Today’s insurers are no stranger to process automation and AI technologies. In fact, for the past decade, insurance leaders have been implementing these technologies as part of digitization strategies and a broader goal of staying in line with and, ideally, ahead of, the demands of both the market at large and customers.
So, what makes natural language processing (NLP) and large language models (LLMs) different from previous technology stacks and what can they achieve that previously couldn’t be done?
It’s their ability to transform language in different forms of text (forms, reports, transcripts, etc.) into data.
The Importance of Insurance Language Data
The effectiveness of LLMs and NLP rests on the fact that language is a form of data, and within insurance, language is very specific and ubiquitous. Whether it is engaging with customers on the front end or how they respond to a claim on the back end, insurance is driven by the analysis of language data.
By using technology to understand and action insurance language data, insurers can improve their performance and impact their current ratios in a meaningful way.
LLM and NLP technology, like that offered by expert.ai, can provide insurers with a strong, consistent foundation to ensure there is a match on the front end and back end, which means analyzing ALL the available language data to assess risk or matching a claim with what is covered.
As an example of expert.ai in action, if an individual is injured on a job site and the company has workers’ compensation, massive amounts of very specific data is needed to correctly assess the claim, such as employment history, medical notes, incident reports, notes from legal counsel and more. Expert.ai can automate the processing and provide consistent assessment of the insurance language data to identify the parts that require the insurance company to dive deeper into and, when necessary, include a “human in the loop.”
The question is, are insurers aware of the potential and considerations with LLM and NLP?
And, what is the right strategy for their adoption?
Walt Mayo, CEO of expert.ai, answers these questions and more in this interview with FinTech Global.
Three major takeaways include:
- Set accuracy goals appropriately: The volume, complexity and consistency of AI needs to be balanced against the perceived manual performance, then realistically applied.
- Language AI software is probabilistic: Software to date has been mostly deterministic, in other words, we know what it would return. NLP and LLMs return a range of outcomes.
- Focus on Return on AI (ROAI): Successful adoption should NOT be about solving the most complex problems initially, but first focus where AI provides the most value.
Read the full article: Understanding the Potential of LLMs and NLP in Insurance
