How risk management shapes the ethical use of AI

Faster claims and pricing come with challenges in fairness and trust

How risk management shapes the ethical use of AI

Risk Management News

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Artificial intelligence (AI) is revolutionizing the insurance industry, offering faster processes and greater efficiency. However, its adoption comes with significant risk management challenges, particularly concerning discrimination and bias in underwriting.

In conversation with Insurance Business, Marc Voses, partner at Clyde & Co, provided key insights into how the industry is addressing these issues while navigating the ethical complexities of AI.

AI algorithms can inadvertently perpetuate biases embedded in historical data, leading to discriminatory outcomes in pricing, coverage decisions, and claims processing. According to Voses, this underscores the importance of insurers adopting stringent fairness criteria and ensuring transparency.

“AI systems can inadvertently perpetuate biases present in historical data, leading to unfair treatment of certain groups. This can manifest in discriminatory practices in pricing, coverage decisions, and claims processing,” Voses explained.

These challenges necessitate robust risk management strategies, including the implementation of regulatory frameworks.

As Voses noted: “Regulatory frameworks are being developed to address these issues,” pushing insurers to measure and ensure the ethical use of AI.

Finding the equilibrium between efficiency, accuracy, and ethical responsibility is central to AI’s integration.

“The balance between efficiency, accuracy, and ethics is a major focus for insurers and regulators,” Voses said. To mitigate bias, insurers employ diverse and representative datasets to train AI systems and emphasize human oversight.

Transparency also plays a pivotal role in risk management. Insurers must make their processes clear to regulators and consumers, explaining how decisions are made and what data is used. Regular audits and updates of AI models ensure they remain fair and accurate over time.

Gaining consumer trust

Consumer skepticism remains a barrier to AI adoption in insurance. To address this, Voses emphasized the need for insurers to build trust through transparency and accountability.

“AI can streamline insurance processes, but gaining consumer trust is key,” he said, noting that insurers should demonstrate how AI can deliver faster and more accurate outcomes.

“If a consumer gets an answer quickly and accurately, they’re less likely to question the process. But what happens when the response isn’t satisfactory? There needs to be a clear process for consumers to challenge decisions, with human oversight to address their concerns,” he said.

When issues arise, insurers must analyse and learn from them. “Was there a communication gap? Did the policyholder misunderstand the coverage? Or did the insurer make an error? Learning from these cases can improve the overall customer experience and build trust in AI,” Voses added.

Mitigating risks in AI

Strong regulatory frameworks are essential for managing AI risks and fostering consumer confidence. According to Voses, effective regulation should include independent bias audits, transparency requirements, and legal accountability.

“Regulation is essential for consumer trust and guiding insurers on ethical AI use,” he explained.

In the US, the National Association of Insurance Commissioners (NAIC) has introduced a model framework adopted by over 20 states. This framework focuses on transparency, accountability, and risk management.

“The NAIC framework provides clear guidance for insurers to use AI responsibly while protecting consumers,” Voses said, highlighting its emphasis on third-party vendor oversight and data privacy measures.

For AI to promote accessibility and fairness, insurers must focus on ethical frameworks and risk management practices. Voses suggested that designing AI systems to expand coverage options and improve affordability for diverse customer groups is critical.

“To do this responsibly, insurers should develop ethical AI frameworks with clear policies and risk management,” he said. “Streamlining processes and reducing frustration are key. What happens when AI leads to fewer delays or errors? Consumers start to see the value. If premiums decrease because overhead costs are reduced, that’s a direct benefit to the customer.”

“If someone can get insurance faster or appeal a decision and see that their input improved the process, it builds confidence,” Voses added.

AI offers tremendous potential for transforming insurance, but its success hinges on effective risk management. By addressing bias, ensuring transparency, and adhering to robust regulatory frameworks, insurers can navigate the ethical complexities of AI.

“When consumers know there are safeguards and accountability measures in place, they’ll feel more comfortable embracing AI,” he said.

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