Tag Archives: auto claims

Getting to ‘Amazon-Like’ Auto Claims

Last week, we debuted our “Connected Insurance” webcast series with ““DISRUPTION: Technologies Transforming the Industry.”  I had the pleasure of serving as host and moderator, and by all accounts the session was well received. 

Our underlying theme for the session was how digital technologies, including AI, are transforming the insurance economy. More broadly, our expert panelists shared their perspectives on the state of digital transformation across the auto insurance ecosystem and views on  the technologies poised to revolutionize how the process works near- and long-term.

Participants represented four key inter-dependent industry segments:

  • Collision repairers/MSO (Matt Ebert, CEO, Crash Champions) 
  • Auto insurer claims management (Scott Kohl, AVP Claims, Kemper)
  • OEM parts distribution (Dan Ducharme, senior manager, wholesale parts, VW America)
  • Insurance economy platform provider (Marc Fredman, chief strategy officer, CCC Intelligent Solutions)

The premise of the session was this: A billion days elapse every year between when auto claims are opened and when claims are resolved. Consumers are losing patience with industries that don’t keep pace with modern experiences, and disruptors stand ready to deliver change. 

Several important observations became apparent as the discussion unfolded:

  • Technology has and will continue to transform how business is conducted in every segment; the rate of change is accelerating and presenting its own challenges, including shortages of skilled staff 
  • The new technologies having most impact across the entire ecosystem are telematics, auto photo inspection and computer vision, advanced driver-assist systems (ADAS) and electric vehicles (EVs)
  • The companies in all four segments are focused on exactly the same outcome but are achieving it in different ways – they seek safe and proper repair of damaged vehicles, resulting in a positive customer experience and ultimately brand loyalty and retention 
  • The collision repair industry has changed most dramatically over the past decade, driven by consolidation financed by private equity investors
  • Digitization of the thousands of steps involved in the auto claims process, and integration between segment participants, is enabling the customer experience associated with auto claims to begin to approach the “Amazon standard” 

At the industry segment level, the following panelist comments struck me as particularly insightful and informative:

Collision Repairers/MSO

Matt Ebert noted that the new technologies are definitely making the “front end” of the auto claims process faster, but the offset for collision repairers is that the greater complexity of vehicles (e.g., ADAS) creates new challenges for repairers; even what used to be a simple bumper repair may now involve scanning and recalibration).  

Matt also pointed out that repairers are affected by the large number of different repair requirements from carriers and OEMS and often find themselves “in the middle” of the dynamics that exist between some insurers and OEMs. On the subject of OEM certified repair networks, Matt said that, while his participation in these programs helps brings some incremental repair volume to his repair shops,  particularly for higher-end vehicles, to him right now participation is more about credibility with insurers and consumers.

See also: Key to Transformation for Auto Claims

Auto Insurer Claims Management

Scott Kohl made the point that what was only recently thought of as innovative in auto claims is now table stakes and that planning cycles that used to be in the five-year range are now only two to three years. He added that there is a constant need to be ready to pivot quickly in response to events and market changes. He also stressed the importance and value of using AI to connect the supply chain for multiple use cases including automating accident management triage, parts ordering from real-time first notice of loss (FNOL) and total loss identification and resolution as that percentage continues to rise toward 25%.

OEM Parts Distribution

Dan Ducharme also referenced automated parts ordering using on-board vehicle telematics to pre-position parts inventory based on vehicle and damage detail and geo-location to reduce overall “keys to keys” time for customers. He also reinforced that auto makers are focused on keeping customers for life, which further emphasizes the importance of customer experience and satisfaction.

Insurance Economy Platform Provider

Marc Fredman pointed out that while some of us talk about emerging technologies and future auto repair process capabilities, a good deal of it is happening and available today; he indicated that CCC has 30,000 customers using their technology across all segments represented here. Marc also indicated that the building blocks for an Amazon-like experience exist today, as exemplified by the CCC Engage and CCC Car Wise solutions in use in approximately 25% of their shops for sharing photos and estimates between consumers, insurers and shops and enabling insurers and consumers to schedule repair appointments. He said that over 200 million shop calendar entries have already been made in this way. Marc also said that insurer adoption of digital solutions is significant, with some insurers managing 25% to 30% of assignments. Other examples of digital building blocks already in market include 50 million text messages that employ AI and enable insurers to better manage claims intake and provide text messages to consumers tracking repair status and “ready for pickup” notices.

