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Insurance CIO Outlook | Tuesday, September 20, 2022
Many insurance companies collect and analyze massive amounts of data to predict losses better and optimize business operations.
FREMONT, CA: The use of AI and ML has the potential to significantly improve the underwriting process by providing access to information from previously unavailable sources. Any information that isn't already neatly organized into predefined fields may come from these sources. Although this unstructured data appears chaotic to a computer, it contains a wealth of information that underwriters can use to assess risks, classify businesses, and verify information provided in an application.
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Insights into underwriting sources
Insurers can learn more about an applicant's business without prying with inquiries or relying solely on the information they offer in an application. Computer vision algorithms illustrate this trend because they allow the software to recognize objects in photographs and examine images shared on social media to spot potential security risks. For instance, insurers use an automated system to identify potential risks throughout the underwriting process.
Data insights from existing sources
Many insurance companies collect and analyze massive amounts of data to predict losses better and optimize business operations. Compared to more conventional prediction methods, such as linear models, AI/ML techniques offer exciting new avenues for exploring and understanding this data. It is because AI/ML methods are particularly good at analyzing the interplay between various factors and drawing conclusions based on these analyses.
Increased uniformity
When compared to human underwriters, the outcomes given by AI/ML models can be more consistent because they are based on objective, quantified data. For instance, when presented with the same data multiple times, an AI/ML algorithm that applies classifications and recommends coverage alternatives will consistently reach the same conclusions. In contrast, a less-experienced human underwriter can draw a different judgment from the same set of circumstances as would a more-seasoned one.
Maximized effectiveness
Underwriters for small business insurance policies of today may need to manually check information, either by searching the internet or by calling agents and prospective policyholders to get it. The information is then evaluated by underwriters, who decide whether to approve or refuse the policy and set the premium. Every one of these procedures can automate with the correct information.
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