The majority of this post was originally post on the Specialty Insurance Blog and is included below with updates.
AI (“artificial intelligence”) is a hot topic for many service businesses, and it is beginning to impact the insurance industry. How is it being used? What will the impact be? And how will it impact specialty lines? We do not know, but a series of articles provides some thoughts, if not insights.
AI, or artificial intelligence, is also called machine learning, cognitive computing and predictive analytics. We have compiled a number of excerpts from articles on AI in insurance and on InsureTech, and have included an interesting example of AI being utilized in an MGA. Please note that these perspectives on technology in insurance do not necessarily represent the opinion of this author.
The traditional insurance distribution system has gradually developed over the centuries into one where product sales are largely agency driven and the broker-insurer relationship is the predominant distribution model for the majority of lines. The proliferation of technological innovation means that primary insurers are able to more efficiently and effectively source insurance and underwrite directly with customers, lessening the dependence on intermediaries; a so-called “disintermediating” effect. One such advancement has been in the area of AI. AI is the operational processing and analysis of consumer data by sophisticated intelligent automation systems that, together with a series of algorithms, can emulate human behaviour and reconstruct human thought processes and intelligence. In other words, AI systems can carry out the work that previously required human intelligence.
Accelerated use of technology in the insurance sector is having both a disruptive and transformative impact on areas including product development, distribution, modelling, underwriting and claims and administration practice. The result is a new industry, known as InsurTech.
The hottest category of Insurtech in 2017 has arguably been artificial intelligence (AI) as 5 of the largest 15 deals have gone to AI startups. This builds off a strong 2016 that saw 45% of investments in Insurtech going to startups utilizing big data analytics and AI
75 percent of insurance executives agree that AI is about to dramatically reshape the insurance industry.
[Executives] described some of the AI initiatives already underway at their firms. They range from condensing lengthy engineering reports for swifter underwriting, reassigning some of the claims administrative services handled by offshore humans to robots, interpreting crop risk information delivered by drones, and deciphering communications from customers with heavy accents using natural language processing.
The MIT-founded Insurify is an auto insurtech that … has …launched a virtual assistant on Facebook Messenger. This virtual assistant acts as a broker, interacting with the consumer via Facebook Messenger to assess their cost and policy benefit preferences. These preferences are used to determine potential insurers for the consumer, which are then recommended based on the insurer’s customer service and product reviews.
Another example of an Insurtech startup deploying AI to improve underwriting risk assessment is the New York based Tyche. Tyche uses natural language processing to categorize the historical data of an insured and subsequently, cross-reference this data with claim experiences to uncover the factors that truly drive risk.
Digital distribution models are advancing beyond the price comparison website model to encompass the sharing economy, P2P features, artificial intelligence (AI), robo-advice, machine learning and advanced robotic process automation (RPA). These features give customers greater control over what products they can purchase and on what terms, often without human intervention, usually all through their mobile phone.
Carrier Management has an interesting and detailed article on the development and implementation of AI in an MGA (see here), including notes on a third party AI application called DataRobot. Some observations by the author:
- There are no one-size fits all, and your needs will change over time.
- Internally, we continue to prioritize predictions on fundamental questions to our business: pricing, underwriting, claims handling, fraud detection, customer engagement, operations, etc.
- DataRobot’s concept of applying machine learning to the model selection process (and data science in general), ended up being the right fit for where Atlas is today.
More recent commentary includes:
Recent research by Genpact, a global professional services firm that offers a modular AI-based platform, found that 87 per cent of insurers are investing more than $5 million in AI each year, and more than half are planning to transform many of their existing business processes over the next three years. (see here.)
Like most industries, the hype and excitement around the opportunities to leverage AI are increasingly common. Also like most industries, real adoptions of AI are slowly starting to trickle into the marketplace. On the insurance carrier side, we are seeing insurance companies embrace AI applications around claims processing…, alternative risk analytics…& marketing. (See here.)
The ability to analyze countless data points almost instantaneously creates new and exciting ways for insurers to assess situations and predict patterns that humans could not do on their own. But this doesn’t mean robots will be replacing humans anytime soon; ideally technology like AI and machine learning, if implemented properly, can free up humans from rote tasks like data entry to focus on the more high-touch and value-added aspects of customer service. (See here.)
AI, data analytics and other technology applications are working their way into the insurance business, but it is still early days.
Innovate Insurance – Innovation & Entrepreneurship in Insurance