Tag Archives: cape analytics

Are Insurers at Risk of Becoming Obsolete?

Most insurance companies understand that the industry has a looming obstacle to overcome, but they are not doing anything to prepare for this shift.

A recent PricewaterhouseCoopers survey suggests that 74% of insurers view financial technology innovations as a challenge. While they might understand the potential of fintech, only 28% of industry players are working to collaborate with fintech companies. There is a definite disconnect between thoughts and actions.

Moreover, a paltry 14% of insurers participate in accelerator and incubator programs, where potential is ripe for partnerships. As innovations ranging from AI to the sharing economy steer insurance toward disruption, the companies that drag their feet ultimately risk being left behind.

A perfect combination of technology and data certainly has the potential to revolutionize insurance. In the coming years, leaders in insurance will lean on technology like bots and AI to help the industry flourish. Because insurance already involves so many algorithms, underwriting will soon reach levels of precision that were hard to imagine only a few years ago.

See also: How to Embrace Insurtech Culture

Cape Analytics, for example, is using technology to spot high-risk roofing in a sea of aerial images. Company data indicates that homes in the U.S. have an 8% likelihood of having a roof of either poor or severe quality. This is huge news for insurers — when a building has a low-quality roof, the possibility of a claim is 50% higher, and the resulting payout is generally larger. When insurers can gauge roof conditions in advance, they are in a better position to provide an accurate quote and avoid unexpected losses.

The insurance industry has offered more uncertainty than peace of mind in years past, but innovation and digitization have the power to change that. Regardless of how this change happens, future insurance industry disruption will revolve around customers.

Digitization in consumer finance has given industry players a wealth of data, but this evolution has yet to make a real difference in the lives of consumers struggling with the same financial problems that have plagued generations. New payment methods, robust financial applications, and a variety of shiny gadgets are great, but insurtech must tackle consumer concerns directly if they want to become true customer advocates.

Technology has already left an indelible mark by making insurance cheaper, but cost savings are only the tip of the iceberg. Insurtech can make insurance products more attractive and easier to understand, which will increase the likelihood that customers recommend these products to friends and family. Improving experiences at an individual level might seem minor, but it has the added benefit of ensuring the broad swathe of underinsured customers in Europe and the U.S. are better-protected against risk.

See also: Finding Value in Insurtech (Part 1)  

Running toward insurtech rather than away from it will help companies cater to underserved markets and create disruptive offers. Many insurance companies are calling on insiders with industry knowledge — whether they are underwriters, actuaries or claims professionals — to become more involved and identify meaningful opportunities to solve problems. Instead of trying to catch up to emerging trends, insurers can use the potential of insurtech to leapfrog their competitors.

With new technologies like the autonomous vehicle, medical advances and robotics becoming a viable reality, disruption in the insurance space is inevitable — even if it comes from outside of the industry. It is time for insurance executives to think ahead and embrace innovation as the heart of their strategies while determining how extensively they would like to work insurtech into their operations. The insurtech revolution is coming, and the companies that truly embrace this change will be poised to make the most of future disruptions.

How AI Is Redefining Insurance Industry

The insurance industry has operated with great consistency and clear processes for many years. People may not always like or agree with how things work, but nearly everyone from the consumer to the provider essentially goes with it — no uprisings to drive change, no big shakeups. That is until recently. Seemingly all of a sudden, artificial intelligence (AI) is infiltrating the insurance industry, which may be a bit scary to those devoted to long-established practices.

In reality, we are witnessing relatively quick developments and sparks of innovation, considering the overall life cycle of the insurance industry. And what AI offers — now and promises to in the future — is anything but scary. It’s actually quite exciting as the industry enters a truly transformative period that will result in greater efficiency, significant cost savings, and far better service and care.

What Constitutes AI

AI has become one of the biggest buzzwords in the tech landscape, so I want to define what it really means, particularly as it pertains to the insurance industry. AI is a computerized system that exhibits behavior that is commonly thought of as requiring human intelligence. Taking this a step further, it essentially translates to machines acquiring a certain level of “human-ness” so that interactions with software become more like interactions with real people. It also mandates that a system has the ability to learn and improve on its own.

