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Artificial intelligence (AI), specifically generative AI models, in the corporate sector and insurance industry, is experiencing a dynamic evolution, both individually and collectively, to promote new opportunities for growth and differentiation in a competitive marketplace. The integration of AI has become a central force for promoting efficiency and accuracy across industries. Through the utilization of AI technologies, companies can optimize overall performance in various areas including customer service, sales, fraud detection, risk assessment and claims processing.
The increased utilization of AI will improve efficiencies in routine tasks, but it does not eliminate the need for human oversight. Human oversight is required to ensure ethical, legal and operational integrity within insurance operations. Human judgment is critical to ensure the integrity, fairness, and reliability of insurance operations in an AI-driven environment. By fostering collaboration between humans and AI systems, insurers can harness the strengths of both to achieve optimal outcomes while upholding ethical and regulatory standards.
AI algorithms are not infallible; they may exhibit biases or errors, particularly when trained on biased data or subjected to unforeseen circumstances such as system interactions, whereby the AI may encounter new patterns or types of data that were not present in its training datasets. Human oversight is required to identify and correct biases, ensuring that AI-driven decisions are fair, transparent, and accurate. Human experts can review AI-generated outputs, validate decision-making processes, and intervene when necessary to rectify inaccuracies or prevent harmful outcomes.
Insurance companies will need significant safeguards in place to successfully and legally implement AI. This technology has made substantial strides in recent years but is still far from a perfect and fully reliable system. Insurance companies implementing AI will need to provide specific prompts and commands to effectively review large amounts of data and produce accurate results based on the analysis. Human judgment is further required in handling complex or ambiguous cases. Experts will bring domain expertise, contextual understanding, and critical thinking skills to decision-making processes, complementing AI-driven analyses and ensuring comprehensive risk assessments. Insurance companies will need to hire a team of data analysts, AI engineers, and attorneys to collaborate and develop a system of review and safeguards to prevent the inclusion of false or misleading information into policies, documents and claims decisions. Secure safeguards will reduce insurance companies’ exposure to litigation.
AI processes data on a much greater scale than humans but requires large sets of data to adequately analyze and deliver the best results in the task it is being asked to complete. Insurance companies will need to continuously provide the AI systems with new reliable and accurate data to allow the model to continue to develop. The source of the data poses an issue because companies will need to locate secure, reliable sources. Companies could collect data from their own insureds but must first obtain consent. If their internal data is insufficient, then companies may turn to third parties, which similarly results in significant risks of reliability and accuracy. Insurance companies will likely need teams of data analysts to review and vet the data sources. Furthermore, analysts will need to work with AI engineers to ensure the model is receiving reliable data to prevent false or misleading outcomes that could lead to litigation.
AI risks exposing insurance companies to various types of legal complications without proper oversight and review. Accordingly, companies must retain skilled attorneys who specialize in AI law to counsel on the implementation of mitigation processes. The attorneys must be familiar with the functionality and utility of AI as it relates to internal systems to provide the best defense possible. Legal compliance is paramount in the insurance industry, where regulations govern various aspects of operations, including data privacy, consumer protection and anti-discrimination laws. Human oversight is essential to ensure that AI systems comply with legal requirements and industry standards. Experts can interpret and apply regulations to AI algorithms, mitigate legal risks, and address regulatory concerns effectively.
The increased use of AI by insurance companies will inevitably lead to litigation, and in turn, regulation. As AI becomes more commonly utilized in the claims process, there is a greater risk of exposure to lawsuits alleging causes of action resulting from wrongfully denied claims or wrongful termination of coverage. An example includes an AI operating system using fraudulent or incomplete data to make coverage decisions leading to erroneous claims decisions against the insured.
Insurance companies are already submitted to a significant amount of governmental regulation. The incorporation of AI in the insurance sector will continue this trend as government agencies and legislators will need to create additional regulations to protect consumers.
Sarah La Pearl is an associate at Segal McCambridge in the firm’s Chicago office. She focuses her practice on complex commercial litigation. Evan Trevino is an associate at the firm who concentrates on first-party insurance defense litigation.
