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Property Claims: It’s Time for Innovation

The personal and commercial property claims process has traditionally lagged well  behind other segments of P&C insurance in the adoption of technology and innovation. That officially ended in 2020, aided by a global pandemic that changed virtually everything about life and business as we knew it. Understanding the factors behind the historical lack of innovation in property claims provides insights into why and how this segment is suddenly undergoing such rapid transformation.

Auto vs. Property Claims Process Transformation

When compared with the recent impressive rate of change in auto claims, property claims appeared to be a more of a laggard than it really was – but a laggard nonetheless. To put this in perspective, U.S. auto insurance policies, premiums and claims in 2019 were approximately four times larger than property. Further, auto claims are generally more visible and more consequential to the public than property claims. And the auto claims process was broken until about 1990, with the emergence of direct repair programs enabled by internet and database technologies, so the transformation has been that much more obvious and impressive.

Industry Fragmentation

The property claims repair market is characterized by extreme fragmentation, which exceeds that in the auto insurance claims industry. This is due to several factors: 

  • the relatively large number of service providers specializing in distinctly different major damage types, especially managed repair networks, as well as independent contractors, in general
  • the complexity of property claims themselves, which involve the coordination of numerous general and specialty provider types for a given claim 
  • the proliferation of task-specific software solutions, which are generally not integrated with one another
  • the smaller influence of property insurers on the repair process as compared with the influence that auto insurers have (because of less consolidation of property insurers and because they collectively represent only about 33% of repair industry revenue while auto insurers represent almost 90% of collision repair revenue)

A high-level comparison of market fragmentation of third-party auto and property claims repair provider markets provides another important explanation of the emerging transformation in property claims. The collision repair industry has undergone significant consolidation both in terms of the numbers of repair shops and shop ownership – and consolidation continues. Since 1990, the number of U.S. repair locations has fallen roughly 50% to approximately 32,000. Moreover, consolidators have created large multi-location, multi-regional and national MSOs (multi-shop operators) and now control almost 30% of the repair industry revenue. Private equity investments and relatively inexpensive debt have provided the enormous pools of capital required to enable this consolidation.   

See also: Key Advantage in Property Underwriting

Property Claims Ecosystem

In studying the property claims, mitigation and restoration ecosystem, we identified 110 companies with material market share, which we grouped within nine distinct categories:

  • Software applications for:
    • Property estimating
    • Restoration management
    • Claim management platforms
    • Accounting/financial, measurement, documentation, communication and productivity
    • Payment solutions
    • Imaging/aerial inspection
  • Services:
    • Third-party administrators (TPAs)
    • Property claims adjusting and estimating
    • Managed property repair networks

Industry Consolidation

When we researched corporate ownership profiles for these 110 firms, we discovered that 45 – or 39% of them – are funded or controlled by private equity, venture capital or a few strategic investors. While there is some such investor activity in every one of the nine segments, it is most pronounced in managed property repair networks, claims management platforms and imaging/aerial inspection verticals.

These investors are fueling consolidation in these segments in much the same way as they are in the auto claims ecosystem, and will spur greater adoption of cost-effective and process innovation technologies. This is already evidenced by the emergence and adoption of artificial intelligence, computer vision, augmented, virtual and extended reality, machine learning and natural language processing across property claims.

Opportunities

Emerging Property Repair Market Opportunity

The property repair industry is 40% to 50% mature, while we estimate the auto claims industry is approximately 80% mature. This is partially illustrated by direct repair claims penetration of the collision repair industry, which is at or over 50% for carriers with higher market share (and more for some auto carriers) versus less than 10% on average for property repair.

Homeowners property insurance claims and ecosystem software and technologies market, viewed holistically, represent a significant and mostly unaddressed market opportunity. The situation closely parallels the auto insurance claims process and collision repair markets of 1990, which saw technology and economics drive vendor consolidation and carrier adoption of managed national repair programs, which were enabled by automated estimating software development, digital communications, imaging and end-to-end claims workflow tools.

