Insurtech AI: Why automation is the future of insurance

September 02, 2022

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From underwriting to risk assessment and data management, automation innovation is the new superpower for insurance companies

Automation is a term thrown around with abandon these days. Indeed, everything seems to include an element of it, and those that don’t are seen as slightly archaic and of a quaintly bygone era.

Put simply, automation – a process driven by machine learning and AI technologies – streamlines processes, enabling them to be carried out much faster and with far greater accuracy. For an industry like insurance, where the bulk of the donkey work has, for centuries, involved trawling manually through data to decipher results, automation is seen as the silver bullet to the industry’s inefficiencies.

In the world of technology, everything moves with lightning speed, and the sheer volume of automation technology adoptions since 2020 within the insurance industry has been staggering. Even compared to 12 months ago, companies are operating within a very different business climate.

Paul Donnelly, Executive VP EMEA at Munich Re, explains: “The pandemic and our societal response to it will have a lasting impact. Just look at how we have moved en masse to contactless and cashless payments, whether you measure that in terms of the increase in the payments themselves or the rise of digital banking, or even the drop in ATM withdrawals. Let’s face it – when did you last hold a cheque in your hands?”

Donnelly says the insurance industry is no different. In fact, it’s front and centre in this move to the digital world. The pandemic has brought a renewed focus among insurers to progress their plans for implementing digital solutions, and we have seen a significant leap forward.

Adoption of automation is accelerating

Automation is now not only widespread, but is in fact what makes access to life insurance possible for many, according to Donnelly – “whether that be on the application or enrollment side, with automated underwriting in Europe; or on the benefits side when accessing Paid Family & Medical Leave (PFML), Paid Family Leave (PFL), or short-term temporary Disability Insurance (DI) in the US, or Personal Accident Insurance in Australia, as examples.”

He believes automation lends itself most naturally to customer-facing data collection processes. “The application and onboarding process – with its attendant collection and management of structured data concerning the applicant, the onboarding and distribution paths followed, and the underwriting philosophy applied – naturally lends itself as the first point of adoption. But, there is a sense of ‘start as you mean to go on…’, in which I see the data collected by the insurer at the beginning of the consumer's journey with them being used to automate, speed up and clarify the insured’s later interactions as they make adjustments or claim to access benefits.”

Why customers love automated insurance

In a nutshell, automation makes the customer experience much less complicated while increasing accuracy. The easier a process is, the more traction it has in the marketplace.

Brian Mullins, CEO of the Mind Foundry, explains that automation technology is a logistic necessity. “The amount of information currently available on customers has reached unprecedented levels and humans can’t process it alone. AI enables insurers to uncover subtle patterns in data, giving human insurance agents insight into emerging trends, opportunities and threats.”

He states that though the benefits of these huge data sets are widespread, most important of all, they enable insurers to offer hyper-tailored and flexible products. “For instance, an insurer might provide information on the safest route to take to work, or how and when a customer should drive to reduce risk. Taking this one step further, usage-based insurance (UBI) in car insurance uses the driver's real-time location to cover them on demand, offering a price adjusted for the risk they incur at the moment. For example, companies like Marmalade allow their customers to add temporary users to their car insurance for a specific length of time.”

“Automation can speed up interactions between the insurer and the insured, make sure that nothing is missed or delayed, and ensure that the insured is not repeatedly asked for the same information at multiple steps in their journey,” concurs Donnelly.

Megan Bingham-Walker, Co-founder and CEO at Anansi Technology, the London-based automated shipping insurance platform, agrees. “My favourite current examples of innovative use cases for AI within the insurance industry are those that directly benefit the policyholder.”

Bingham-Walker cites a number of examples with regard to these innovative use cases, including: precise risk scoring used by Flock Cover; streamlined claims processing using computer vision utilised by Bdeo; and the best-in-class customer success experience via natural language processing used by Lemonade.

She says: “Initial use cases tended to focus on fraud detection, which was a US$4.1bn market in 2018, growing to US$10bn in 2025 – a huge problem within the insurance industry, as was seen with Shift Technologies and sprout.ai. Over time, however, we are likely to see a wider use of AI more broadly across the full end-to-end insurance value chain.”

AI, ML and automation will shape the future of insurtech

The insurance industry has often been accused of slow innovation uptake, but now the benefits and necessity of automation are clear, the future for the industry is rooted in technology.

“After a long period where insurtech was ‘nibbling at the edges’ of our industry, today it has taken centre stage. To paraphrase Brian Moynihan of Bank of America, the successful insurers of the future are clearly technology companies and need to be at the cutting edge of digital innovation. Insurtechs are the fastest and smartest route to that goal,” says Bingham-Walker.

“In 10-15 years’ time, I see the insurance industry as having almost fully automated, AI-assisted end-to-end processes. Ranking algorithms will ensure that product recommendations will be precisely tailored to a policyholder’s needs. Very few questions will be asked during the onboarding process because natural language processing will be applied across diverse, pertinent data sources from public databases to social media data to support the initial risk assessment. This will both streamline the process, reduce repetition and human data transposition errors during the application process.”

Mullins agrees: “Human-AI collaboration is essential in helping mitigate the risks of AI. This involves developing AI systems that understand their limitations and when they need human guidance, as well as how to communicate with other agents and humans. This creates a much more intuitive and efficient relationship between humans and AI – and ultimately a more responsible and ethical outcome for the lives affected.”

Bingham-Walker concludes: “AI-supported risk profiling will feed into real-time dynamic pricing decisions. When claims arise, the processing will be largely automated, supported by machine vision and natural language processing.”