Three out of four U.S. insurers are already incorporating Generative AI (Gen AI) into at least one aspect of their operations, particularly in claims processing and customer service, according to a Deloitte survey of 200 insurance executives. Despite the growing adoption of Gen AI, scaling its use across the industry is challenging. Issues like data security, privacy concerns, and integration complexities remain significant obstacles for insurers seeking to expand AI applications within their organizations.

The potential benefits of Gen AI are vast, but so are the risks. Compliance challenges and operational vulnerabilities must be carefully navigated to fully harness AI’s advantages without compromising profitability or governance.

Transforming Operations with Gen AI in Insurance

According to the Capgemini Research Institute's 2025 report, 67% of leading insurers are prepared to leverage Generative AI to enhance policyholder experiences and optimize operations.

Generative AI stands apart from traditional AI by creating new data and content rather than simply analyzing existing data or automating predefined tasks. Here are some tangible examples of its impact:

Automating Policy Document Creation and Improving Claims Efficiency

One of the most effective applications of Gen AI in insurance is automating the generation of policy documents. By inputting customer-specific data, AI can create tailored policy documents that comply with regulatory requirements and meet customer needs, significantly reducing time and manual effort.

Gen AI can also collaborate with large language models insurance professionals, including underwriters, actuaries, claims adjusters, and engineers, to enhance productivity and efficiency. The technology can synthesize large volumes of data from the claims lifecycle—such as call transcripts, notes, and legal or medical documents—helping to speed up claims processing and improve accuracy, which is particularly beneficial in property and casualty insurance.

Synthetic Data for Model Training

Generative AI is also disrupting the industry by enabling insurers to simulate various risk scenarios using historical data. This approach allows for the creation of realistic datasets that mimic actual customer data, offering a valuable tool for training machine learning models in areas like fraud detection and risk evaluation.

By leveraging past customer data, Gen AI can generate simulations of potential future risks, which are then used to fine-tune predictive large language models insurance. This method allows for more accurate premium pricing and better risk assessments, providing insurers with a competitive edge in managing risk.

Personalized Marketing Content

Insurers are also using Gen AI to generate marketing content that resonates with individual customers. By analyzing customer data and preferences, Gen AI can quickly produce personalized brochures, blog posts, social media content, and emails, which enhances engagement and improves conversion rates. For many marketing teams, Gen AI is already a key tool, and expanding its role in automation is a natural next step.

Moreover, Gen AI assists in direct customer communication, such as drafting service emails, policy updates, and automated responses. This ensures timely and relevant interactions throughout the customer journey, contributing to higher satisfaction and loyalty. However, it’s still recommended that a human review the content before finalizing.

Improved Customer Interactions

Several insurers have integrated Gen AI into their customer service systems to facilitate more intuitive and context-aware conversations. By analyzing previous interactions and policy details, Gen AI can provide highly personalized responses to customer inquiries. For instance, when a policyholder asks about their claim status or coverage, the AI delivers precise information, reducing the need for human intervention and significantly improving response times.

These examples highlight how Gen AI in insurance is not merely an extension of traditional AI, but a catalyst for innovation that enhances operational efficiency and customer experience.

Challenges and Risks with Generative AI

Generative AI, as its name implies, creates new content by learning from data inputs, enabling the AI to produce text, reports, and insights that mimic human-generated output. While this capability enhances customer experience and operational optimization, it also introduces complex risks and compliance challenges that require expert oversight.

Model Hallucinations and Decision Integrity

One potential risk with Gen AI is the occurrence of “hallucinations,” where the model generates outputs that seem plausible but are factually incorrect. In the context of large language models insurance, such inaccuracies could lead to flawed risk assessments, mispriced policies, and incorrect claims decisions, compromising the integrity of underwriting and claims processes.

Despite this, Gen AI remains a valuable asset for insurers, enhancing efficiency by quickly generating drafts for numerous clients or new coverage options. The technology ensures consistency through standardized language across policies, which helps reduce errors. To mitigate the risks of hallucinations, insurers are putting safety measures in place, such as validation checks, and maintaining human oversight to ensure that AI-generated content is accurate and reliable.

Adversarial Attacks and System Vulnerabilities

Generative AI systems can be susceptible to adversarial attacks, where malicious actors manipulate inputs to deceive the AI into making erroneous decisions. In insurance, this could lead to fraudulent claims being approved or altered risk assessments.

To counter these risks, insurers must adopt strong security protocols, including data encryption, secure model training procedures, adversarial testing, and regular audits. Robust authentication methods and continuous monitoring of AI systems for abnormal behavior are also critical for ensuring the security of AI applications in the industry.