"Recently, Lemonade hit a speed bump in its journey as a visible disruptor and innovator in the insurance industry when a privacy class-action lawsuit over its alleged collection and use of biometric data was filed on August 20. I am not privy to any details or knowledge about the case or what Lemonade is or isn’t doing, but the Twitter event and public dialogue that built up to this moment brings forward some reflections and opportunities every carrier should pause to consider."
Anthony Habayeb co-founder and CEO of Monitaur, an AI governance software company.
Habayeb points out that far more than technology is involved as Lemonade has experienced. It is all well and good claiming that 90% of auto claims will be handled virtually before long. We know that virtual inspections have been manna from heaven during the pandemic but they have been guided by professional expertise. They have been remotely managed rather than by onsite visits with hoary engineers and inspectors ready to look into the parts that the claimant or repairer would rather hide.
"Pay attention, AI innovators; if we don’t more intentionally engage and address the risks of algorithmic systems and our intended use of consumer data with the public and regulators, we are going to hit a massive innovation speed bump. If all we do is talk about “black boxes,” facial recognition, phrenology, and complex neural networks without also clearly investing in and celebrating investments and efforts in AI governance and risk management, the public and regulators will push pause.
Media coverage and dialogue about AI’s risks are getting louder. Consumers are concerned, and in the absence of more proactive industry messaging about responsible AI efforts and consumer-friendly visibility into how data is being used, regulators are reacting to protect individuals."
This is backed up by recent research.
While the shift to tech-forward insurance processes is underway and the industry shows a willingness to embrace the cutting, if not bleeding, edge, new consumer research is showing auto and home insurance policyholders aren’t quite confident in a fully automated experience, according to a survey from Policygenius Inc.
More than 70% of consumers said they would be uncomfortable purchasing insurance without speaking to a person, while 64% said they’d be uncomfortable filing claims on a website or app without human interaction. This sentiment was expressed across cohorts, with 66% of policyholders age 25-53 saying they were uncomfortable purchasing insurance online without talking to a human. Nearly 80% of consumers 55 and older said the same.
Further, 60% said they would rather switch insurance companies than let artificial intelligence (AI) review their claims, Policygenius reported. Just 17% of consumers were comfortable with a home, renters or auto claim being reviewed exclusively by artificial intelligence.
In addition to AI, policyholders are voicing unease with data-rich big tech companies, such as Amazon, getting into the insurance game. When it comes to big tech offering home or auto coverage, 67% of survey respondents said they aren’t comfortable with the idea. For 40% of consumers, the idea makes them very uncomfortable.
NUPropertyCasualty 360 in Consumers voice unease with AI-only home & auto claims
This is not to imply carriers should slow down innovation projects leveraging AI. if anything they should pay more attention as it takes time to work these issues through. And by working them through you can address the concerns consumers and regulatory authorities have.
While involving AI/ML in the complex process of insurance claims now might be piecemeal, the future is bright for insurers to speed time to claim resolution by using image-based data and machine learning models to understand the scope of damage to vehicles or eventually, entire geographic regions.
"The timing couldn’t be better. With climate-driven catastrophes increasing in frequency, leaving areas difficult to access for humans to understand the extent of insurance claims, bringing rich models with satellite and other data to bear makes sense. But as with other areas of the economy that have long since had many humans in the loop, making AI the trusted source for claims might take some time.
There are a few things that might happen over the five years. First, we might expect large insurers to kick the tires with AI using standalone startups (like Tractable) as a proof of concept. If the time to claim completion improves dramatically, we could see acquisitions of those teams and talents to bring that capability in-house as a strategic asset, or to see insurance companies hiring their own legions of computer vision experts to streamline the claims flow."
Nicole Hemsoth in NEXT-GEN INSURERS ARE GOING TO NEED (WAY) MORE AI HORSEPOWER
For our purposes here at The Next Platform, of course, the real question is what an AI-driven insurance industry might look like from a systems and software standpoint. Given the uniqueness of claims in any arena, vehicle or land, having a go-to training set that can be retrained seems a difficult task. For Tractable’s car insurance claim arm, retraining on models of a wide range of vehicles, components, and damage types is one thing. For specific geographies, it might get quite a bit more complicated, pushing the need for ever-increasing training investments.
In the case of automotive, as the company enters new markets, it encounters cars with different design features, even among vehicles of the same make and model. There are other variations that also have to be accounted for, including different methods used to fix vehicles in different countries. All of this must be learned by the AI for each territory that Tractable takes its service to – making training and model refinement an everyday activity at Tractable.
Right now, for its car claims business, the company is using AWS for production AI training and inference runs. “We’re using something like 60-70 GPUs for model and training and research, then another 40-50 in production,” Ranca says.
It shows the value of working with companies that have the resources, vision and global reach to manage the complexity of insurance across different markets.
And technology partners that have the data management processes, compliance, security and global experience to face the issues we have raised above. Not for nothing do technology vendors like CoreLogic and Verisk label themselves ecosystem partners. They might lack some of the snazzy front end eFNOL and straight-though processing (STP) capabilities of digital claims platforms like RightIndem and Synergy Cloud but they can integrate these into an overall data model and technology ecosystem that will satisfy regulators, consumers and carriers.
Carriers need the capabilities of these technology partners to both innovate, anticipate lawsuits like that Lemonade face and satisfy the concerns of consumers and regulators. They can help deal with this dramatic announcement.
"In addition to helping police arrest the wrong person or monitor how often you visit the Gap, facial recognition is increasingly used by companies as a routine security procedure: it’s a way to unlock your phone or log into social media, for example. This practice comes with an exchange of privacy for the promise of comfort and security but, according to a recent study, that promise is basically bullshit."
End-to-end claims management platforms promise to triage all the activities and participants from policy holders and witnesses to claims adjusters, supply chain partners and brokers. There are so many identities that have to be verified to ensure security is maintained. The single sign-on process, and some digital platforms do not even offer that, must be robust.
The insurance industry is no different than others where the value of field employees making decisions is less efficient and in some cases, accurate, than what a well-trained model can deliver. However, what makes insurance different is that there is no one-size-fits-all model that can be optimized or added to for nuance when branching into the property domain. AI will have to get better, but so too will the sources of data for an ever-expanding cadre of calamities beyond mere auto claims. This also means hardware will have to be more affordable and efficient. Larger, more complex, varied models mean big training times. We’ll keep an eye on the ROI, as always.
Which is why it is wise to licence rather than build to put the digital horsepower behind leveraging AI.
Being a disruptor is hard. It requires taking disproportionate risks, pushing the status quo, and — more often than not — hitting speed bumps.