Advances in AI come because of a number of factors, but, undoubtedly, consumer-based technologies have led the charge. Voice, machine learning, computer vision and deep learning have been refined in consumer products, services and platforms, but they are now being combined to create really powerful automated solutions for some of the biggest issues organizations face.

See also: 4 Ways Machine Learning Can Help  

Specific to the insurance industry, novel AI-based applications can shift the workforce and advance what companies are able to assess and offer as well as how quickly they can do it. And this is just over the short term. McKinsey predicts that AI “has the potential to live up to its promise of mimicking the perception, reasoning, learning and problem solving of the human mind. In this evolution, insurance will shift from its current state of ‘detect and repair’ to ‘predict and prevent,’ transforming every aspect of the industry in the process.”

The Rise of Insurtech

This may sound a bit abstract and futuristic, but AI advances have already led to a whole new market segment: insurtech. A slew of new companies have popped up, showcasing strong growth by bringing AI and machine learning to market with the industry’s very specific and nuanced needs in mind. For example, Cyence, which was acquired by Guidewire Software, developed a platform to ascertain the financial impact of cyber risk and management of risk portfolios; and Cape Analytics provides a service to property insurers that combines AI and geospatial imagery to analyze property and streamline the underwriting process — and these are just two examples. Other AI-based companies have emerged to reduce costs in claims operations, identify various insurance protection options, and transform mobile and social media marketing for insurance companies.

The insurtech segment is not defined by new players alone. Several incumbents have also dipped their toes into the AI waters to develop innovative applications. State Farm developed Distracted Driver Detection that uses dashboard camera images. Allstate has ABIE, a virtual assistant to help agents with information regarding Allstate’s commercial products, and Progressive now applies machine learning on top of data collected from client drivers through the “Snapshot” mobile app.

What Does It All Mean?

First and foremost, the rise of insurtech indicates that the insurance industry is changing profoundly as it modernizes. The ability to analyze countless data points in mere seconds opens ways to assess and predict that humans simply cannot hope to accomplish. This does not mean that humans are no longer needed in the industry. Quite the contrary. People still possess higher-level thinking skills that machines are not equipped to gain. The capacity to factor in intangibles, to make judgment calls, to see and interpret what lies beyond the screen — these are human skills that will always be in demand.

See also: Key Challenges on AI, Machine Learning 

In this light, AI and machine learning applications should be leveraged to streamline and better inform the decisions that humans must make. When this happens, workers are freed to focus on the facets of their jobs that matter the most. In addition to benefits to workers, organizations experience multiples of improvement in cost savings by increased efficiency, accuracy and better predictions generally. Simultaneously, customer service and patient care improve by providing answers and resources tailored to their specific case in a fraction of the time.

Perhaps the most exciting impact of insurtech, however, will be the new business models that arise. The notion of how we administer care will change, as will the way we construct policies for individuals and companies. Essentially, what has never been possible before is suddenly on the table. The options may appear overwhelming or even threatening to the existing way of life, but AI and insurtech have arrived. The advancements that will occur over the next decade will be extraordinary for all constituents. Pay attention and embrace the innovation long needed in the insurance industry.

Why Is Data on U.S. Property So Poor?

How a building is constructed and maintained and where it is located all have a massive impact on its potential to be damaged or destroyed. That knowledge is as old as insurance itself.

So why do so many underwriters still suffer from lack of decent data about the buildings they insure?

And when better data does get collected for U.S. properties, why does it seem to get lost as it crosses the Atlantic?

London is an important marketplace for insuring U.S. risks. It provides over 10% of the capacity for specialty risks — those that are hard, or impossible, to place in their home market through admitted carriers. Reinsurers of admitted carriers, insurers of homeowners and small businesses in the excess and surplus markets and facultative reinsurers of large corporate risks all need property data.

