Generative AI in Insurance: Use Cases, Potential Benefits, and Risks

A Brief History

In this article, we will discuss the potential use cases for Generative AI in insurance. But first, let’s take a quick look at where this recent phenomenon came from. Generative AI, a subset of artificial intelligence has recently exploded in popularity due to the virality of ChatGPT. It has shown incredible potential in various industries and is the fastest app to ever reach 100mm users. 

It should be noted, however, that this technology is not brand-new. Instances of Generative AI date back to the 1960s in the early days of chatbots. In 2014, with the introduction of generative adversarial networks, or GANs, Generative AI took a major leap forward. GANs are a type of machine learning algorithm that enables generative AI to create seemingly authentic images, videos, and audio. Two additional advancements made the recent explosion in popularity possible – transformers and the large language models they enabled. 

TechTarget does a great job explaining these advancements. “Transformers are a type of machine learning that made it possible for researchers to train ever-larger models without having to label all of the data in advance. New models could thus be trained on billions of pages of text, resulting in answers with more depth. In addition, transformers unlocked a new notion called attention that enabled models to track the connections between words across pages, chapters, and books rather than just in individual sentences. And not just words: Transformers could also use their ability to track connections to analyze code, proteins, chemicals, and DNA.” 

This led to what is better known today as Large Language Models, or LLMs. LLMs are the base of apps like ChatGPT. We’re still in the very early days of building out the app layer and the interfaces we will use in the future. While the generative AI investment bubble continues to inflate, fueling the next generation of applications built for end-users, we wanted to take a look at the potential impact on the insurance industry. 

The Promise of Generative AI in Insurance

Use cases for Generative AI in Insurance have been popping up more and more. Scroll on LinkedIn, attend a conference, or check your weekly insurance email newsletters, and you’ll be sure to see it pop up. Here’s an article from HannoverRe on potential insurance use cases for Generative AI. 

generative ai in insurance

In the context of Generative AI in insurance underwriting, experts say that it could potentially help with the following:

  • Automated Content Creation: Generative AI can potentially streamline the creation of complex insurance documents, policy agreements, and even marketing materials. By analyzing existing data, the technology might be able to generate coherent and tailored content, reducing manual work and improving turnaround times.
  • Personalized Policies: With the capacity to process large volumes of customer data, generative AI could create highly customized insurance policies that cater to individual needs. This level of personalization enhances customer satisfaction and engagement.
  • Risk Modeling and Scenario Planning: Generative AI can simulate numerous scenarios based on historical and real-time data, assisting insurers in developing more accurate risk models. This allows for proactive adjustments to underwriting strategies, enabling insurers to stay ahead of emerging risks.
  • Fraud Detection and Prevention: By analyzing patterns and detecting anomalies in large datasets, generative AI can enhance fraud detection and prevention. This technology can quickly spot irregularities in claims or behavior, helping insurers mitigate financial losses.
  • Customer Support and Interaction: Generative AI-driven chatbots and virtual assistants can provide instant responses to customer inquiries. This allows for 24/7 support and frees up human agents for more complex tasks.

Why Generative AI Benefits for Insurance Might Take Time

While Generative AI remains in vogue, our position is that it will take years for its widespread adoption in insurance. Here are some reasons behind the potential delay:

  • Data Availability and Quality: Generative AI requires large amounts of high-quality data to generate accurate insights. Many insurers are still in the process of digitizing their data and improving its quality. Additionally, Generative AI and  LLMs have a long way to go before the application layer is vertically specific. 
  • Complexity of Underwriting: Insurance underwriting involves intricate decision-making that considers a multitude of factors. Developing generative AI models that accurately replicate this decision-making process is a complex task.
  • Regulatory Challenges: The insurance industry is heavily regulated, and deploying generative AI models will require thorough testing and regulatory approval.
  • Complex Implementation: Integrating generative AI into existing insurance workflows can be intricate and resource-intensive. Proper training, data preparation, and system integration are necessary for effective implementation.
  • Ethical and Fairness Concerns: Generative AI models need to be carefully monitored to ensure they don’t inadvertently introduce biases or unfair practices into the underwriting process.

With that said, we believe that carriers will rightly focus on solutions that will have an immediate and long-lasting impact on profitability. Some will focus on AI in insurance underwriting, but we’re urging them to focus even more specifically by leveraging real-time behavioral intent data for a competitive underwriting edge in solving one of today’s biggest challenges: Premium Leakage and Nondisclosure