The Amazon-like Auto Claim Experience

Having worked in the claims technology space for more than 35 years, I find it stunning to look back and realize all the improvements that technology has already enabled. It is even more exciting to realize how much more improvement can be expected and see the rate of that change accelerating. This change is driving collaboration and cooperation between key participants in this extensive supply chain to a level until now unimaginable. 

The final pieces of technology required to achieve the long-elusive goal of delivering straight-through-processing of auto claims are now falling into place. A couple of prime examples include artificial intelligence and computer vision to assess damage and produce estimates from photos and digital claims payments speeding settlements to insureds, vendors and lenders. 

A truly Amazon-like auto claim and repair process is finally at hand.

AI and Its Impact on Automotive Claims

For more than six decades, innovators have attempted to unlock the full potential of artificial intelligence (AI). It wasn’t until the past decade that the science finally caught up to expectations. Today, the AI market is on track to reach $500 billion by 2024. COVID-19 has fast-tracked AI adoption and acceptance.

McKinsey & Company says that “insurance will shift from its current state of ‘detect and repair’ to ‘predict and prevent,’ transforming every aspect of the industry in the process.”

See also: Key to Transformation for Auto Claims

AI-enabled solutions have opened up new possibilities for auto insurers and collision repairers. From detecting a car accident with IoT technology, to instantly processing a payment for completed repairs, the opportunities are endless. First on the list for most carriers, however, is using AI to automate the appraisal process and produce a “touchless” estimate. This can improve efficiency, shorten cycle time and meet policyholder expectations for a streamlined, digital claims experience. Now, thanks to these four trends, creating that experience is within reach.

1. Shifting Methods of Inspection

Prior to COVID-19, virtual estimating was reserved for low-severity claims. However, the need for social distancing during the pandemic and changing consumer demands spurred the adoption of virtual inspection methods. In April 2020, Mitchell data shows that the use of virtual, or photo-based, estimating more than doubled from earlier in the year. Just one year later, LexisNexis Risk Solutions reported that virtual claims handling has now “settled to a level of a little over 60%.”

This shift opened the door to the long-term aspiration of “touchless” claims and leveraging AI in the appraisal process. Over the last year, virtualization—considered the first level of automation—has resulted in estimate efficiency and consistency gains. From images, appraisers can complete approximately 15 to 20 estimates per day versus three to four out in the field. This has prompted more carriers—nearly 70%, according to LexisNexis Risk Solutions—to embark on the claims automation journey.

2. The Prevalence of Big Data

According to the Center for Insurance Policy and Research, “The successes of AI are also being facilitated by the massive amounts of data we have today. The wealth of data we now create is astonishing, and the speed at which data is generated has only made data management tools like AI even more important.”

The property and casualty industry has always thrived on capturing, analyzing and interpreting data. Whether it’s from mobile devices, automobile IoT sensors or other sources, this data gives decision makers the information necessary to personalize customer interactions and address issues. When it comes to touchless estimating, though, data alone isn’t enough. Access to a comprehensive library of vehicle, repair and historical claims information is needed—along with the ability to quickly interpret that information using AI. In the case of Mitchell Intelligent Estimating, claim details and images are collected. AI then analyzes the data, comparing it with Mitchell’s comprehensive library of vehicle and repair information that spans more than 30 years. From there, the machine-learning algorithms translate the output into component-level estimate lines for appraiser review and approval.

3. Human-Machine Collaboration

Just as humans continually learn and improve, so do machines. As highlighted in Insurance Thought Leadership, “good machine learning systems involve feedback loops…. By letting the machine know what happens on the ‘real world’ side of things, machines learn and improve”—no different from claims adjusters!

Support for a human-machine feedback loop is critical to automating the claims process and can lead to vast improvements in speed and accuracy. An appraiser’s feedback helps teach the machine to make better decisions. As AI-powered solutions remove repeatable tasks, employees have more time to focus on complex claims that may require extra scrutiny.

4. The Growth of Cloud Computing and Open Ecosystems

AI’s dependence on data increases the need for cloud-based systems that can access and aggregate vast amounts of information, making it available from anywhere. These systems help organizations reduce development and maintenance costs, enhance security and accessibility and improve speed, reliability and scalability.