The emergence and growth of a new type of property insurers in the U.S. such as Hippo and Swyfft has been driven by an expectation of having access to excellent data. They are geared up to perform fast analyses. They believe they can make accurate assessments and offer cheaper premiums. The level of funding for ambitious startups shows that investors are prepared to write large checks, tolerate years of losses and have the patience to wait in the expectation that their companies will displace less agile incumbents. If this works, it’s not just the traditional markets in the U.S. that will be under threat. The important backstop of the London market is also vulnerable. So what can established companies do to counter these new arrivals?

Neither too hot nor too cold

The challenge for any insurer is how to get the information it needs to accurately assess a risk, without scaring off the customer by asking too many questions. The new arrivals are bypassing the costly and often inaccurate approach of asking for data directly from their insureds, and instead are tapping into new sources of data. Some do this well, others less so. We’re already seeing this across many consumer applications. They lower the sales barrier by suggesting what you need, rather than asking you what you want. Netflix knows the films you like to watch, Amazon recommends the books you should read, and soon you’ll be told the insurance you need for your home.

Health insurers such as Vitality are dramatically improving the relationship with their clients, and reducing loss costs, by rewarding people for sharing their exercise habits. Property insurers that make well-informed, granular decisions on how and what they are underwriting will grow their book of business and do so profitably. Those that do not will be undercharging for riskier business. Not a viable long-term strategy.

Fixing the missing data problem would be a good place to start.

We recently brought together 28 people from London Market insurers to talk about the challenges they have with getting decent quality data from their U.S. counterparts. We were joined by a handful of the leading companies providing data and platforms to the U.S. and U.K. markets. Before the meeting, we’d conducted a brief survey to check in on the trends. A number of themes emerged, but the two questions we kept coming back to were: 1) Why is the data that is turning up in London so poor, and 2) what can be done about it?

This is not just a problem for London. If U.S. coverholders, carriers or brokers are unable to provide quality data to London, they will increasingly find their insurance and reinsurance getting more expensive, if they can get it at all. Regulators around the world are demanding higher standards of data collection. The shift toward insurers selling direct to consumer is gathering momentum. Those that are adding frictional costs and efficiencies will be squeezed out. This is not new. Rapid systemic changes have been happening since the start of the industrial revolution. In 1830, the first passenger rail service in the world opened between Liverpool and Manchester in the northwest of England. Within three months, over half of the 26 stagecoaches operating on that route had gone out of business.

See also: Cognitive Computing: Taming Big Data  

Is the data improving?

Seventy percent of those surveyed believed that the data they are receiving from their U.S. partners has improved little, if at all, in the last five years. Yet the availability of information on properties had improved dramatically in the preceding 15 years. Why? Because of the widespread adoption of catastrophe models in that period. Models are created from large amounts of hazard and insurance loss data. Analyses of insured properties provide actionable insights and common views of risks beyond what can be achieved with conventional actuarial techniques. These analytics have become the currency of risk, shared across the market between insurers, brokers and reinsurers. The adoption of catastrophe models accelerated after Hurricane Andrew in 1992. Regulators and rating agencies demanded better ways to measure low-frequency, high-severity events. Insurers quickly realized that the models, and the reinsurers that used the models, penalized poor-quality data by charging higher prices.

By the turn of the century, information on street address and construction type, two of the most significant determinants of a building’s vulnerability to wind and shake, was being provided for both residential and commercial properties being insured for catastrophic perils in the U.S. and Europe. With just two major model vendors, RMS and AIR Worldwide, the industry only had to deal with two formats. Exchanging data by email, FTP transfer or CD became the norm.

Then little else changed for most of the 21st century. Information about a building’s fire resistance is still limited to surveys and then only for high-value buildings, usually buried deep in paper files. Valuation data on the cost of the rebuild, another major factor in determining the potential scale of loss and what is paid to the claimant, is at the discretion of the insured. It’s often inaccurate and biased toward low values.

If data and analytics are at the heart of insurtech, why does access to data appear to have stalled in the property market?

How does the quality of data compare?