Property Claims Solution Platforms

Property insurance carriers increasingly will be seeking technology-driven end-to-end property claims management solutions featuring;

  • connectivity between all parties from report of loss to remediation to payment and closure
  • hybrid insourced/outsourced carrier claims and repair network management capabilities, including  universal, standardized contractor onboarding, performance metrics, automated skills/needs matching, user reviews and vendor rankings.
  • integration with Guidewire’s claims platform or similar partner ecosystems

Property Claims Technologies

Artificial intelligence (AI), machine learning (ML), robotic process automation (RPA),computer vision (CV), natural language processing (NLP), aerial imagery including drones and digital payments are being aggressively adopted across the P&C insurance claims process, and specifically property.

  • Smart home technology adoption will mitigate and in some cases eliminate claims and losses; Bain Capital predicts that in just five years there will be 50 billion connected devices and a trillion by 2030. According to Statista Market Forecast, the global smart home market was valued at $55.65 billion in 2016 and is projected to reach $174.24 billion by 2025, growing at an annual rate of nearly 14%. While 32% of homes currently have a smart device, that number is expected to reach 52% by 2025.
  • The impact of these technologies to the property claims and restoration industries is already — and will become even more — significant
  • As residential policyholders become more comfortable with self-administered smartphone photo and video inspections of property damage reported directly, insurers will gain more control over the restoration assignment process, which will promote the use of national repair networks (and the claims management software that can manage the end-to-end process)
    • It is estimated that the use of photo inspection services can reduce field claims cost from an average $550 down to between $60 and $90 and the cost of technical inspections from $550 to $300
    • Technical inspections or VAIP (virtual adjusting and inspection programs) will fuse services, including the use of a licensed adjuster. Claims will offer faster cycle times and savings of 35%.
    • Providers of satellite and aerial images, including drones, are gaining in importance in the residential property damage identification, validation, damage assessment and repair estimation process.
    • Satellite and aerial imagery are increasingly being used by the property insurance industry for catastrophe planning and response, including damage evaluation and estimation.

Property insurance carriers now seek to avoid the effort and responsibility of managing restoration contractor selection or oversight but require a complete end-to-end workflow management platform to achieve their goal.

See also: How to Pursue Innovation in a Crisis

The property insurance claims and repair industries continue to move through a multi-segment structural transformation caused by prevailing market conditions, including industry fragmentation, consolidation, investments, revenue and geographic scale, end-to-end technology and software integration, emerging technology adoption and claims process improvement. Companies and investors that recognize the numerous opportunities presented by this transformation and solve for these dynamics are likely to be the future industry leaders.

AI in Commercial Underwriting

Today’s underwriters have more variables to contend with, more submissions, more competition and more data of all kinds to deal with than ever before. That’s why more and more insurance firms are deploying AI in commercial underwriting.

Machine learning (ML) and AI are incredibly well suited for helping to deal with the masses of data that underwriters now face. These technologies are changing underwriters’ working lives for the better and delivering huge benefits to businesses and the insurance industry as a whole.

In this article, we’ll explore five key ways you can implement AI and ML in the underwriting process and the results they can achieve. Without further ado, let’s get started.

1.  Processing underwriting submissions

Although efforts have been made to streamline submission processing, many lines of business in the insurance industry still have to deal with large volumes of documents that need to be processed manually. Until now, that’s just been part of the job — and a time-consuming, laborious one.

New applications of AI in commercial underwriting can give great assistance in extracting information from PDFs, printed documents, emails and even handwritten documents, reducing the amount of work underwriters need to do by hand. Optical character recognition and natural language processing are now sophisticated enough to identify the required data in a document, extract it and even perform a degree of evaluation. These advances in text extraction and analysis are opening up efficiencies in underwriting processes, expediting workloads that had previously been a burden to insurance professionals. Time saved on submissions processing is time gained for more rewarding work that makes better use of underwriters’ skills and helps to develop the business.

2. Making risk appetite decisions

As you know, reviewing submissions for viability is another task that can take up a lot of an underwriter’s time. Analyzing the submission and all the related risk data, making the decision whether to underwrite it – it all takes time and effort. And it’s another area where you can deploy AI in commercial underwriting to achieve great results.

