by reducing their workforces but by elevating them to higher-value activities.

The Build vs. Buy Debate

How does an organization achieve that kind of transformation? It starts with a predisposition toward innovation and a recognition that advanced data analytics has the potential to transform claims management from an operational perspective.

Conventional wisdom tells us that proprietary data is a differentiated asset. In other words, companies place a high value on their internal data because it’s theirs, and nobody else has it. In the world of AI and machine learning, though, more data is generally better. When ML models have access to higher volumes of information, from a relatively wide array of sources, they can “learn” faster and more effectively.

Building and maintaining those kinds of high-volume data sets can be extraordinarily costly and time-consuming. The implication for insurers is that, in the build-versus-buy debate, there is an increasingly powerful case for moving beyond proprietary data and embracing best-in-class platforms to drive the data-informed model.

This provides for a flexible co-innovation process, enabling insurers to leverage solutions and platforms that have already been proven in the real world, without reinventing the wheel. It’s the fast-track alternative for companies seeking to become data-informed organizations.

As first published in Claims Journal.

Giving the customer what they want — or need, depending on whom you're quoting — is a basic tenet of business. And perhaps the one thing all customers want — and need — is a good experience with the company they are working with.

It's a truth that the insurance industry needs to understand. One of the biggest complaints consumers have against their insurers is the often lengthy time it takes to settle claims. And dissatisfied customers often turn into former customers; studies show that over 60% of customers are likely to switch to a competitor after just one bad experience — while more than three-quarters would jump ship after just two such experiences.

If customers equate slow claims processing with poor service, insurers need to find a way to speed up claims — and fortunately, modern technology, in the form of advanced computer vision, can provide insurers with the means to do just that. These advanced computer vision systems use cameras and visual detection systems in vehicles, drones equipped with high-resolution cameras and images taken by devices such as phones and tablets. The images are then automatically uploaded to machine learning-based AI systems that analyze thousands of data points — type of damage, estimated repair costs, structural integrity (of buildings or vehicles) and much more.

These AI-based systems — relying on advanced data analysis and using machine learning — are able to develop a clear basis for claims far more quickly and accurately than with the traditional method, where human adjusters must visit the scene of the claim and human actuaries must determine the amount to be paid. Companies can thus cut down the investigation phase of a claim from weeks to minutes — rapidly approving settlements and ensuring high customer satisfaction and retention.

Investigations of structural damage from natural disasters, fires, hurricanes and similar events — where conditions make it difficult, if not impossible, to send out adjusters — become much more efficient with computer vision technologies. Drones can much more easily navigate these disaster scenes, flying into corners and crevices and recording images over, under or inside damaged structures. AI-based systems receive and examine the images, determining what damage was caused by the event and what damage may have already been in place. The analysis is based on damage models — patterns of damage based on specific events associated with disasters, such as how a roof would appear if it were blown off by a gust of wind measured at 75 mph — as well as with data on other similar incidents. With all the data taken into account, the system can quickly provide a full assessment of the damage and determine how much the insurer needs to pay to satisfy claims.

Computer vision can also streamline the claims process for incidents involving vehicles — often a sore point for customers, and the source of the largest number of complaints by policyholders. Here, too, the traditional process of claim evaluation — with adjusters sent out to evaluate the condition of a vehicle, the circumstances of an incident (traffic, weather, road conditions, etc.), along with statements by those involved in the incident, as well as police — becomes a time-consuming project that taxes the resources of insurers and leads to undue delays in claim settlement.

See also: Customer Segmentation Is Key

Images taken right away with a customer's mobile phone camera, along with data collected from sensors, road cameras and images recorded by in-vehicle cameras, are collected and analyzed by AI models, which provide an accurate picture of the circumstances of an incident, along with the liability of drivers and the amount of money the company needs to pay. Neural networks running on the customer's mobile device can provide real-time guidance to ensure suitable images are captured that can be used as evidence as part of the claim process.

Computer vision systems can also make sure that insurers get a clear picture of the condition of a vehicle before they even issue a policy — with previous damage noted and excluded from the policy, ensuring that they, too, are treated fairly in their relationship with their customers, eliminating potential fraud on the part of customers.

In addition to increased efficiency and speedier claim settlements, computer vision-based systems ensure that insurance processes are more likely to be perceived as fair by customers — increasing the level of customer satisfaction. Unlike human adjusters, computer vision systems are unlikely to miss small details that could end up being crucial to ensure an accurate evaluation. Customers will appreciate that insurers are doing everything possible to make sure that they get all the money they are entitled to as quickly as possible — and will reward their insurers with continued loyalty.

