The use of generative AI in insurance is a powerful tool for increasing the efficiency and accuracy of key functions, ultimately driving improvements in operational efficiency and the customer experience. Here, we highlight those use cases and examples, and explore the functionality driving these results.
The power of AI and generative AI in insurance is no longer up for debate. And as insurers seek to tap into this power, it's essential to understand the technology driving these results.
AI, the simulation of human intelligence processed by machines, is capable of performing tasks such as problem-solving, learning, and decision-making. It analyzes datasets to extract insights, detect patterns, and make predictions quickly and accurately. With a standard machine learning approach, AI models are built to solve one particular type of problem at a time. For example, reviewing claims for common indicators of fraud.
Generative AI is a branch of AI that is capable of generating new and original information. Models are trained on vast generic datasets, and through the use of dedicated prompts, they can be leveraged to solve a wide variety of challenges. For example, summarizing a complex insurance policy or summarizing claims notes.
The power of generative AI is derived from large language models (LLMs). LLMs have the ability to read and analyze text, particularly natural language text, in massive quantities. This enables a single model to process and analyze virtually any kind of text it can access, and pull from billions of data sources to solve a variety of complex problems.
Because of its ability to process and synthesize vast amounts of structured and unstructured data, generative AI has made it possible to leverage that information at scale, with both speed and accuracy. For example, imagine a claims handler combing through documents in order to extract specific pieces of information related to the case. With generative AI, this same claims handler could ask for a targeted summary of all documents related to the case, and focus on the important decision of how to proceed with the claim.
During a recent webinar, Shift's own experts delved into the practical applications of generative AI in insurance, highlighting its potential to streamline processes, enhance accuracy, and drive efficiency. Here, we examine three common use cases, and dig into the way generative AI works to drive those results.
By leveraging generative AI for functions such as document classification, key information extraction, and claims situation assessment, insurers have been able to achieve impressive accuracy rates of 95-99%. This not only speeds up the claims process but can significantly increase the automation rate, allowing insurers to handle claims more quickly and accurately. At scale, this can have a major impact on efficiency and customer satisfaction.
A US-based travel insurance company was manually handling around 400,000 claims per year, with each claim taking anywhere from about ten days to three weeks to process. By partnering with Shift, they were able to leverage generative AI in order to achieve 57% automation with 98% accuracy on pay decisions, as well as reduce processing time from three weeks to two minutes. Watch or read the full case study here.
Generative AI is also being used to automate liability assessments. By summarizing loss situations and factoring in details from documents and applicable laws, AI can achieve a 90%+ accuracy rate in auto claims and 95% in property claims. This can result in a 30% increase in subrogation referrals acceptance, significantly enhancing the efficiency of liability determination processes.
Recently, we assisted a leading US P&C insurer by identifying a subrogation opportunity in New York, a state where recovering on personal injury protection (PIP) claims is notably challenging due to stringent legal criteria. These criteria include the requirement for one of the involved vehicles to be a commercial livery vehicle or exceed 6,500 lbs. in weight, which is difficult to verify at scale. By leveraging generative AI, we efficiently analyzed third-party vehicle data against external sources, identifying the vehicle as a registered ride-share, thus meeting the subrogation criteria. The AI models further confirmed the ride-share vehicle's fault in the accident, enabling a swift and accurate alert to the insurer. Watch or read the full case study here.
In the realm of fraud detection, generative AI excels at identifying inconsistencies between documents and actual loss details. With a 93% accuracy rate in detecting inconsistencies, generative AI helps insurers accurately uncover suspicious claims.
In photos submitted as part of a storm damage claim, generative AI can detect whether or not the damage is in fact storm related, or just a result of normal wear and tear. In the clip below, Shift Chief Data Scientist Arnaud Grapinet discusses this example, as well as the application of the same technology to distinguish between water damage and mold. Play the clip below to learn more, or watch the full webinar here.
To learn more about the use cases, examples, and specific technology behind generative AI in insurance, watch our on demand webinar: Beyond the Hype: Real Examples Showing How GenAI is Already Benefiting Insurers
As the value of generative AI in insurance has become widely accepted, insurers face an unprecedented amount of information. And to meet the growing interest and demand, these solutions have become more and more prevalent. But not all generative AI is created equally, particularly when it comes to the insurance market.
While technical debt comes from a variety of practices, implementing the wrong generative AI solution can have a significant impact. Many of the available solutions prioritize rapid deployment over long-term benefits, or provide a one-off solution to a broader opportunity, which are both factors that can contribute to technical debt. This often results in a vicious cycle, in which complexity builds, and the costs continue to grow. Additionally, there's a significant opportunity cost to bypassing solutions that bring the full value of generative AI to insurance.
LLMs have proven extremely effective at extracting information and categorizing generic documents; however, they do not often display high performance in insurance-specific use cases. These documents contain information often critical to contextualization and accuracy, and are essential to the performance of any generative AI solution. Best-in-class, insurance-grade solutions are built and trained specifically on these types of industry-specific documents.
Insurers should prioritize generative AI partners that understand the complexity and nuance of insurance operations, regulatory compliance, and data privacy. Unlike general-purpose AI providers, a partner with deep insurance expertise can build solutions that align with real-world claims, underwriting, and fraud scenarios. Industry-specific knowledge is essential for grounding generative AI in accurate, compliant outputs. This focus enables insurers to deploy AI that enhances trust, reduces operational risk, and adds measurable value.
Generative AI must be deployed with strong ethical guardrails and governance. Insurers should seek partners who are committed to responsible AI development—including transparency, data privacy, and the ability to explain model behavior. Importantly, partners should design AI that augments—not replaces—human expertise. “Human in the loop” workflows ensure outputs are validated and used appropriately, especially in high-stakes domains like claims adjudication or customer communications. This responsible approach mitigates risk and builds confidence in AI adoption across the enterprise.
Generative AI solutions must be built for scale, security, and enterprise integration. Insurers operate in a highly regulated environment, and any AI partner must be able to meet stringent standards for data handling, cybersecurity, and compliance. Successful partnerships also depend on the ability to integrate AI into core systems and workflows—delivering value without disrupting existing infrastructure. A capable partner will offer scalable APIs, customizable interfaces, and secure data pipelines that support real-time decision-making across the insurance value chain.
To learn how Shift can help you leverage generative AI to deliver measurable impact and exceptional customer experiences, contact us or schedule a demo.