Machine learning can now offer underwriters valuable assistance in the decision-making process. Using data on previous applications that have been approved or rejected, these systems build an understanding of which are likely to be viable and which aren’t. The systems can automatically decline certain activities described in the application as free-form text, if deemed too risky or otherwise unviable. Using text classification, these activity descriptions can be automatically mapped onto their corresponding industry codes, based on a given standard. If an application is found to be viable according to the system’s judgment, it can also recommend the most appropriate product according to your historical data. Once again, this valuable assistance can be a real asset for time-pressed underwriters.

3. Submission assignment and triage

Some underwriting submissions, in certain lines of business, require extra attention during processing. They need to be prioritized, but, unlike with other submissions, this can’t be done using simple, blanket rules such as their policy effective date. Underwriters need to look in greater depth to decide their priority.

Using AI in commercial underwriting can help here, too. Optimization and forecasting technologies can assist in assigning these submissions to the most appropriate underwriter. Predictive modeling can also rank submissions according to their estimated closing ratio or some other key performance indicator (KPI). For instance, AI could decide to rank one application highly because you’ve recently been successful at closing business with that broker. These innovations have a tangible impact on how well your business operates and your bottom line: Submissions are allocated more effectively, and your overall closing ratio improves.

See also: ‘3D Underwriting’ in Life Insurance

4. Evaluating risk profiles

To evaluate the risk involved in a submission, underwriters must often invest considerable time in research. They must research and weigh all kinds of information to properly evaluate these risk profiles. Sifting through the wealth of information available, in myriad formats, can be like searching for a needle in a haystack — until now.

Today’s intelligent tools can search through many types of structured (processed and labeled) data as well as raw, unstructured data and aggregate relevant information for underwriters to use. For instance, an underwriter may use this system to search through a database of property inspections, to compare similar cases of structural damage and their results. These systems also make it far easier to retrieve similar past applications to see patterns and learn from earlier experience. Now your business never has to make the same mistake twice.

As we said earlier, AI is the master of dealing with large volumes of complex data, so, when it comes to locating and surfacing valuable items of information like this, AI is in its element. The benefits for underwriters and businesses are huge here: They can be better informed and more confident in their risk evaluations.

5. Coverage recommendations

Toward the end of the underwriting submissions review process, it’s time to make a judgement: what coverages will be recommended? AI-powered systems are capable of assisting end-to-end, so they have much to offer at this point, too.

Recommender systems can help with coverage judgments. By analyzing previous applications, they can get a sense of what the appropriate coverages, with limits and deductibles, might be and offer suggestions the underwriters can use to make their final decision. On a business-wide scale, this means your product and coverage recommendations will be better aligned with clients’ needs and their risk profiles.

Ready to deploy AI in commercial underwriting?

All the use cases we’ve outlined here are available to businesses right now, so if you want to start deploying AI in the underwriting process, you can start obtaining the benefits without delay.

As the industry evolves in the coming years, we’re certain that AI will become an even more useful assistant to underwriters all over the world. And, as new applications of AI in commercial underwriting are developed, we look forward to telling you all about them.

This article was originally published here.

Crucial Technologies for P&C During COVID

Technologies like machine learning, the Internet of Things (IoT), robotic process automation (RPA) and natural language processing (NLP) were already hot topics in P&C insurance before the world was turned upside down in 2020 due to the pandemic. These and many other “transformational” technologies have great potential for insurers in the rethinking and optimization of distribution, underwriting, claims and many other parts of the business. So, it is important to ask the question – how have the initiatives that leverage these technologies changed due to the pandemic?

Are personal and commercial lines carriers still moving forward with projects in 2021? Do executives still have the same expectations about the potential of these technologies to transform their business?

We answer these questions in detail for 13 specific technologies in two new SMA research reports, one covering personal lines and the other covering commercial lines.