Like cloud computing, open ecosystems are also vital to AI and touchless estimating. Open ecosystems allow AI to easily access data, analytics and software across platforms and providers, giving carriers the ability to create a cohesive, end-to-end claims experience. They also introduce flexibility and choice, PropertyCasualty360 reported.

See also: Designing a Digital Insurance Ecosystem

For instance, through Mitchell Intelligent Open Platform, carriers can select the AI that best meets their needs. That includes AI algorithms developed internally, provided by Mitchell or delivered through third parties such as Tractable or Claim Genius. The AI output is used to produce a partial or complete appraisal.

The Future of AI-Enabled Claims

By 2030, McKinsey & Company predicts that more than half of current claims activities will be replaced by AI-enabled automation. “Claims for personal lines and small-business insurance are largely automated, enabling carriers to achieve straight-through processing rates of more than 90% and dramatically reducing claims processing times from days to hours or minutes.”

With the science now ready to deliver on its decades-old promises, the auto insurance industry has reached a turning point. Carriers can either invest in AI or run the risk of being stranded by the side of the road. Ultimately, organizations that embrace this “new” technology to deliver a digitally driven claims experience will be best-positioned to gain market share and consumer loyalty.

Key to Transformation for Auto Claims

The word “transformation” is overused, and yet here in the auto insurance claims industry there is no better word for a process that is being changed so dramatically from beginning to end, and at every step in between. 

But real transformation, while claimed by many, is in reality only enabled by the exceptional few. That is because transformation occurs through a collective, inclusive effort, not a silver bullet technology. And complete transformation requires the active participation of the end-user, to ensure higher levels of acceptance and satisfaction. Transformation must be good for the business and the customer, or it will likely not take hold at all. 

Foundational to Success

Digital transformation is the essential driver behind how companies will add value and deliver services to their modern customer, a customer who expects and trusts digital interactions. AI is critical to processing and assessing all inputs and removing friction. Yet AI alone cannot deliver transformation. 

Let me explain.

We know data availability is increasing rapidly across multiple dimensions – volume, velocity and variety. In the last two years, more data was created than in the entirety of human history. This is a fraction of the data that will be available in the near future as connections continue to multiply, becoming increasingly bi-directional and informing virtually everything. Right now, there are more than 50 billion connected devices in the world, and connected cars are emerging as an important digital platform.

Artificial intelligence is the only way businesses can leverage the tremendous amounts of data available. AI synthesizes all of this data. This is positive and necessary. But AI output is often delivered to humans, reviewed offline and paused before actions occur. Companies have to eliminate this pause and disconnect in the process to transform their operations. AI decision-making must be digitally connected to operating systems or consumer interfaces or both to drive action and to create a truly elevated, digital experience.  

See also: Transforming Auto Claims Appraisals

Relevant mobile technologies, network connection management and industry-specific workflow applications are required to activate AI, automating tasks based on that data to speed up and simplify lengthy and complex processes. The auto insurance claims process is an ideal candidate for such transformation. Our industry needs to connect AI to technologies that drive action. 

Here’s an example of how a transformed auto claim experience can look to your policyholder when AI gets put into action with mobile and network technologies:

  • Pat enters his vehicle in the morning. The app on his phone activates and begins tracking his trip so that his auto insurance policy premium is calculated for only the time he is in transit, based on the policy he selected upon enrolling. 
  • On arrival at his employer’s office parking lot, Pat accidentally scrapes the side of his vehicle on a pillar. 
  • His vehicle and app automatically detect the incident and offer Pat the opportunity to submit the incident to his insurer to determine if a physical damage claim should be opened.
  • Pat decides to proceed and immediately receives a text link with instructions about how to take a few smartphone images of the damaged area and text them to his carrier. 
  • Pat is immediately notified by text that the damage is minor and that the car can be safely driven but that the repair cost likely exceeds his policy deductible by at least $500. 
  • Pat decides to file the claim and receives a text with a list of nearby repair facilities, including consumer ratings, shop certifications or specialties and availability. 
  • He taps a few links and schedules the repair, and once he arrives a pre-arranged temporary rental car will be waiting for him. 
  • Pat continues to receive status updates from his insurer until he is advised what time his vehicle will be ready for pickup or delivery, as preferred.  

