3 InsurTech AI Trends for 2024
Photo by Katie Shivers
A year has passed since the launch of ChatGPT, and many InsurTech start-ups are still exploring how to effectively leverage the new technology. Artificial intelligence (AI) has become the norm, and differentiation will come from how it is creatively applied to solve practical issues.
The recent Plug and Play Winter Summit in Sunnyvale, CA, fully displayed this phenomenon. Plug and Play connects start-ups with large corporations through industry-focused accelerator programs. The Winter Summit InsurTech line-up unveiled three key AI themes that we can expect to see progress over the coming year.
AI as a Co-Pilot
At the beginning of the AI frenzy, employees across all industries worried they would be replaced by new technology, and for good reason. Many corporations were openly considering implementing AI solutions that would decrease the need for human workers, thus cutting costs and increasing efficiency.
As InsurTech start-ups explore applying AI to the insurance agent workforce, they are shifting away from viewing AI as a potential replacement for agents, instead viewing AI as a tool to enhance and streamline the work that agents do. For example, using AI solutions to reduce processing time and help tackle highly complex claims.
It is not uncommon to hear InsurTech AI solutions being pitched as a “co-pilot” for agents, likely inspired by the similarly named Microsoft Copilot, an AI chatbot. Microsoft also recently made waves in the InsurTech industry by partnering with SCOR and ReMark to host a collaborative hackathon exploring generative AI and reinsurance.
As Pooja Shah, Senior Associate at Avanta Ventures, put it, “Ultimately, despite all of the hopes that we have for GenAI and AI in general, at the moment there still is a human involved. So, it’s not going to be adjuster vs. AI, it’s going to be adjuster with AI vs. just a normal adjuster.” Shah also noted that the key to reducing employee AI concerns is quality training that shows AI is not something to fear. Rather, it can be a valuable tool for agents that may give them an advantage over others in their field.
The current limitations of AI capabilities are not the only factor keeping humans involved. There are also emerging regulations such as the AI Act and ethical concerns aiming to prevent the elimination of our jobs.
Integrating with Legacy Systems
Throughout the InsurTech evolution, the community has come to learn the complexities of legacy systems. The overall mindset has shifted from attempting to replace these systems to integrating into them, and AI is no exception. AI start-ups have learned from the past and are focused on incorporating AI into established tech stacks.
It is imperative that AI tools are easy to integrate into existing systems and do not require a complete overhaul. No matter how valuable an AI solution may be, if it is deemed too complex to integrate with internal tech and use with ease, it will not be considered. For many organizations, the time and resource costs a system replacement requires outweigh the benefits a new system would offer.
There are also security advantages to integrating into legacy systems instead of trying to replace them. Companies may be hesitant to export all their data to an unfamiliar system, especially since regulations that govern AI data security are in their infancy. Instead, startups can provide value if their AI tools integrate into what already exists, allowing companies to keep their data private and build on their infrastructure without giving that data to someone else’s model.
Growing Interest in Multimodality
Large Language Models (LLMs) have been the foundation for many InsurTech start-ups. However, some consider multimodality the next leap forward for artificial intelligence solutions. While LLMs process text and perform language-related tasks, multimodal AI can intake many data types, including image, text, and speech, to understand behaviors and patterns in larger contexts.
When applied, LLMs can help underwriters understand long, complexly written site assessment reports and speed up administrative tasks by identifying what is important and answering specific questions. However, multimodal AI solutions go beyond just describing what happened to understanding why it happened and predicting future risks.
The concept of multimodal AI in the insurance space is still young and a bit nebulous, but founders are confident that it can help professionals understand behaviors and patterns of behavior in larger contexts, providing just as much or even more value than the LLMs we’ve seen.
"The word LLM - Large Language Models - is getting outdated,” said Dr. Ivan Poupyrev, CEO and founder of Archetype AI. “As we move into the physical world, there will be even more separation from the LLM because we will be adding time dimensions, spatial dimensions, and behavioral dimensions." If start-ups perfect the multimodal solutions they are working on, the insurance industry has much to gain.
As we kick off 2024, it is helpful to reflect on what we learned about AI in 2023 and consider the trends led by the start-up community with the potential to become industry norms.
Originally published on remarkgroup.com
Katie Shivers
Katie Shivers is a digital marketer located in Charlotte, NC. She is a multi-faceted writer, experienced project manager, and social media enthusiast. Katie loves craft beer, live music, and supporting local artists.