However, I won’t leave you hanging in this blog, wondering about the answers to those questions. The short answer is yes – P&C insurers generally plan to move forward in 2021 with projects that leverage various technologies that have the power to deliver significant results and competitive advantage. The technologies we follow closely and have profiled in our reports have been organized into three strategic planning horizons: short-term, near-term and long-term.

For both personal and commercial lines, technologies in the AI family play heavily in the short-term category. Machine learning, NLP, RPA, computer vision and new user interaction technologies all rank high in terms of their potential to transform and in the level of activity underway or planned by insurers. Technologies that fall into the near-term or long-term horizons include wearables, blockchain, voice, AR/VR (augmented reality/virtual reality), 5G and autonomous vehicles. All have potential in insurance and will likely be incorporated into projects by innovators over the next couple of years but will not make it into broad, mainstream application until midway to late in the decade.

Our research on transformational technologies, when viewed in concert with our SMA Market Pulse surveys, shows that in some cases proofs of concept (POCs) and new projects have been put on hold in 2020, but all indications point to full steam ahead in 2021. Major projects already underway are continuing, and insurers state that they do not want to lose momentum for foundational projects like core systems. Projects that include transformational technologies needed to address digital gaps that were exposed during the pandemic have been raised in priority.   

See also: AI in a Post-Pandemic Future

In many ways, the pandemic is accelerating digital transformation across all industries, including insurance. Transformational technologies will play an outsized role in that transformation and look to be important components of insurers’ plans for 2021 and beyond.

‘3D Underwriting’ in Life Insurance

After 9/11, a revolution happened in airline travel. Airline security tightened far beyond what we had previously known. In addition to new carry-on guidelines, travelers were subjected to more frequent individual screenings. More items had to be removed from our bags and examined. Electronics had to be turned on. Our shoes started coming off. The TSA needed to know us personally before we were allowed to fly.

The revolution, of course, created lines. We had to get up earlier to get to the airport to account for two hours in security. Shorter flights were no longer worth it. “I can drive there in less time.”

So, in 2019, the TSA, doing anything it could to improve the line situation, began using computed tomography (CT) scans in many busy airports. CT scans would change 2D baggage scans into 3D scans, allowing the operator to look at an item in greater detail without tagging a bag to be opened and checked by hand. The ability to “see” the hidden information would shorten lines and streamline the travel experience.

When COVID hit, lines evaporated. Air travel hit rock bottom. Lines are mostly not an issue right now, but public safety is now an even greater issue. In China, for example, large scanners are being used to check human temperatures on anyone traveling. Those with high temperatures are tagged and removed for further screening.

This concept bears a close look for all insurers, and especially for life insurers. In what ways can we use technology to know applicants and policyholders instantly, using that information to protect them and our level of risk in the process? Can we build flexible frameworks for accelerated and fluidless underwriting that will allow us to tackle new issues as they arise and capitalize on new data as it becomes available? Life insurers, group providers and voluntary benefits carriers are entering new regions of opportunity through new doors of data capability.

In Majesco’s latest thought-leadership report, Rethinking Life Insurance: From a Transaction to a Life, Health, Wealth and Wellness Customer Experience, we examine the nature of the purchase experience. Our recent survey across all age groups segmented into two groups – younger (millennial and Gen Z) and older (Gen X and Boomer) generation – painted a picture of a population that is growing in its desire to buy, growing in its goals to stay healthy and wanting the purchase to happen.

Every experience holds data

Today, nearly every aspect of the B2B, B2B2C and B2C customer experience has a level of intelligence that has created a wealth of data about customer activity, behavior and preference. From smart speakers to smart watches, phones, appliances, outlets and more — sensors and signals are everywhere. And, with customers’ permission, sensors are measuring nearly every aspect of their lives. The result is that we now have the data to capture the instantaneous 3D view instead of the 2D view. But insurers must strategically invest in ways to capture and master this data to transform customer experiences in an age of instant digital engagement, delivery and satisfaction. 

The use of data for life insurance is crucially important. Interestingly, the insurance industry has been capturing behavioral insights from customer interactions—offline—for many decades, before technology simplified managing customer relationships. Companies unfortunately didn’t know how to optimize their use of the data before now. That must change if insurers are to survive.

