At a startup’s birth, an idea or a vision can be so well wrapped in a blanket of buzzwords that not even the smartest investors can stop themselves from throwing money at it. Let’s be honest: Most groundbreaking ideas deserve a sober share of criticism, but once a company gets its initial funding and the team goes into execution, continuing to feed the same party line has to be called out.
Some of the buzz floating around the insurance industry is artificial intelligence (AI). When I say “buzz,” I mean product messaging like this:
“Our [insert product name] uses artificial intelligence to help find you the best insurance coverage.”
Or, better yet:
“Let artificial intelligence handle your insurance claims from the initial call to the fund’s disbursement.”
One of the insurance industry’s sore spots is claims — from the first notice of loss (FNOL) to the claim’s payout. It is ridden with fraud, inefficiencies and lack of transparency. Many of us have experiences spending hours on the phone with customer service describing what happened, going from one shop to another to get several repair estimates and waiting for the reimbursement check to arrive.
It gets worse when dealing with home insurance claims. There is always a caveat to what is covered and what is not covered.
Thus, filing a claim turns into a never-ending argument with the insurer and a big out-of-pocket expense. This surely sounds like a perfect candidate to be saved by an AI knight in shining armor riding on a machine learning horse.
Let’s take a look at auto claims, which are relatively standardized and straightforward compared to home damage claims.
I cringe when I read that certain insurance companies are “using the latest AI technology” to help resolve claims in seconds or minutes rather than weeks or months. Without digging deep under wraps of each insurer’s statement, I believe that, at best, the usage of AI is limited to identifying some of the damaged parts.
Here is how a typical AI computer vision system works: It analyzes (using neural networks) thousands of images (let’s say, a rear bumper) and uses proprietary mathematical models to come up with a certain confidence level upon looking at a new picture of what is supposed to be a bumper.
Once the system has analyzed the image and determined with some confidence level that it is indeed a Ford Mustang rear bumper and the damage is “medium,” the platform can match all the previous Ford Mustang rear bumper repair jobs that were classified as “medium” and suggest how much it would cost to fix the damage (whether to repair or replace). Then it can suggest a course of action based on the deductible or coverage details.
I recall my own “minor damage” claim experience. I happened to hit a turkey while driving 65 mph on an interstate. Besides losing one side marker lamp and acquiring a few bumper dents, I couldn’t see anything serious. I called my insurance company and filed a claim. When my insurance company adjuster visually inspected the damage, his estimate was about $1,650. I took my vehicle to a body shop, and the mechanic lifted the car to inspect it properly. Guess what? He found more damage and had to invite the adjuster back to look. The final bill ended up being around $4,500.
Would this number have been know without a human looking more closely? Could AI have found all that hidden damage that an adjuster with 30 years of experience didn’t see?
While some insurance companies claim AI technology provides them with immediate total loss and repair estimates based on photos and, thus, saves everyone time and expenses, I am personally skeptical. Yes, this would have made my FNOL experience superb. I wouldn’t have spent time on the phone, driven to an adjuster’s shop or waited for his initial inspection. I would have just received an ACH transfer to my bank account for $1,650. The “fun” part would have begun once I brought the car to the mechanic, who then would have had to schedule an appointment to get the adjuster over to approve more funding, and I’d be waiting again, this time cursing my insurance for not being able to get it right the first time.
Insurance companies watch their claim payouts like hawks. It’s no wonder why. In my experience, overall claim expenses are 35% to 45% of their total expenses. So if AI will begin to overpay, an insurance company will be underwater in no time. So naturally, they would “tune” AI to underpay, resulting in additional adjuster visits, thus defeating the purpose of AI to begin with.
Do I believe AI will be capable of replacing human adjusters or making entire insurance claim processes “touchless”?
Maybe in the near future, perhaps in 5-10 years.
For now, adjusters, customer service agents and underwriters are safe from the incoming AI doom-and-gloom predictions.