We dug a bit deeper with our group to discover what types of problems they are seeing. In some locations, such as those close to the coast, information on construction has improved in the last decade, but elsewhere things are moving more slowly.

Data formats for property are acceptable for standard, homogeneous property portfolios being reinsured because of the dominance of two catastrophe modeling companies. For non-admitted business entering the excess and surplus market, or high-value. complex locations there are still no widely adopted standards for insured properties coming into the London market, despite the efforts of industry bodies such as Acord.

Data is still frequently re-keyed multiple times into different systems. Spreadsheets continue to be the preferred medium of exchange, and there is no consistency between coverholders. It is often more convenient for intermediaries to aggregate and simplify what may have once been detailed data as it moves between the multiple parties involved. At other times, agents simply don’t want to share their client’s information. Street addresses become zip codes, detailed construction descriptions default to simple descriptors such as “masonry.”

Such data chaos may be about to change. The huge inefficiency of multiple parties cleaning up and formatting the same data has been recognized for years. The London Market Group (LMG), a powerful, well-supported body representing Lloyd’s and the London company market has committed substantial funds to build a new Target Operating Model (TOM) for London. This year, the LMG commissioned London company Charles Taylor to provide a central service to standardize and centralize the cleaning up of the delegated authority data that moves across the market. Much of it is property data. Once the project is complete, around 60 Lloyd’s managing agents, 250 brokers and over 3,500 global coverholders are expected to finally have access to data in a standard format. This should eliminate the problem of multiple companies doing the same tasks to clean and re-enter data but still does nothing to fill in the gaps where critical information is missing.

Valuation data is still the problem

Information on property rebuilding cost that comes into London is considered “terrible” by 25% of those we spoke to and “poor quality” by 50%.

Todd Rissel, the CEO of e2Value, was co-hosting our event. His company is the third-largest provider of valuation data in the U.S. Today, over 400 companies are using e2Value information to help their policy holders get accurate assessments of the replacement costs after a loss. Todd started the company 20 years ago, having begun his career as a building surveyor for Chubb.

The lack of quality valuation data coming into London doesn’t surprise Todd. He’s proud of his company’s 98% success in accurately predicting rebuilding costs, but only a few states, such as California, impose standards on the valuation methods that are being used. Even where high-quality information is available, the motivation may not be there to use it. People choose their property insurance mostly on price. It’s not unknown for some insurers to recommend the lowest replacement value, not the most accurate, to reduce the premium, and the discrepancy gets worse over time.

Have the losses of 2017 changed how data is being reported?

Major catastrophes have a habit of exposing the properties where data is of poor quality or wrong. Companies insuring such properties tend to suffer disproportionately higher losses. No companies failed after the storms and wildfires of 2017, but more than one senior industry executive has felt the heat for unexpectedly high losses.

Typically, after an event, the market “hardens” (rates get more expensive), and insurers and reinsurers are able to demand higher-quality data. 2017 saw the biggest insurance losses for a decade in the U.S. from storms and wildfire — but rates haven’t moved.

Insurers and reinsurers have little influence in improving the data they receive.

Over two-thirds of people felt that their coverholders, and in some cases insurers, don’t see the need to collect the necessary data. Even if they do understand the importance and value of the data, they are often unable to enter it into their underwriting systems and pipe it digitally direct to London. Straight-through processing, and the transfer of information from the agent’s desk to the underwriter in London with no manual intervention, is starting to happen, but only the largest or most enlightened coverholders are willing or able to integrate with the systems their carriers are using.

We were joined at our event by Jake Hampton, CEO of Virtual MGA. Jake has been successful in hooking up a handful of companies in London with agents in the U.S. This is creating a far stronger and faster means to define underwriting rules, share data and assess key information such as valuation data. Users of Virtual MGA are able to review the e2Value data to get a second opinion on information submitted from the agent. If there is a discrepancy between the third party data that e2Value (or others) are providing and what their agent provides, the underwriter can either change the replacement value or accept what the agent has provided. A further benefit of the dynamic relationship between agent and underwriter is the removal of the pain of monthly reconciliation. Creating separate updated records of what has been written in the month, known as “bordereau,” is no longer necessary. These can be automatically generated from the system.