See also: Key Advantage in Property Underwriting

Can data improve the experience?

To meet the needs and expectations of today’s customers, insurers must create a radically different insurance experience, moving from a reactive approach to using real-time data, artificial intelligence (AI), machine learning (ML) and behavioral science to make processes and transactions simple, convenient, transparent and fast, like in other businesses. Encouragingly, our research found that the younger generation is ready and willing to use and share most new data sources for buying and rating life insurance. This willingness will be a key to unlocking sales. 

Adding to this market opportunity, Majesco’s survey data showed that even those Gen Z/Millennials who currently DON’T have life insurance are open to these new data sources being used, nearly the same as their peers who DO have life insurance.

Insurers that are not actively planning and building capabilities to use new sources of data will be rapidly left behind. 

MIB’s February 2020 activity report highlighted that pandemic-related demand for life policies pushed application activity to its highest level for the period since 2015. As noted previously, online, “fluidless” life insurance has dramatically increased during the first three months of 2020.

To accomplish this major experience transformation and bring the decision and the purchase into the same moment, insurers are moving from an underwriter-centric view to a digital, data-driven, accelerated and sometimes fluidless underwriting process. Accelerated underwriting is becoming widespread for term insurance. As shown in Figure 1, the interest in products that use dynamic underwriting and pricing is over two times higher in the younger generations – a significant difference that many insurers are unable to react to today. Once again, our data showed the younger generations who DO NOT currently have life insurance are even more interested in this option than their peers who DO have life insurance (41% vs 35%). Market and growth opportunities await for those who accelerate the move to dynamic underwriting and pricing with new data sources.

Figure 1: Interest in products that use dynamic underwriting and pricing

With the proper use of data, we don’t stop people at the checkout counter

In the traditional underwriting model, we ask people to come to the checkout counter for a price check, then send them home until we verify their ability to buy by gathering lots of medical and personal data. Companies are surprised with their level of not-taken policies. But if you put yourself in the customer’s shoes, why do you want to go through the extra hassle and time, particularly when other options are emerging. Using this picture, we can correctly assume that accelerated underwriting is a modern-day non-negotiable capability that will fit the desire for instant gratification at the point of need.

As a start, some insurers are reducing attending physician statements (APS) and paramedical exams (providing bodily fluids) and using more third-party data and predictive analytics-based models to automate and enhance the underwriting process.

Others are bringing in behavioral data from fitness and wellness programs, social media and new sources with AI and machine learning algorithms to create “smart, automated underwriting” that is continuously learning and adapting. All of this is redefining the process and cycle time and is creating a completely different customer experience.

The right ingredients in the right place at the moment of opportunity

Here is where technology acts as the enabler. For life insurers to fit their products into lifestyles and experiences instead of traditional transactions, several components must be in place.

Cloud Use for Digital Enablement: The point of sale (and marketing) must be well-integrated into current life processes. Often, this means partnerships or channel expansion that will require digital integration using application programming interfaces (APIs) and a cloud-based environment.

Data Integration (and AI / ML) for Accelerated and Fluidless Underwriting: Insurers need to create ways to go fluidless and physician-less so they can automate decisions in real time.

Ecosystem Philosophy: Insurers need to ramp up quickly. They can do this by partnering with ecosystem developers that can give them access to the data sources, channels and technology opportunities that will contribute to quick transformation.

Innovate for the Future: Innovate. Replicate. Reach. Learning the lessons from the past, that good data doesn’t necessarily get used effectively, insurers need to place themselves on a course for optimal data usage across the enterprise. They need to innovate how they use data to get a 3D picture for accelerated underwriting. They need to replicate greenfield and startup methodologies that transform data accessibility into improved experiences. And they need to reach growing and untapped markets with products that sell at the point of life experiences, instead of relying on traditional sales tactics.

See also: Underwriting Wildfire Takes Extra Care

Industry status quo is no longer an option. Your customers, particularly the younger generation who will be your dominant buyers, are expecting all of this and more. They want a customer relationship with a broader value to make their lives better across life, health, wealth and lifestyle needs.

