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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.