Even though e2Value is generating very high success rates for the accuracy of its valuation data, there are times when the underwriter may want to double-check the information with the original insured. In the past, this required a lengthy back and forth discussion over email between the agent and the insured.

JMI Reports is one of the leading provider of surveys in the U.S. Tim McKendry, CEO of JMI, has partnered with e2Value to create an app that provides near-real-time answers to an underwriter’s questions. If there is a query, the homeowner can be contacted by the insurer directly and asked to photograph key details in his home to clarify construction details. This goes directly to the agent and underwriter enabling the accurate and fast assessment of rebuild value.

What about insurtech?

We’ve been hearing a lot in the last few years about how satellites and drones can improve the resolution of data that is available to insurers. But just how good is this data? If insurers in London are struggling to get data direct from their clients, can they, too, access independent sources of data directly? And does the price charged for this data reflect the value an insurer in London can get from it?

Recent entrants, such as Cape Analytics, have also attracted significant amounts of funding. They are increasing the areas of the U.S. where they provide property information derived by satellite images. EagleView has been providing photographs taken from its own aircraft for almost 20 years. CEO Rishi Daga announced earlier this year that their photographs are now 16 times higher-resolution than the best previously available. If you want to know which of your clients has a Weber barbeque in the backyard, EagleView can tell you.

Forbes McKenzie, from McKenzie Insurance Services, knows the London market well. He has been providing satellite data to Lloyd’s of London to assist in claims assessment for a couple of years. Forbes started his career in military intelligence. “The value of information is not just about how accurate it is, but how quickly it can get to the end user,” Forbes says.

See also: How Insurtech Helps Build Trust  

The challenges with data don’t just exist externally. For many insurance companies, the left hand of claims is often disconnected from the right hand of underwriting. Companies find it hard to reconcile the losses they have had with what they are being asked to insure. It’s the curse of inconsistent formats. Claims data lives in one system, underwriting data in another. It’s technically feasible to perform analyses to link the information through common factors such as the address of the location, but it’s rarely cost-effective or practical to do this across a whole book of business.

One of the barriers for underwriters in London in accessing better data is that companies that supply the data, both new and old, don’t always understand how the London market works. Most underwriters are taking small shares of large volumes of individual properties. Each location is a tiny fraction of the total exposure and an even smaller fraction of the incoming premium. Buying data at a cost per location, similar to what a U.S. domestic insurer is doing, is not economically viable.

Price must equal value

Recently, the chief digital officer of a London syndicate traveled to InsureTech Connect in Las Vegas to meet the companies offering exposure data. He is running a POC against a set of standard criteria, looking for new ways to identify and price U.S. properties. He’s already seeing a wide range of approaches to charging. U.K.-based data providers, or U.S. vendors with local knowledge of how the information is being used, tend to be more accommodating to the needs of the London insurers. There is a large potential market for enhanced U.S. property data in London, but the cost needs to reflect the value.

Todd Rissel may have started his career as a surveyor and now be running a long-established company, but he is not shy about working with the emerging companies and doesn’t see them as competition. He has partnerships with data providers such as drone company Betterview to complement and enhance the e2Value data. It is by creating distribution partnerships with some of the newest MGAs and insurers, including market leaders such as Slice and technology providers like Virtual MGA, that e2Value is able to deliver its valuation data to over a third of the companies writing U.S. business.

Looking ahead

It is widely recognized that the London market needs to find ways to meaningfully reduce the cost of doing business. The multiple organizations through which insurance passes, whether brokers, third-party administrators or others, increase the friction and hence cost. Nonetheless, once the risks do find their way to the underwriters, there is a strong desire to find a way to place the business. Short decision chains and a market traditionally characterized by underwriting innovation and entrepreneurial leaders means that London should continue to have a future as the market for specialty property insurance. It’s also a market that prefers to “buy” rather than “build.” London insurers are often among the first to try new technology. The market welcomes partnerships. The coming generation of underwriters understands the value of data and analytics.