The technology to implement these systems is available right now, and as the technology proliferates, the price of systems continues to fall. That insurance companies have not yet widely adopted these systems yet is understandable  insurance is a very conservative business, and companies need to ensure that stockholders, stakeholders, and regulators are on board with major changes to the claims process. But implementing computer vision systems for damage assessment is worth the effort; companies will avoid wasting time and money, and ensure that they use their resources as efficiently as possible — while customers will get their money faster, increasing their satisfaction with their insurers and ensuring that they remain customers for many years to come.

Cyberattacks and breaches are happening more frequently and carry a hefty price tag, yet most organizations are still unprepared to deal. According to the IBM 2022 Cost of Data Breach report, the share of breaches caused by ransomware grew 41% in the last year and took 49 days longer than average to identify and contain. The report says a data breach in the U.S. costs over twice the global average, coming in at $9.4 million. Organizations recognize that risk continues to grow more dynamic and enduring.

2023 poses an opportunity for organizations to invest in cybersecurity; how they choose to invest will determine the strength of their cybersecurity posture. Incident response solutions drive resilience by improving action and analysis of threats, enabling security teams to increase efficiency and success rates.

Below are six cybersecurity and incident response trends and priorities that can help organizations in 2023.   

Investments in cybersecurity are core to investments in the entire business. 

Organizations tend to limit themselves by focusing on just cyber trends, rather than cybersecurity as a whole. Yes, businesses should invest in strong security protection for computer systems and networks to help eliminate the chances of a cyberattack or breach. However, incident response is also an integral component of improving an organization’s cybersecurity posture, providing organizations with the foundation to leverage cyber data and apply it to their responses. Businesses should think about putting an extra emphasis on building and reinforcing their incident management response going into the new year.  

See also: Cyber Risk and Insurance in 2022

Preventative controls need to be balanced with resilience.

The threat landscape evolves faster than most organizations can anticipate, and most of the time there isn’t just one “cookie cutter” incident response that can cover every cyber incident. Having a solution in place that allows organizations to target their response to a specific incident improves their agility and reduces remediation time. 

Organizations need to defend against multiple attack vectors and address the complexity. 

Changes brought by digital transformation and remote work are increasing ecosystem complexity. As organizations invest in tools that monitor, detect and provide information on their IT environment, they should invest in the processes that leverage this information. Incident response solutions orchestrate and automate responses for all threat-types. Organizations accelerate processes, streamline collaboration and use system data and documents by responding through a "single pane of glass." 

Organizations will be evaluated against performance requirements related to risk. 

Cybersecurity is being recognized as more than an IT issue, as CEOs tie risk management to business value. Incident response tools serve this aim by aligning the activity, information and people involved in each leg of the response. With the entire response memorialized in one place, management conducts reporting and process improvement with greater insight. 

Cybersecurity is becoming the determinant factor in third-party transactions and engagements. 

Existing collaboration tools are not fit for secure collaboration between internal and external stakeholders. However, there are a few incident response solutions that can provide secure access to system data and documents, enabling organizations to pursue transactions and engagements with third parties. Challo by CafeX is one of these platforms. The main benefit of using a third-party-friendly solution is the speed in which you are able to share information and address the cyber incident instead of having barriers block communication or access to documents. 

Automation is the future of cybersecurity and incident response management. 

In any threat situation, organizations face common challenges, including locating incident response plans, communicating roles and tasks to response teams and monitoring actions during and after the threat. Often, information and actions are siloed, which can slow response times and hamper recovery. And in the case of ransomware, plans may be inaccessible and communication systems knocked offline, prohibiting an effective response. Organizations can rely on solutions that can automate an incident response protocol to help eliminate barriers.

Speed is everything. More specifically, as digital technology and software continue to transform traditional business sectors, the speed of innovation is everything. Those companies that can innovate quickly will thrive. Innovation comes through iterative experiments, large data sets and insights. 

Artificial intelligence and machine learning (AI/ML) promise to automate much of the process to find those insights. However, programming AI and getting it right is a tricky business. If it were easy, everyone would be doing it.

Projections show a future market of $500 billion for AI applications, yet there are only 300,000 developers available. There is a big talent gap across all the required skillsets.

There are two core challenges here: 1) how to get to market with innovation quickly? 2) how to drive powerful AI/ML into those innovative applications and at scale?

Low-Code

Our answer to solve these challenges is to combine the speed of low-code application development with the scalability and power of the leading AI/ML engines and large data sets. The low-code approach to development breaks applications into distinct "blocks" within a visual interface, enabling a Lego-like assembly of drag-and-drop components to form a flow, which can then be deployed as a complete application. Almost all low-code development platforms will tread the common ground of basic RPA (robotic process automation), where documents or customer information flows from an input form into a portal and then a customer data platform (e.g. Salesforce). Low-code is common among marketing team applications that manage tracking segments and advertising campaigns. 

