AI is playing a growing role in the way insurers do business, but it’s clear that improvements are needed.
What seemed like nothing more than science fiction a few years ago is now becoming a commonly accepted technology for the near future of the insurance industry as artificial intelligence (AI) rapidly develops. That said, as has been made more than clear in recent days with the goofs and flubs made by Google and Microsoft chatbots, it still has a lot of kinks to be worked out.
Insurers have been increasingly investing in AI technology to work into their everyday offerings.
There are two primary drivers of the use of AI technology by the insurance industry. The first is the explosion of insuretech companies and the second is the pandemic. That said, as much as insurers are certainly jumping on the artificial intelligence bandwagon, it’s clear that it’s not quite ready for a full roll-out, particularly when it comes to more complex communications.
Instead, AI is being developed with more of a supporting role in mind, such as in identifying potential customers, enhancing the overall claims experience, calculating premiums with a more accurate reflection of an individual consumer’s risk and needs, and others.
The insurance industry has recognized that the technology is only as good as what the AI has been taught.
By bringing AI together with stale data, insurers risk pouring their investments that will not only fail to live up to its potential, but that could actually work against them. For instance, old actuarial data would magnify biases that would unintentionally place certain groups of people at a notable disadvantage.
Therefore, researchers and tech experts are recommending that insurers focus on providing their AI with highly recent, relevant marketing data, which can be thoughtfully integrated into the tech’s processes to reduce biases currently woven into many of the insurance industry’s existing algorithms. Failure to do this, experts warn, runs the risk of creating a foundation that only builds on existing biases and will work against the companies and their customers alike. Moreover, that is a problem that risks compounding over time, the longer it runs on the same flawed data.