The London market cannot, however, survive in a vacuum. Recent history has shown that those companies with a willingness to write property risks with poor data get hit by some nasty, occasionally fatal surprises after major losses. With the increasing focus by the regulator and Lloyd’s own requirements, casual approaches to risk management are no longer tolerated. Startups with large war chests from both U.S. and Asia see an opportunity to displace London.

Despite the fears that data quality is not what it needs to be, our representatives from the London market are positive about the future. Many of them are looking for ways to create stronger links with coverholders in the U.S. Technology is recognized as the answer, and companies are willing to invest to support their partners and increase efficiency in the future. The awareness of new perils such as wildfire and the opening up of the market for flood insurance is creating opportunities.

Our recent workshop was the first of what we expect to be more regular engagements between the underwriters and the providers of property information. If you are interested in learning more about how you can get involved, whether as an underwriter, MGA, provider data, broker or other interested party, let me know.

And the Winner Is…Artificial Intelligence!

Artificial intelligence stands out as one of the hottest technologies in the insurance industry in 2018. We are seeing more insurers identifying use cases, partnering and investing in AI. 85% of insurers are investing time, money and effort into exploring the AI family of technologies. The focus is not so much on the technology itself as on the business challenges AI is addressing.

  • For companies looking to improve internal efficiency, AI can assist through machine learning.
  • For those working to create a dynamic and collaborative customer experience, AI can assist with natural language processing and chatbots.
  • For those seeking an edge in data and analytics, AI can help to gain insights from images with the help of machine learning.

Through our annual SMA Innovation in Action Awards program, we hear many success stories from insurers throughout the industry that are innovating for advantage. AI was a key technology among this year’s submissions. The near-ubiquity of AI was even more obvious among this year’s insurer and solution provider winners, many of whom are leveraging some type of AI to solve widely variant business problems. They have provided some excellent use cases of how insurers are applying AI and how it is helping them to succeed.

Two AI technologies, machine learning and natural language processing, fuel Hi Marley’s intelligent conversational platform, which West Bend Mutual Insurance piloted in claims with outstanding results. The Marley chatbot lets West Bend’s customers text back and forth to receive updates, ask and answer questions and submit photos. Its use of SMS messaging means that communication can be asynchronous and done on a customer’s own schedule, eliminating endless rounds of phone tag.

  • Natural language processing allows Marley to communicate with customers in plain English – both to understand their needs and to respond in a way that they will understand.
  • Machine learning enables Marley to continue to improve. The platform analyzes every conversation and uses it to shape how Marley responds to specific requests, refining its insurance-specific expertise for future interactions.

See also: Strategist’s Guide to Artificial Intelligence  

Natural language processing is also a critical tool for Cake Insure, a digital workers’ comp MGA with a focus on making the quoting experience easier for direct customers. One of the hurdles that would-be customers had to overcome in obtaining workers’ comp coverage was answering a multitude of questions regarding very specific information that a layperson is unlikely to know about or understand.

  • NAIC codes, for example, are required for every workers’ comp policy, but the average small business owner would be baffled if asked about them. Cake circumvents this by asking usera to type in descriptions of their companies in their own words. Natural language processing parses this plain-language description and searches for its approximate match in the NAIC data sets. This back-end process occurs without the user’s awareness and without exposing potentially confusing content.
  • As with Hi Marley’s chatbot functionality, natural language processing is paired with machine learning to improve its ability to respond to specific phrases and content.

Machine learning can also be deployed in conjunction with other AI technologies. Image analysis and computer vision are combined with machine learning in Cape Analytics’ solution, which can automatically identify properties seen in geospatial imagery and extract property attributes relevant to insurers. The result is a continually updated database of property attributes like roof condition and geometry, building footprint and nearby hazards.