Our evolution to the low-code platform brings the full power and potential of the leading AI engines and IoT platforms. With these smart engines incorporated directly into the low-code platform, complex AI-enhanced and IoT-connected applications can now be built much more quickly. Basically, the speed of low-code + the power of AI = enhanced speed of innovation.

The Smart Applications

As these AI applications become easier to develop and deploy, we are witnessing an explosion of use cases across industries. License plate readers are deployed to provide an almost touchless experience at the gas pumps. Drivers can now “just drive out” of parking garages because their license plates have been read at entry and exit, and fees are automatically calculated. License plate readers can automate any drive-through experience, where each coffee/food/pharmacy order is made on the app ahead of time and fulfilled orders are lined up to be handed out according to the queued cars.

Image recognition is becoming smarter at an astonishing rate: What was only primitive car counting and  people counting two years ago, is now full 3D capture of streets, all the objects and moving vehicles while the car and camera are in motion. Each object is being recognized and rendered as a standalone 3D object in near real time. Our team has taken this technology to recognize weapons (rifles, guns, knives) with a high-90s percentage of accuracy and is offering it to schools. We can do this because the image recognition engine is now advanced and compact enough to run on a small edge computer on-site.

See also: SAAS 3.0: Smarter, Faster, Better

Low-Code Brings the Speed and the Smart Together

These AI capabilities are complex, involving terabytes of data to train and deploy. As the pace of innovation accelerates, the application development becomes the actual bottleneck, not the core intelligence of the AI or IoT engine within the app. Low-code gives a promising solution to this bottleneck: as new data sets become available, or new engines evolve, and as new deployment scenarios emerge that rely on legacy or complex data environments (e.g., the gas pump scenario has to go all the way to legacy solenoid switches to physically unlock the pump), Low-code will likely be the only development method that will be able to keep pace.

Talking with people in the wake of the International Insurance Society's Global Insurance Forum three weeks ago, I've heard a note I haven't heard much lately: optimism.

In the face of the formidable challenges we see in the news daily, there is a sense among speakers and attendees that the industry has risen to the occasion in handling massive claims and now has an opportunity to play a leadership role on even seemingly intractable problems such as climate change.

Josh Landau, president of the IIS, told me that, "the climate side of ESG [environmental, social and governance issues] is an area where the industry can have an outsized impact in terms of what the industry is underwriting or not underwriting and where they're investing and not investing. The industry also has an opportunity to help shape behaviors that promote social equality and inclusion and equity."

Ken Mungan, chairman of Milliman, who spoke at the GIF, said bluntly: "Humanity will solve the challenge of climate change. It's just a question of how long it will take and how much pain we will have in the process." He then offered what I thought was a remarkably clever idea for how the insurance industry can do even more than it is already. 

The problem he addressed is that insurers are only allowed to invest in securities with an investment-grade rating, and many of the efforts to reduce climate change and mitigate its effects don't carry such a rating. They can't. They're based on such new technology, in many cases, that many are bound to fail. But what if investments in climate-related initiatives could be pooled and then split into tranches? Equity investors and, in many cases, government funding could take the riskiest tranches, while insurers could invest in debt that, based on the pooling, could easily carry an investment grade.  

Climate initiatives would still require an awful lot of funding from sources outside of insurance companies, of course, but the industry would be able to put a lot of weight behind environmental efforts while earning good, safe returns that will protect policyholders.

Thierry Leger, group chief underwriting officer at Swiss Re, said the industry can already hold its head high because of how it's handled massive claims in recent years because of the global pandemic, disruptions in supply chains, the Russian invasion of Ukraine and other geopolitical crises, etc. 

"The insurance and reinsurance industry has kept its promises to help societies be more resilient," he told me as part of the video interviews I conducted following the GIF, which we'll share as soon as the edited series is available. "We have paid billions to help companies, communities, people and families to get back on track. And we're all very committed to this course."

I encourage you to check out the recordings of the sessions from the Global Insurance Forum -- they all offer meaty thinking from some of the most senior executives in the industry, and you'll surely find two or three that will grab your attention. And I certainly learned a lot hosting the interview series based on the thinking at the conference. I suspect you will, too. 

Now, the industry obviously still faces many challenges. Josh said that, in particular, he expects insurers will continue to have to wrestle with inflation over the next year, along with the related labor shortages. He added that "the geopolitical issues we were talking about at the beginning of the year, as we picked the Great Reset theme for the GIF, have not only not abated but have become more volatile."

But let's stick with the theme of optimism at least for a bit. It's nice to have a break from the doom and gloom, and there will be plenty of time to focus on inflation, geopolitics and whatever else this crazy world decides to throw at us.

