Morningstar DBRS: Insurers’ growing AI adoption brings efficiency gains but heightened risks

By Mia MacGregor

July 15 - (The Insurer) - Insurers are increasingly investing in artificial intelligence to drive growth and profitability, but the shift comes with significant operational and regulatory risks, according to a new report by credit ratings agency Morningstar DBRS.

The report, Insurance Companies Increasingly Look to AI for Growth and Profitability, highlights that AI technologies, including machine learning and predictive analytics, are becoming essential for maintaining competitiveness.

North American insurers are projected to increase the percentage of their IT budgets that are investments in AI from 8% in 2024 to over 20% within three to five years, according to a Wipro Limited survey cited in the report.

Morningstar noted that AI offers numerous benefits across the insurance value chain, improving operational efficiency, customer experience and core functions like underwriting and claims management. AI tools can handle repetitive tasks, streamline customer interactions and reduce customer acquisition costs, leading to reported cost savings.

AI-powered chatbots and virtual assistants simplify the insurance purchasing process, although Morningstar warned that they can face challenges in handling highly customized tasks such as claims settlements.

In property and casualty insurance, AI can assess damages and estimate repair costs using digital images, and it can quickly evaluate exposure to natural disasters.

Fraud detection is another area where AI adds value, helping to identify potentially fraudulent claims and expedite legitimate ones.

However, the report warns that AI adoption carries operational risks, including financial and reputational damage if not properly managed. One of the most serious challenges, according to Morningstar, arises when AI is used extensively in underwriting and pricing, as these decisions directly affect 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),” the report stated.

AI models in claims processing are also vulnerable to costly errors and biases, potentially exposing insurers to regulatory fines and legal challenges. The use of vast data sets to price risk more accurately increases insurers’ exposure to cyber risk, the report noted.

The evolving regulatory landscape adds further complexity, particularly for smaller insurers with limited resources and data policies.

“Ultimately companies need to invest in AI to stay competitive; however, at the same time, they must not lose sight of the importance of having commensurate risk management frameworks,” the report said.