  • Computer vision helps turn the unstructured data in photos and videos from drones, satellite and aerial imagery into structured data.
  • Machine learning allows the solution to train itself on how to do that more effectively, as well as higher-level analysis like developing a risk condition score for roofs.

We are only scratching the surface of how AI can be applied across the value chain. The incredible variety of AI’s potential applications in insurance is difficult to overstate. QBE knows that well: It won a company-wide SMA Innovation in Action Award for wide-ranging activities in emerging technologies and partnerships with insurtech startups, but AI in general, and machine learning specifically, are their top priorities. In addition to partnering with dozens of insurtechs, QBE has also pushed itself to deploy each insurtech’s technology somewhere within its business – meaning QBE has dozens of different creative AI applications in play at once. For example, in partnership with HyperScience, QBE is improving data capture from paper documents through machine learning and computer vision.

These winners’ stories demonstrate the myriad ways that insurers are applying AI to improve business operations. Notably, its deployment helps them to significantly improve the customer experience – or, in the case of data capture, the internal employee experience. The need for this kind of seamless customer experience in the digital world cannot be overemphasized. AI, which struck many as a science-fictional concept, has proven its real-world worth by enabling insurers to transform their customer journeys and experience.

With full-scale implementations popping up across the insurance industry, as well as the pilots and limited rollouts that we have seen in previous years, it is easy to lose sight of the fact that we are seeing only the very tip of the iceberg in terms of how AI can transform the business of insurance. Applications of more advanced and advancing AI technologies, as well as the combination of AI with emerging technologies such as drones, new user interaction technologies, autonomous vehicles and IoT, are unexplored territory that is bright with promise.

See also: 3 Steps to Demystify Artificial Intelligence  

This much is clear: AI will change the face of the insurance industry. In fact, it’s already happening.

For more information on the SMA Innovation in Action Awards program and this year’s winners, please click here.

To download a free copy of SMA’s white paper AI in P&C Insurance: Pragmatic Approaches for Today, Promise for Tomorrow, please click here.

Future of P&C Tech Comes Into Focus

In a 2017 report titled “Drones: Reporting for Work,” Goldman Sachs estimated the addressable market opportunity for drones globally between 2016 and 2020 to be $100 billion, of which the insurance claims drone market was estimated to be $1.4 billion.

And the report did not address the wider opportunities in personal and commercial property insurance: underwriting, pricing, risk prevention, traditional and virtual claims management, fraud detection and product marketing. The report also didn’t cover the use of images from satellites and fixed-wing aircraft, including streaming video.

Whatever the actual size of the total insurance market opportunity, the impact of aerial and drone images in insurance will be enormous.

Industry observers are just beginning to recognize the transformation in property insurance underwriting and claims that is emerging through advanced analytics, artificial intelligence and machine learning tied to neural networks and integrated with data from aerial and drone images.

Property claims investigation costs the industry an average of about 11% of premiums – automated inspection can reduce that expense substantially. And automated property inspection cycle times can average two to three days, compared with 10 to 15 days using traditional methods – lowering costs and increasing customer satisfaction.

Providers will transform the property insurance industry through the convergence of these sources of better images, expanding numbers and types of connected home technologies, customer self-service and aggregated property risk data (historic and real-time).

Follow the money

Venture and private equity investment activity in emerging technologies is a good indicator of potential growth opportunities – these professionals typically engage subject matter experts and conduct deep market research and diligence in a highly disciplined and proven evaluation process prior to investing. Since 2012, almost $2 billion has been invested in more than 370 drone company deals, and the current run rate is more than $500 million in announced deals annually, according to CB Insights research, which states that ”19 of the 24 smart money venture investors have backed at least one drone company since 2012.”

