Embracing the Future: Exploring Gen AI in Underwriting

In today's evolving insurance landscape, the integration of generative AI in underwriting is one huge key to staying ahead. Here are a few considerations as you assess solutions:

🎯 1. Determine Your Priorities

‍Underwriting is complex, and each step of the process requires different types of generative solutions. Gen AI can greatly improve the intake, classification, summarization, and policy binding suggestions for risks. In the early days, we’ve seen most underwriters prioritize two key use-cases:

1) Fix the “front door” problem by enhancing the triaging and routing of submissions and

2) Summarize and classify submissions according to the insurer’s custom risk appetite.

🔮 2. This Isn’t Your Grandparents’ AI

The rules of generative AI are different from the previous generations of AI. Prior efforts at AI in underwriting have required mountains of training data to even get started. That’s not the case with Gen AI underwriting and you can start seeing impacts very quickly. As important, gen AI underwriting isn’t the dreaded “black box” that insurers and regulators fear. Instead, Gen AI underwriting models can “show their work” and explain all the steps they take to assist an underwriter.

🧠 3. Understand the Models

Navigating generative AI for underwriting requires a comprehensive understanding of the different LLMs and vector databases in the market. LLMs are not all equal. Each LLM has its own strengths and weaknesses. Some are better at summarization, some at reasoning, and others are better at helping an insurer to find its voice. And some still require special care to control hallucinations. Understanding the differences between models helps you select the most suitable LLM (or LLMs) for your specific underwriting needs. For example, we deploy 5 different fit-for-purpose LLMs for a variety of gen AI underwriting.

⚖️ 4. Start With Compliance at the Table

Compliance is paramount in the evolving world of generative AI. It is crucial to ensure your compliance and legal teams have a seat at the table. When set up optimally, generative AI can help streamline compliance procedures and address legal concerns. Emphasizing the shift from bias to traceability is vital as regulators increasingly stress the importance of transparent and accountable AI systems, ensuring that organizations remain ethically and legally sound in their AI-driven endeavors.

💡 5. Built-for-Purpose Tech > ChatGPT

While publicly available gen AI models like ChatGPT and Bard have their place in the consumer world, they can’t be used at an insurance carrier. The regulated nature of insurance coupled with data privacy concerns means you must use purpose-built technology for underwriting. With each insurance carrier possessing a distinct risk appetite and set of guidelines, built-for-purpose generative AI incorporates (and keeps secret!) your underwriting protocols.
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Sixfold is the first generative AI created exclusively for insurance underwriters. Our purpose-built AI increases an insurance carrier’s capacity, accuracy, and compliance. The Sixfold AI intelligently ingests, routes, classifies, and summarizes submissions, providing underwriters with trustworthy, data-driven policy recommendations in a user-friendly format. Sixfold provides full AI traceability, ensuring your underwriting processes adhere to your underwriting manual and regulations, enhancing trust and reliability in every decision.

This article was originally posted on LinkedIn