A lot of people are talking about the promise of artificial intelligence (AI), and some say it’s too early to evaluate its long-term impact. I disagree. I believe we need to evaluate AI’s value now, because it’s already beginning to fundamentally change the way auto insurers do business.
A sweeping statement, perhaps, but there’s a lead-up to this discussion that is creating the perfect storm for P&C insurers.
First, insurer performance is challenging, and most every insurer I speak with is racing to identify ways to reduce expenses while continuing to offer desirable products to savvy consumers -- consumers who expect insurance to be delivered and serviced just as seamlessly as their interactions with their favorite online retailer.
Next, vehicle complexity is making it extremely difficult to price risk, predict frequency (largely due to advanced driver assistance systems, or ADAS) and understand increasing repair costs, thanks to enhanced electronic content, such as the sensors in newer vehicles.
In this environment, AI can play a critical role, helping insurers bring expenses back in line while creating opportunities to deliver a better insurance experience for consumers. And, as vehicles become more connected, streaming more data, the role of AI will only grow.
If you’re still not clear on what exactly AI is, it refers to programs that are capable of learning to make decisions more like humans. AI is at work all around us – when robots control other robots on the manufacturing line, intelligently automating the management and optimization of financial portfolios, detecting cancer using MRIs and machine vision and powering self-driving cars. In fact, AI is becoming so prevalent it’s expected to create $1.2 trillion in business value by the end of this year and $3.9 trillion by 2022
AI in Insurance
It's now our industry’s turn to put AI to work. What we’re seeing in other industries is now happening in claims. AI is being injected into key points in the claim process, helping to create value that can be seen (and felt) inside and outside the organization. Meaning, AI done right can yield improvements designed to enhance the experience of all stakeholders.
From an internal efficiencies perspective, consider AI’s impact on workflow challenges. As just one example, let’s look at the value of mobility and IoT, telematics in particular, because this is foundational to AI-driven improvements in processes. As you read on, think about all the existing processes and labor currently linked to your own auto claims area, because even the workflow that initiates a claim--in place for a hundred years--is now being changed, thanks to AI.
See also: Strategist’s Guide to Artificial Intelligence
The New Claims Workflow
There is a new claims workflow taking hold right now, not some point in the future.
First, policyholders won't call the insurer when they experience an accident—the insurer will contact him/her. This is because the insurer will apply AI to telematics data, setting an alert tagged to view the rate of change of the vehicle and determine in real time that there has been an accident.
Now apply AI-driven conversations via chatbots with customers at scale, in real time, to guide them through the claims process after that accident occurs. In our example, the chatbot asks the claimant for a photograph—an automated, back-end review determines suitability of the photograph, enabling the insurer to determine with high accuracy and in real time whether the vehicle is likely to be a total loss or repairable and advises the policyholder accordingly. Fast, transparent communication.
If the damage is repairable, the chatbot asks for additional facts and photos. The insurer detects location and severity of the damage by automatically comparing it against millions of collision variables and applying predictive, model-driven AI. Heat maps are used as visible illustrations of the damage, building credibility with your policyholder.
The internal workflow changes further when virtual inspections are powered by AI. Remote appraisers can be given photos, heat maps and even a guided estimating tool, reducing time and effort in the field and yielding higher accuracy and productivity, processing 15 or more estimates per day versus four to five estimates in a pre-AI, field inspection world. Once the estimate is written, information gleaned from the photographs is fused with insights gleaned from CCC’s wealth of estimating experience to determine if the estimate is in line with insurer guidelines. The appraiser views the pictures and, applying AI, builds out the estimate with interactive prompts to improve it.
Thanks to AI, the policyholder is given an estimate in significantly less time than is possible today. AI also fuels communication that is more transparent and consistent with consumer expectations. When a vehicle is repairable, the policyholder doesn’t need to wait impatiently for days while the claims and repair process slowly unfolds in ways they don’t know about or understand. Instead, a consumer has access to a host of chatbot and SMS technology, where messages communicate the necessary steps to resolve the claim. Similar to how we book a restaurant reservation, the policyholder can schedule a repair shop appointment; and like an airline that can notify us of flight status, repair status updates have become standard practice for shops.
Through the use of AI, services can be dispatched and intelligently routed to the repair shop of choice—or the salvage yard in the case of a total loss, saving time and money on additional tows and storage fees. From the policyholders' perspective, the insurer continues to prove that it has their best interests at heart, building trust and loyalty at a pivotal time in the relationship.
In other words, an experience is built around AI, putting it to work to benefit the consumer. And, the same thing is happening for the estimator.
On the casualty side, insurers handling first- and third-party claims can leverage AI to help inform investigations and increase loss cost management accuracy. For example, AI can detect the principal direction of force and the delta V to predict the likely physical injuries and outcomes of the vehicle’s occupants. There are early indications that integrating this data for analytics and intelligence purposes can improve claims outcomes, both by qualifying injury causation and revealing whether certain injuries are consistent with the facts of the accident.
See also: Why AI-Assisted Selling Is the Future
What I’ve just described is the tip of the iceberg. We are at the tipping point. Connected cars will drive another wave of claims innovation. According to IHS Markit, worldwide sales of connected cars will reach 72.5 million units in 2023, up from 24 million units in 2015. That means, in just over eight years, almost 69% of passenger vehicles sold will be exchanging data with external sources.
What does that mean for us?
If I’m looking into my crystal ball, here’s what I see:
When there's an accident, the amount of instantaneous information available to us will probably be 10 times what it is today. We won’t need policyholders to take the photographs I mentioned earlier. With telematics data, we will have all of the information that is knowable about an accident event, which makes the AI even faster and more accurate and claims management and related outcomes even that much better.
If the car isn't that safe, it will be picked up by a self-driving tow truck and taken to the shop while another self-driving car will come pick up the policyholder. By the way, at the shop, no one's going to have to order any parts; the parts will be ordered within minutes, maybe even seconds after the accident.
From an internal and external perspective, there is no downside to embracing AI’s promise: reduced claims costs, increased customer satisfaction and improved business outcomes – today and into the future. The value is there; the time is now.