The Rise of Natural Language Processing Tools in Group Insurance
It’s in the news and lurking on your computer: the natural language processing tools revolution driven by AI technology is here. It goes by many names – ChatGPT, Bard AI, Chatsonic and Bing AI to name a few – and whether you find it revolutionary or a bit on the scary side, these interfaces are entering the business enterprise. From enhancing internal operations to leveraging private data sources, the short-term and long-term impact of these tools on the group insurance industry are many. Let’s look at the exciting possibilities that lie ahead.
Short-Term Impact: Internal Applications
The short-term impact of language models would be most prominent in internal applications within organizations. While the unrestricted use of ChatGPT interfaces with external customers may still pose challenges due to concerns regarding bias and factuality, internal users can benefit from these tools. For example, employees processing claims can leverage ChatGPT interfaces to access frequently asked questions or seek clarifications, thereby reducing reliance on massive manuals. Moreover, language models excel at understanding and extracting information from documents, bridging the gap between handwritten notes and automated data processing.
Long-Term Impact: Risk Assessment and Sentiment Analysis
Whether onboarding new policies or analyzing disability claims, organizations can leverage these models to scrape and analyze vast amounts of data, including social media feeds. By tuning the guardrails around privacy concerns and ethics, language models can provide valuable insights into membership sentiment and identify potential risks.
Safeguarding Private Data
Addressing the vital issue of privacy and data protection is crucial and it’s important to differentiate between training data and transactional data. Training data shapes the language model’s parameters and is not directly linked to private information. On the other hand, transactional data, which may contain personal information, is used within specific conversations, and does not persist within the model. This separation ensures that private data remains confidential and is not disseminated unintentionally.
The Future of Practical Applications
It is quite clear that these tools are set to transform customer service operations. With ongoing advancements in algorithms and improved efficiency, language models like ChatGPT have the potential to become the first line of customer interaction. Their impressive ability to provide high-quality responses and the integration of emotion-sensitive AI technology make them ideal for real-time customer interactions. The collaboration between various AI components, such as language understanding and speech recognition, will further enhance these capabilities.
Collaboration between Humans and AI
Another significant area of future development lies in the collaboration between humans and language models. Humans and AI will work together to develop content and enhance productivity. Industries such as insurance, which require precise and compliant language, can leverage language models to streamline their processes and accelerate product development.
From improving internal operations to analyzing sentiment and managing risk, natural language processing tools offer exciting possibilities for the group insurance industry. As technology advances and ethical considerations are addressed, we can anticipate language models playing a significant role in customer service and collaborative content development. While the “the rise of the machines” scenario remains a distant possibility, the growth and integration of language models will undoubtedly reshape the insurance landscape.
To learn more about contextual AI and its potential future applications in benefits administration, listen to our podcast episode here: The Enterprise Impact of Contextual Artificial Intelligence