Morningstar DBRS warns AI adoption in insurance packs big risks — not just rewards.
Insurers are pouring more of their IT budgets into AI, with North American firms raising AI spending from 8% in 2024 to over 20% in the next three to five years, per a Wipro survey.
The rise in AI use spans underwriting, claims processing, fraud detection, customer service chatbots, and loss assessment. AI streamlines repetitive tasks, boosts customer insights, and can speed claims by flagging complex cases for human review. 
But Morningstar DBRS stresses AI also brings big pitfalls. Faulty AI can misprice policies, mishandle claims, or reject valid ones—triggering legal, reputational, and financial damage.
Smaller insurers face bigger challenges, struggling with limited data and weaker risk management. Many lack clear data usage policies despite heavy AI reliance.
Morningstar DBRS issued this on AI risks and credit impact:
“We would also note that companies using AI assessments to reject claims could be exposed to legal and reputational risk if those AI models turn out to be unreliable.”
“By synthesizing granular data and improving existing risk models, AI can complement human expertise in underwriting and provide valuable insights for risk selection and pricing. This may ultimately help insurers write more policies with consistent pricing for similar risk profiles.
“On the other hand, since underwriting decisions have a direct impact on profitability, AI models need to be carefully selected, trained and tested as otherwise mispriced policies could result in very serious reputational and financial implications.”
“For example, determining marketing leads based on AI recommendations is generally a low-risk proposition. At other times, benefits and risks are closely related such as in terms of how AI impacts customer experience.”
“But, in our view one of the most serious challenges arises when AI is used extensively in underwriting and pricing of policies as those decisions are directly related to profitability.
“In those situations, the insurer could be subjected to various costly errors and biases (i.e., quoting unreasonably high/low premiums for characteristics that are not well represented in the data used in training AI models). Additionally, there could also be regulatory fines amid the evolving regulatory landscape. Equally concerning could be certain decisions related to claims processing.”
“At the same time, they must not lose sight of the importance of having commensurate risk management frameworks. From a credit rating perspective, AI can both enhance and damage franchise strength by affecting customer experience. Moreover, while it may improve profitability through efficiency gains, it generally also contributes to higher operational risks, including legal and compliance risk.”
Insurers betting big on AI need strong governance or risk credit rating hits. AI offers growth and profits but carries serious risk exposures that cannot be ignored.