Note that the steps described above begin and continue with AI-enabled decision-making and workflow management. Out of view of the policyholder, AI and digital connections are powering the parts ordering process, and the repair facility is digitally paid by the insurer within hours of the vehicle being delivered. Without these enabled technologies, a digital end-to-end experience would not be possible. But when combined with the other elements, the result is transformative, completely digital.  

Sourcing the Data That Powers AI and Drives Decisions

The relationship between the ability to reliably predict outcomes and the absolute volume of historical claims data leveraged to train the software is directly proportionate – the greater the amount of relevant data used, the better the outcome. We frequently hear from our insurance clients of all sizes that the volume of data needed to develop reliable algorithms is greater than even the largest insurers have available. CCC has processed more than $1 trillion of claims-related data, which we put to work to develop hundreds of actionable AI models. And while data relevancy is essential, another key difference in AI efficacy is the use of a combination of AI disciplines. Deep and machine learning and business rules combine to deliver the most reliably predictive, comprehensive results for faster, smarter resolutions. 

Here’s how: 

Deep learning is an AI method that uses historical data to inform which action is likely to lead to which outcome. Let’s take photo-estimating as an example. To train an AI model that can review smartphone images from a collision and predict whether a vehicle is repairable versus a total loss, the AI model needs to learn from historical data: photos of other car crashes, as well as the claims data that accompanies those photos regarding the parts, labor, cycle time and medical assistance needed for each claim. The question is: Does the AI model have enough historical data to make that prediction actionable? A few hundred images are helpful, but decades’ worth of wrecked car images and related metrics make the AI model far smarter. Another question: Can the AI model sort out the anomalies from the requisite data set? Can it learn from them?

Another key discipline is machine learning, which allows historical data to be influenced by behavioral or pattern changes that might make recent actions more likely to occur again. Let’s say you have been a Facebook visitor every day for the last five years, but more recently you’re only visiting Instagram. In this case, the majority of data would say you’re going to visit Facebook again, but recent activity would suggest Instagram is a better prediction. Why does this matter? Vehicles and parts are not static. New cars and parts are introduced continuously; if an AI solution is going to be effective, it needs to base predictions on data that can account for recent behaviors, not just historical data. 

A less sophisticated, yet foundation disciple, includes the use of rules. A rules-based approach can offer helpful predictions when historical data is not available or recent activity is not accurate enough to ensure a reliable prediction. Suppose that an inbound technical support email contains the word “urgent” in the body. A rule is triggered, and that email is forwarded to someone who can immediately act on it. These types of rules can get into extremely complex decision points, leading to hundreds of potential rules, some of which may even conflict with each other. This is why rules-based AI is an incomplete approach that can fall short in accuracy and reliability. Yet, because data and domain experience aren’t required to create rules-based AI, it is a helpful starting point that can assist companies to begin the journey of automating complex workflows such as auto insurance claims.  

See also: Auto Claims: Future May Belong to Bots

When It All Comes Together – A Reimagined Insurance Experience

When the claims experience is working in harmony as a result of automated, AI-enabled decisions and all the needed inter-company, inter-industry integrations, not only will the insurer’s customer’s experience be maximized but real hyper-personalization can be achieved, meaning that each insurer’s individual customer will enjoy an exemplary service experience in the manner and method that they expect and prefer. 

Industry transforming technology is here and ready to be combined in time to meet consumers’ evolving expectations. Insurers are in a position to connect AI, mobile and network to transform what’s possible.

Transforming Auto Claims Appraisals

The pace of digital transformation is accelerating in many industries due to COVID-19, and nowhere is this more evident than in automotive insurance. At the start of the pandemic, carriers and collision repairers had to find new ways to minimize in-person interaction between employees and customers. This resulted in the rapid adoption of virtual, or photo-based, estimating and served as a tipping point for claims automation.

Virtualization supports the need for social distancing, accelerates the claims-handling process, improves efficiency and increases customer satisfaction. According to LexisNexis Risk Solutions, 95% of auto insurance carriers are embracing virtual claims handling, with many setting their sights on touchless estimates. By 2025, the global data and analytics company predicts that more than 80% of claims processed will be virtual, and up to half of non-injury claims fully automated.