How do your strategies align to what customers want? What plans are you taking to improve your odds of success? 

Are you ready? Your customers are.

For more insights on how you can end transactional thinking and begin capitalizing on customer life experiences, be sure to download, Rethinking Life Insurance: From a Transaction to a Life, Health, Wealth and Wellness Customer Experience.

How to Thrive Using Emerging Tech

Early adopters of artificial intelligence (AI) and machine learning (ML) are able to sift through massive amounts of data and use it to enable various capabilities. These range from making decisions about how to triage a claim using algorithms to improving a customer’s overall claims experience using more data and sources automatically pulled in from AI and ML methodologies. 

But where does the rest of the industry stand with these new capabilities? We released a study around how the top 100 U.S. carriers are benefiting from AI and ML and the challenges and opportunities for an AI-driven future. We found that 75% believe proper implementation of AI can provide carriers with a competitive advantage through better decision-making. 

While only 62% say the carrier they work for is already applying, piloting or planning AI and ML initiatives, these early adopters are already seeing significant AI and ML benefits. In terms of improving the experience for existing customers, insurers are experiencing advantages with faster claims settlements (88%), improved fraud detection (87%) and better risk scoring (85%). On the prospecting side, AI and ML are enabling early adopting insurers more customized and targeted opportunities for cross- and upselling (88%). 

Of the survey respondents representing insurers that are early adopters, most come from the 20 largest U.S. carriers, but adoption across the remaining top 100 U.S. carriers is also rapidly increasing.

While carriers are generally positive about their use of AI and ML, implementation does come with its own set of challenges surrounding staffing, data and compliance. 

The challenges around AI and ML adoption 

Insurance carriers are largely positive about the value of their AI and ML initiatives, but the study identified the challenges they will need to overcome. Staffing challenges are a major concern. According to the study, nearly half of the respondents (49%) said that AI and ML implementation has already affected their staffing plans today. Insurers need people who can understand the inputs and outputs of the applications, and who can explain them to the business. They need knowledge managers who can speak in both technical and non-technical languages and link the dialogue between parties.

See also: Stop Being Scared of Artificial Intelligence

Another major concern is the ability to access high-quality, trustworthy data. The three main issues with data that survey respondents mentioned include their ability to manage the volume and security of the data; linking and normalizing data across different data sources; and ensuring access to the data. Adopters clearly see the value of third-party data, as a majority of the adopters (82%) say their organizations have or will buy external data for their AI and ML initiatives. 

The third concern we found is around compliance and regulatory challenges with insurers’ use of AI and ML. Adopters worry that regulators and legal bodies may not understand AI and ML applications and could possibly block or limit them. Nearly three-quarters (74%) of adopters also have concerns about data privacy, security and ownership issues, anticipating increased regulatory scrutiny as more data sources are accessed and modeled.

Although the COVID-19 pandemic has slowed things, 95% of personal lines insurers are moving forward with their overall technology plans and investments, with only 5% retrenching, according to Strategy Meets Action (SMA). Meanwhile, 75% of commercial lines insurers are moving forward with their overall technology plans and investments, with only 25% retrenching or pausing. 

See also: Step 1 to Your After-COVID Future

Despite these challenges, the early adopters of AI and ML are already seeing benefits. Faster claims settlement, more targeted cross-selling and upselling, improvement in fraud detection and better risk scoring are just a few advantages that insurers are leveraging. As insurance carriers look to implement emerging technology, they should find a technology partner that has a deep understanding of the data, analytics and insurance industry to help them maximize their AI and ML initiatives. In particular, they should look to find a partner with a demonstrated expertise in building models that leverage advanced analytics and that have extensive experience in managing, normalizing and analyzing increasing volumes of data. By this time next year, only those insurance carriers that are fully embracing and implementing AI and ML capabilities now will have that competitive advantage.

For additional insights and data from our study, you can turn to our white paper, The State of Artificial Intelligence and Machine Learning in the Insurance Industry.