See also: How Technology Drives a ‘New Normal’  

Within just the past two months, four such insurance-related transactions were announced;

  • Nationwide Ventures made an investment in Betterview, a machine learning insurtech startup focused on analyzing data from drones, satellite and other aerial imagery for commercial and residential property insurers and reinsurers. This follows a September 2017 seed round funding of $2 million.
  • DroneDeploy, the world’s largest commercial drone platform, raised $25 million of Series C venture capital, bringing total funding to $56 million.
  • Cape Analytics raised $17 million to grow its AI and aerial imagery platform for insurance companies, led by XL Innovate.
  • Clearlake Capital Group acquired a significant interest in EagleView Technologies alongside Vista Equity Partners, which had purchased EagleView in 2015. (Vista also owns the majority of Solera, parent of property and auto insurance claims services and information providers Enservio and Audatex.)

In 2017, Genpact, a global professional services and insurance claims solutions provider, acquired OnSource, which provides 24/7/365 full service on-demand drone property inspection claims and settlement services across the U.S. Earlier that year, Genpact acquired BrightClaim and National Vendor, providers of integrated claims solutions to the U.S. property insurance market

In 2016, Airware, a global enterprise drone analytics company, closed a Series C round of $30 million to bring its total funding to $110 million. Early in 2016, Verisk Analytics formed the Geomni business unit to specialize in image sourcing and analysis and has since acquired a number of U.S.-based aerial survey companies and their aircraft fleets. Verisk also owns Xactware, the dominant industry provider of property insurance claims solutions and third party products. The Geomni fleet is expected to include more than 125 fixed-wing aircraft and helicopters by the end of 2018, operating from 15 hubs located throughout the U.S. Verisk expects to invest approximately $100 million in Geomni through 2018.

Competition and differentiation

The space has attracted a large number of participants in the past two years, and there are no signs of slowing. Competitors are taking innovative paths to differentiation, including: drone manufacturing, drone operating software for use by field staff and contractors, ground-based roof and wall measurement technologies and full-service, virtual property inspection and property damage reports using drones.

Insurance industry adoption and barriers

The insurance industry’s use of images from satellite and fixed-wing aircraft is fairly well-established, particularly in catastrophe response planning and claims. The North American property/casualty insurance industry has been cautious and conservative in its testing and adoption of drone use for property claims and in using aerial images for underwriting.

Until recently, FAA rules had made it onerous for carriers and industry vendors to obtain licenses and permission to use drones for property inspections. However, after extensive industry lobbying efforts, assisted by more pro-business policies, that obstacle has eased significantly, and several carriers have trained staff and hired contractors to use drones for property claims inspections. Obstacles remain, including restrictions on use near airfield perimeters and outside of operators’ line of sight.

Carriers are split into two roughly equal camps (by market share) on more recently introduced third party services that provide virtual property inspections: those that do not believe that drone image and damage identification technology is sufficiently accurate as yet to manage claims leakage as effectively as their own staff field adjusters – and those that do. Both groups acknowledge that drones are not appropriate for all property claims. Furthermore, customer satisfaction and therefore retention is thought to be higher when insurance company staff visit the property and the homeowner in person.

The future of property insurance

For claims, virtual methods of inspection will include not only drones but claims reporting that involves customers. Claim self-service, including smartphone images and video, which has seen impressive adoption and results in auto claims, is beginning to penetrate property insurance claims, particularly for reporting home interior and exterior wall damage. New, accurate 3D smartphone image measurement technology combined with higher image resolution and the expected expanded availability of much faster 5G wireless broadband will drive adoption.

See also: Secret to Finding Top Technology Talent  

Other methods of property inspection, particularly following extreme wind or hail events and catastrophes, will most certainly incorporate the use of drones, whether operated by insurance staff, managed repair network contractors or third-party inspection services. Also, autonomous drones performing roof inspections not requiring an operator on site may be expected soon.

Finally, on the property underwriting side, we expect high-resolution geospatial image data from multiple sources, artificial intelligence and machine learning to transform that process. Real-time feeds of comprehensive property attributes such as measurements and condition of roofs and other property on the target site will enable instant and more accurate pricing, quoting and binding/renewal of property insurance.

Aerial imagery, mobile technologies, artificial intelligence and computer vision will continue to transform property insurance products and processes, leading to better pricing accuracy, more profitable operations and, above all, better customer experience for policyholders.