Artificial intelligence is becoming one of the most significant areas of uncertainty for insurers, creating opportunities for innovation while introducing new risks around governance, decision-making and operational control.
Insurance leaders are highlighting that AI has the potential to transform areas such as underwriting, claims management, customer service and business operations, but insurers must develop appropriate frameworks to manage its unknown risks.
AI adoption is increasing across the insurance sector as companies use advanced analytics and automation to improve efficiency, enhance customer experiences and make better-informed decisions.
However, the unpredictable nature of AI systems creates challenges for insurers. Unlike traditional models, AI-based systems may produce outcomes that are difficult to explain or anticipate, particularly when they rely on large and complex datasets.
One of the major concerns is model risk. AI models can generate inaccurate results due to poor-quality data, biased information, incorrect assumptions or changes in market conditions. These issues can directly affect underwriting decisions, pricing accuracy and claims outcomes.
Transparency and explainability are becoming increasingly important. Insurance decisions often influence customer access to coverage and claim settlements, making it essential for insurers to understand how AI systems arrive at conclusions.
Data governance is another critical area. AI systems require extensive data, increasing the importance of maintaining data accuracy, privacy protection, cybersecurity and responsible data usage practices.
The use of AI in underwriting provides significant opportunities by improving risk assessment and enabling faster decisions. However, insurers need appropriate controls to ensure automated decisions remain fair, consistent and compliant with regulatory expectations.
Claims management is another area where AI can deliver benefits through faster processing, fraud detection and improved customer service. At the same time, insurers must ensure that automation does not reduce human oversight in complex cases.
Third-party AI solutions also create additional risks. Insurers using external technology providers need effective vendor risk management frameworks to evaluate security controls, data handling practices and operational dependencies.
Regulatory expectations around AI governance are increasing globally. Insurers need clear accountability structures defining who is responsible for AI systems, how they are monitored and how potential failures are managed.
The role of insurance professionals will also evolve. AI will support decision-making, but human expertise will remain essential for interpreting results, applying judgement and managing complex risk situations.
The insurance industry’s challenge is not whether to adopt AI, but how to adopt it responsibly. Organisations that combine innovation with strong governance, model oversight and risk management will be better positioned to benefit from AI.
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