Cheers,

Paul 

To provide a superior customer experience, insurance companies must thoroughly understand their customers. Where are they in their lives? What are their concerns and expectations? What motivates them, and what are their immediate needs? Answering these and similar questions enables insurers to better connect with their customers.

The challenge is that no two customers are the same. For example, the insurance needs of a 25-year-old state worker living in an apartment outside Albany are vastly different than those of a 62-year-old retired CEO in Tampa who owns two homes and a new boat. Insurance products best-suited for the state worker in New York likely wouldn’t be appropriate for the affluent retiree in Florida.

It would be impossible for insurers with thousands or tens of thousands of customers to fully understand each of them individually. However, by categorizing customers based on personal and financial characteristics, current and future needs and long-term goals, insurers can develop personalized products and services that will grow revenue. Accurately segmenting customers requires that insurers use data from multiple sources.

One of the big values of segmentation is that it helps insurers identify their most profitable customers. Contrary to traditional thinking, this is not often the customer generating the greatest revenue; it can be some smaller-revenue customers that have low costs in servicing them. Segmentation allows insurers to optimize spending and more efficiently invest resources such as support time, product development time and special offers.

Further, effective customer segmentation positions insurers for up-sell and cross-sell opportunities, if analytics uncover buying trends or ownership patterns across different segments. “Analytics tools spotlight the highest-value clients and high-potential leads,” McKinsey writes in a new report.

Though understanding their customers pays off for insurers in any environment, this knowledge is particularly critical today as inflation drives up prices and puts pressure on margins. Data is the key to gaining actionable insights into your customers.

Below are three specific data sources insurers can use to effectively gain insights into, and segment, their customers:

Internal customer data

The low-hanging fruit for starting customer segmentation is using internal customer data. Insurers have a wealth of internal information for customer segmentation on hand in the form of sales data. How customers have spent their money with you provides many clues about what motivates an individual or customer segment to make a purchase. This information provides the basis for insurers to anticipate customer needs and tailor products and services to meet those needs.

Applying analytics to sales data allows insurers to segment customers by the types of products and services they’ve purchased, what time of year they’re most likely to be looking for insurance, how much they typically spend and other transaction-based information. 

Third parties

Insurers can purchase consumer data from aggregators such as Acxiom and Experian that contains vast amounts of information for segmentation. They can further append that data to existing customer records and learn more about that customer. This is important because an insurer would want to append the relevant data to the correct client. Typically, the aggregator could do that by one or several data tributes the insurer would have for their clients. For example, one aggregator does that by matching the customer’s street address to the address that they have. Other aggregators use other methodologies of appending.

Through aggregators and direct sources, insurers can access demographic data, identity data, personal background information, lifestyle data, individual health records and prescription histories, personal financial data, credit history, shopping history, preferred mode of payment, property and vehicle information, driver records, data on property risks (flooding, wildfires, etc.) and weather data (historical, current and forecasted).

Third-party data is useful to insurers both for customer segmentation efforts and to improve efficiency by enabling data pre-fill, which streamlines the application process and enhances the customer experience.

Customer-interaction data

Insurers can learn much about their customers – from the customers themselves – simply by capturing and analyzing data from support interactions. When customers call or exchange digital messages with a support agent, they are communicating volumes (through their words and tone) about their current needs, expectations and state of mind.

Data from customer interactions can be used by insurers to segment customers by interests and areas of concern. For example, insurers can segment people who are asking about disaster coverage. Should certain questions come up repeatedly, it tells insurers what is top-of-mind for many customers. This information can lead to revenue opportunities or identify potential problems that must be addressed, such as customers in specific locations not receiving renewal notifications until their policies expired.

A cloud-based platform that offers customers multiple channels for communicating with support personnel makes it even easier for insurers to gather valuable data that can be used for segmentation. And customer support technology with artificial intelligence (AI), machine learning (ML) and natural language processing (NLP) can supply even more data from customer interactions by conducting sentiment analysis. Not only do these intelligent tools assist support workers in real time by feeding them appropriate responses to customers who may be distraught or angry, it also gives insurers insights into a customer’s current emotional state. This can provide an opportunity for the insurer to reach out to the customer.

Conclusion

“The exploding volume of data available to insurance carriers is giving rise to new business models, revenue streams and enormous opportunities to increase value,” McKinsey writes in its report. “As first movers among insurers create new business models and seek to harness the potential of their data, those that wait will be at a significant competitive disadvantage.”

Segmentation increases the value of customer data by giving it context, making it easier for insurers to understand and meet the needs of existing and potential customers. Combining third-party, internal sales and customer interaction data from your support center will help insurers better serve their customers and provide highly targeted products and services that will sell.