At the other end of the insurance process is the issue of claims. It is not only the insured who have problems with claims complexity. In the automotive industry, the need to understand the variety of repair options and parts available create a challenge for both service providers and the insurers.
Machine learning can help with claims in a number of ways. In addition, multiple ML tools can be used throughout the claims process. With other damage, or even to understand if there is a total loss, ML can be used. The most obvious tool is AI vision, but even this can have multiple processes. A phone app can step a customer through taking pictures that an AI system can then analyze for damage, with a backend AI system working to link to parts and estimate.
One thing Evan Davies also pointed out was how the process flow can change depending on the severity of accident or the type of insurance coverage provided. Minor damage and standard coverage can be fully automated, as all parties are fairly comfortable with the process and dollar amounts. Totals, as mentioned, don’t require AI.
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Machine Learning Can Help The Insurance Industry Throughout The Process LifecycleNot all insurance is alike. Life insurance and automobile insurance are not (as far as I know) the same thing. There are many similar processes, but data and numerous flows can be different. Machine learning (ML) is being applied to multiple aspects of insurance practice.
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Machine Learning Can Help The Insurance Industry Throughout The Process LifecycleNot all insurance is alike. Life insurance and automobile insurance are not (as far as I know) the same thing. There are many similar processes, but data and numerous flows can be different. Machine learning (ML) is being applied to multiple aspects of insurance practice. tokenizedtweets 0x12D0F38D06834D32f7d2BFB2Ee94F4926B4D1bB7 /14300
Источник: Forbes - 🏆 394. / 53 Прочитайте больше »
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Источник: Forbes - 🏆 394. / 53 Прочитайте больше »