Insurers need to know with certainty that their AI models aren’t turning prospective policyholders away based on non-relevant socioeconomic or demographic factors. (Photo: Alex/Adobe Stock) Insurers need to know with certainty that their AI models aren’t turning prospective policyholders away based on non-relevant socioeconomic or demographic factors. (Photo: Alex/Adobe Stock)

Adoption of AI has exploded in recent years as organizations recognize the power of machine learning (ML) models in delivering greater business insights and fueling competitive advantage. When applied and monitored properly, AI has proven to drive real, tangible value — particularly in the insurance industry, where AI is being used to support risk assessment and fraud detection. In fact, PwC’s survey of U.S. insurers found that nearly half said AI has improved decision-making. Additionally, almost two-thirds of respondents using AI reported that it helped create better customer experiences.

Unfortunately, some insurers are still facing obstacles when attempting to leverage AI models that will enhance their business operations and bottom lines. The same PwC survey found that potential new cybersecurity and privacy threats topped the list of AI worries, cited by 42% and 36% of survey respondents, respectively. As a result, risk and regulatory teams often hit the brakes on new initiatives.

 

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