How AI Can Be Applied to InsurTech

March 17, 2016     By : Sofia

AI has recently been one of the most debatable topics as the community of professionals across industries have mixed feelings on the outcomes of AI for humans. Nonetheless, there are very successful AI companies applying the power of machines and there are investors supporting them.

AI is most often associated with wealth management as it seems to be the segment where AI could make the most “damage” and cause the strongest disruption. However, AI finds its way into other industries, in particular, InsurTech.

One of the ways AI could foster the development of InsurTech is data capture and storage. AI can significantly ease the collection of valuable information about the customer and details of his/her current policy, financial information at some level and personal information from customer’s accounts across the Web. It would allow creating a holistic image of the customer to make the most optimal offer. In case AI is unleashed to scrape data about the person from all available sources, it may significantly reduce the assessment time, cut the overhead that does the same work manually and, hence, reduce the costs.

The next step where AI can pitch in is collecting data analysis and policy tailoring. Given the rich data about the person that an AI algorithm can collect, it can also tailor the best policy offer to meet anticipated needs. The interaction with the customer doesn’t end with a sold policy. It is only the beginning of the relationship between insurance providers and customers. AI can take under control and streamline those relationships by reminding about the renewal, notification on policy modifications that could be of interest. Moreover, AI can assess whether current policies are the best ones customers can get and constantly adjust the offerings.

Relationships management and constant assessment of the quality of provided services can result in processing additional requests from the customer in case the person is interested in complementary services or extension of the policy. In case a better offering has been selected and required, automated systems can renew it without the overhead and inconvenience for the customer. Marketplace platforms powered by AI would be able to assess the latest offerings and always keep clients up to date on the availability of a better policy.

Just like in wealth management, AI can be applied to advice regarding insurance policies. Advising and optimization can be done more efficiently with the help of AI that monitors the market and processes all data about the client base and policies held. Given the strict regulatory environment in the insurance industry, AI can keep companies up to date on the latest regulatory implementations and “advise” insurance companies on policy development and pricing models.

Given the power of knowledge about the customer that AIs would have, intelligent engine would be able to effectively bundle services and potentially boost the sales. It is more of an advantage for companies than customers, but still a positive effect on the industry overall. A deep understanding of the client’s needs would allow AI to make the best additional offers and increase the chances of a sale. Loyalty schemes could see a second birth with sophisticated machines determining the best complementary product to the general policy relying on available data about the client.

In case AI would be able to predict the environmental catastrophes, it can help insurance companies in high-risk locations to adjust the policies a potentially run from significant expenses in unforeseen situations.

AI has a potential to prevent expenses on claims over staged accidents based on historical data. It is not rare when insurance companies get ripped off over staged accidents and fraudulent claims. Given access to retrospective data, AI machines could be able to give an estimation of the possibility that some cases are staged to receive insurance money. Comparison of a large pool of claims in dataset available to AI can unleash the algorithm to determine whether the situation involving particular customer could be staged.

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Sofia

Sofia is a contributing writer for LTP based in New York. She is a market research professional skilled in data analysis and visualization. Sofia has an extensive experience in consumer behavior studies and marketing analytics. She is passionate about disruptive startups with innovative business models that are having a powerful impact on the industries.

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