As technology becomes more and more advanced, industries across the spectrum are feeling the disruption from digitisation and automation – and insurance is no different.
But while the industry is becoming better educated on insurtech and its potential benefits generally, artificial intelligence – or AI – specifically, seems relatively unexplored.
“The power of AI is in its ability to keep sifting through enormous amounts of data, continuously learn from patterns and use these to project scenarios and suggest actions,” Heidi O’Leary, principal consultant at Market Gravity, a proposition design consultancy, told Insurance Business.
AI enables insurers to assess risk more accurately, as they have increasing amounts of available data and increasingly sophisticated, learning algorithms to analyse it, O’Leary explained.
This enables pricing of individual risks at a more granular level, leading to increasingly personalised and targeted pricing, she added.
But about the potential risks?
“The proliferation of these technologies also creates added complexity in understanding risk, which may become a major differentiator between the winners and losers,” according to O’Leary.
Citing self-driving cars as a major area of AI that will affect insurance, O’Leary said that as the take-up of self-driven vehicles increases, so too does the potential for complex situations on the road.
“Therefore understanding how to assess and price risk on our roads with a mix of self-driving vehicles and traditional vehicles will be a specific challenge for insurers,” she said.
But whilst there are significant challenges in embracing AI, it also has the potential for huge rewards.
“When it comes to estimating losses for an insurer, AI can be used to quickly understand the potential impacts of events around the world,” O’Leary said, explaining that it is able to take unstructured data from sources such as social media, weather and traffic reports, real-time securities feeds, and emails, and analyse them in real-time to predict scenarios and evaluate impacts – for example, claims resulting from severe weather events.
App Orchid, for example, uses AI to enable advanced predictive analysis simply by typing in a question, such as: What are the chances of a terrorist act in Los Angeles during the month of November?
And AI could even help insurers in the ongoing battle against fraud.
“Machine learning has been used in the financial sector to prevent fraud for a number of years now,” O’Leary said. “AI-enabled systems can shift through vast amounts of external and internal data to detect anomalies and fraud-related patterns such as duplicate claims, learning each time an event leads to a confirmed or false case of fraud.”
So it would seem that the possibilities are far-reaching. But is the industry ready yet?
“Insurers have generally been accused of being laggards when it comes to innovating and adopting new technologies,” commented O’Leary.
However, she said that there are positive signals from some insurers that the consultancy has worked with recently, that indicate an ambition to venture outside of the traditional ways of doing business.
But O’Leary admits: “Adapting their systems to the world of AI and open data sources and APIs requires a shift in mindset.”