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 (Rise of the Insurtech – Accenture report). The increased interest and investment in AI is largely due to the prevalence of human interaction and manual processes throughout the insurance value chain.
As a result, AI has the potential to impact each aspect of the value chain, all the way from policy pricing to reinsurance.
This is one of the many changes our industry is learning to navigate through. As a re/insurance professional I think it is important to track the progress of these changes and even find ways to be part of it (through Cookhouse Lab for example). To help you stay up-to-date I am kicking off a weekly series on AI to discover the impact on each aspect of the value chain. The first article of this series focuses on the underwriting and risk assessment segment of the value chain.
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Shift in underwriting data
An insurer’s ability to underwrite risk effectively, is the ability to assess all risk factors present in any given policy and to price those risk factors accordingly. It plays a crucial role in the value chain. Spurred by the advent of intelligent technology, underwriting has moved from a manual process predicated on dated historical data gathered with a handful of data points, to an automated process that uses real-time data from thousands of sources.
Leveraging social media data for underwriting
The UK based Digital Fineprint, who recently partnered with Metlife, is a perfect example of an Insurtech start-up leveraging AI to access a previously vastly untapped data stream: social media.
Digital Fineprint allows insurers (with customer permission of course), to trawl through sites like Facebook and Instagram to flag information that can be used to determine risk factors, such as an applicant’s physical shape and smoking habits. In addition to identifying these risk factors, the data gathered by Digital Fineprint’s AI gives the insurer the ability to effectively manage their relationships and the potential to unlock countless other cross-selling opportunities.
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Imagine the scenario of an insured sharing the exciting news on Facebook that his wife just gave birth to their first child, a baby girl. With the help of Digital Fineprint, the insurer is now able to automatically identify a cross-selling opportunity and reach out to the insured to add a life insurance coverage to his existing auto policy.
Using AI to improve underwriting risk assessment
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.
Through this process, an insurer might identify that an insured’s proximity to a fire-hall is not highly correlated with commercial claim rates. In addition to these capabilities, Tyche can continuously improve its claims likelihood model by using machine learning, which is a method of AI that enables a computer program to analyze data to continuously adjust future behaviour based on previous analysis. Adopting platforms like Tyche will allow insurers to propel underwriting risk assessment accuracy and greatly improve their bottom lines.
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