So how does this affect auto insurers? Although the pandemic has reinforced the benefits of virtualization, truly automated estimates — or touchless estimates — remain the ultimate goal. Achieving that goal, however, requires a technological evolution. Despite catalysts including business incentives, customer expectations and a global pandemic, the transition from onsite inspections to full automation will take time. 

Common Misperceptions

To understand the progression of claims automation, it’s important to first address misperceptions about the current state of touchless estimates. The belief that, today, fully automated appraisals can be accurately produced without human intervention is fiction. In fact, it’s comparable to recent exaggerated assertions about the prowess of self-driving vehicles. While advancements in technology have led to increased automation, the need for human oversight and intervention remains critical. Additionally, the regulation and infrastructure required to support an automated end-to-end system are still evolving.

The Pursuit of Touchless Estimates

Insurance carriers are on a quest for touchless estimates to stay competitive and meet increasing consumer demands for better response times and self-service capabilities. After all, an automated appraisal process is expected to deliver:

  • Improved efficiency and appraiser productivity
  • Greater estimate consistency
  • Better cycle times over traditional methods
  • Higher customer satisfaction

For this automated process to be successful, though, insurers must consider their unique workflow requirements. Unlike field estimating — where a one=size-fits-all approach may work — a scalable, automated appraisal solution requires an open, flexible and cloud-based ecosystem that can integrate with other business applications. Open ecosystems let carriers streamline operations and leverage best-in-class technologies that reduce the reliance on human effort while they build out an automated claims experience.   

See also: Digitally Challenged Miss Opportunities

Automation Trends Enhancing Efficiency

Three trends — or levels of automation — have marked the evolution from onsite inspections to touchless claims. These levels are not sequential; rather, they are being developed and deployed in parallel as the artificial intelligence (AI) used to automate estimates becomes more mature.

Virtual (Photo-Based) Estimating

Virtual estimating demonstrates the power of using photo capture to produce accurate assessments. Images and videos are put at the center of the appraiser’s workflow. Despite its efficiency, virtual estimating is primarily driven by humans, not machines. 

  • Vehicle owners start the claims process on their mobile devices and are guided through how to capture and share photos and video of damage.
  • AI is leveraged to organize and categorize the imported images as well as to detect and highlight the damaged sections of the vehicle. 
  • Appraisers rely on an application with a 360-degree view of the damage to write an initial estimate as if they were physically at the vehicle instead of a remote location.

Before the pandemic began, virtual estimating was used for low-severity claims. However, it accounted for just 6% of all estimates written in the U.S. and 4% in Canada at the start of last year, according to Mitchell data. By April 2020, those percentages more than doubled.

Field appraisers can typically complete three to four onsite inspections per day when factoring in administrative tasks and travel time. Working from images, however, allows an appraiser to finish approximately 15 to 20 estimates per day. 

Guided Estimating

Human-machine collaboration is the next step in the progression from handwritten to machine-written estimates. Appraisers are guided by the AI through each decision. The goal, of course, is to empower the appraiser while leveraging the AI for useful recommendations. 

At this level of automation, the machine is becoming much more involved in the process. While appraisers ultimately remain in control, the information and decisions presented to them are delivered by AI with each sequential line of the estimate suggested for their consideration. Guided estimating extends beyond virtual estimating by:

  • Driving a set of AI predictions that recognize the damage to components
  • Transforming these predictions into repair line recommendations
  • Surfacing supporting evidence and empowering appraisers to make changes based on their own expertise
  • Delivering a continuous feedback loop that relies on appraisers’ decisions to educate the AI

Automated Estimating

Touchless estimating is the final level of automation. This fully automated process is powered by AI using vehicle and claims data to generate all operations, parts selections and pricing. Predominantly machine-driven, the process works by:

  • First capturing claim details and vehicle content, like virtual and guided estimating 
  • Analyzing the information using computer vision and other machine-learning algorithms to translate it into component-level estimate lines
  • Pre-populating the entire estimate for review and approval by the appraiser

Additional data integration will help carriers further streamline the claims process beyond automating appraisals. For example, claims could be settled even faster by incorporating telematics incident reports, loan payoff amounts, titled/registered owner information and taxes and fees into the workflow. And LexisNexis Risk Solutions predicts that “by 2025, at least 40% of total loss claims will be settled within a couple of days instead of weeks.”

See also: 5 Keys to Transforming Underwriting

Where to Start

The transformation from field appraisals to touchless claims isn’t done in a vacuum or entirely by a machine. Appraisers are critical to developing and improving the process. If automation is introduced slowly, they can build their confidence in the AI while perfecting the results through continuing feedback. Creating an experience where the guidance is clear, actionable and transparent helps create trust between humans and machines.

Processes driven by human-machine collaboration take time, but they can lead to vast improvements in speed and accuracy. For auto insurance carriers, the time to act is now. After all, when it comes to meeting policyholder needs and staying competitive, the question isn’t whether to automate appraisals. It’s which partner has the experience and expertise to help you achieve your goals and support the evolution of your organization’s claims process.

Put Tow Professionals at Center of Claims

Insurance carriers have put a lot of resources into optimizing the auto claims process, and they’ve made significant progress. The industry cut the average amount of time it takes to return a repairable vehicle to the customer after the first notice of loss from 13.5 days in 2018 to 12.9 days in 2019, according to J.D. Power, and then to 10.3 days in 2020, though the 2020 gains were at least partially due to fewer cars on the road during the pandemic.  

Many insurers now provide AI-powered claims technology that enables the consumer to report an accident and supply the information needed to settle the claim without ever leaving home. Unfortunately, even with these tools, consumers still take an average of four days to report the accident and another three days to deliver the required images that allow their carrier to use these new AI tools for their claim. That’s seven days of costs — car rental fees, storage charges, etc. — and a significant delay in settling the customer’s claim.

Obviously, the vast majority of consumers want to get their cars back in working condition as soon as possible. So what explains these delays?

The primary issue is that motorists are not in the best position to capture accident information. Following an accident, they are often understandably dazed and focused on their health, the health of their passengers, interactions with law enforcement and the shock of the accident. They’re likely not thinking about taking exact photos of the damage to get their insurance company the data they need to start the claim. In the worst-case scenario, the motorist may be injured and completely unable to make a report.

If a vehicle is undrivable, it will likely be towed to a storage lot, and, many times, the claimant may take days to get the insurance company the information it needs to find and recover the vehicle, and also capture the photos needed for the claim. All the delays increase costs.

Likewise, customers may not be technically savvy. While many of us can use mobile app tools with ease, many others may not be able to navigate them without significant prompting. Even if they are able to use the tools, consumers are not experts in accident-scene images or insurance claims, so the photos they send may be incomplete or unusable, again causing further delays and increasing costs.

Enter the Tow Professional

Instead of relying entirely on consumers to self-report, insurers would likely see faster, more accurate reports if they were to partner with tow providers. After all, nearly all insurers now offer roadside assistance to their customers, so insurers will have a partner already on the scene for accidents that render the car undriveable, which are typically the most expensive claims. Plus, tow operators are in a far better position to capture accident scene information than are motorists. They’ve probably already worked hundreds, even thousands of vehicle accidents, so they’ve seen it all before; a tow operator is far more likely to be in a calm state of mind than the motorist. 

Additionally, tow operators work with vehicles and the damage they incur in an accident on a daily basis. The operators already know their way around an accident scene. Plus, as partners, they can be held to certain standards under a contract and required to undergo training to follow a repeatable process. A motorist is under no contractual obligation, will likely be filling out the accident report for the very first time and almost certainly will have never reviewed the process beforehand. And the more accident reports a tow operator files, the better the operator will become at taking proper photos and providing the required information. 

See also: The End of Auto Insurance

Of course, tow operators won’t be at the scene of every accident. If the vehicle is driveable, there’s no need for a tow, and not all customers will opt for the insurers’ roadside assistance program. But for those customers who do sign up, tow operators who are part of the insurer’s network will be on the scene for the most serious and costly accidents. By getting accurate information and photos the same day as the crash, carriers can reduce the amount of time the vehicle stays in storage and accelerate returning it to the customer or providing a settlement if it is totaled. As a result, the carrier not only saves money but increases customer satisfaction.

Tow operators are already on the scene. Insurers should leverage them to provide claims information and provide better outcomes for everyone involved.