Insurance industry using NLP and AI/ML to better understand customers and claims.

Four Ways Insurers can Leverage AI Powered Text Analytics

Thematic participated in the OnRamp Insurance Conference in snowy Minneapolis last month. The event brought together some of nations premier insurers, investors and startups to discuss innovation occurring in fin-tech and insure-tech. Many of the insurance providers have even established corporate venture capital arms to invest in the technology and service companies that enhance insurance buying, underwriting, and claims.

During the event, Thematic met with many insurers who were ready to use AI/ML to help enhance all levels of their business. Our conversations with insurers triggered ideas for Thematic to collaborate with the insurance industry. Below are four applications of AI and NLP for the insurance industry to learn more about their customers and business.

1) Identifying themes across claims data

Insurance companies have a wealth of information collected by agents who take notes during claims calls. When we met with a consultant focused on the insurance industry, she spoke at length about using AI to scrape through the claims notes to uncover trends in the records and identify recurring themes.

One example that she laid out was for the insurance companies to detect patterns in potentially fraudulent claims and discover topics that only exist in those claims to help prevent further losses.

2) Extract themes and insights from call center transcripts and chat logs

Agents in call centers often handle calls and chats with clients that can quickly be answered using online help desks. Using AI to tag conversations with themes, Support Teams can digest what are the most frequently asked questions based on recent chat logs to create new automated responses for these requests.

Another idea proposed by a member of the corporate venture capital arm of a Midwestern insurance company is to use these uncovered themes to create social posts and short videos when an issue is affecting many users.

Ultimately, the themes uncovered in these logs will allow you to create better support materials, automated chat flows and scripts for new agents.

3) Help sales management identify trends in CRM notes

Sales teams take copious notes and enter them into a CRM. However, sales management usually ignore these notes unless team members are underperforming.

When speaking with the SVP in Data Science from one of the five largest global insurers, he expressed a critical need for tapping into the massive value of notes from their CRM as a top priority.

Another member of the SVP’s team revealed that revenue was down during the last two quarters because of lower renewals – an issue that front line sales people identified as a risk months before the actual policy renewals came up.

By using text analytics to find trends in the sales notes, insurers would be able to uncover unknown risks as well as opportunities that allow them to anticipate changes to their forecasts.

4) Understand why agents are no longer selling your products

For many insurers, independent agents who once sold their products may stop getting quotes from their company due to changes to the insurers’ products. However, there are other reasons why that may be unknown to the insurer. One insurer could see they were not quoted but did not have the insights to know why unless they called or met with that agent in person.

By sending out a survey to these agents to better understand why they stopped quoting will help bring clarity to how the insurer can resolve any issues and continue writing insurance.

Are there other ways that you think insurance companies can leverage AI and NLP? Leave a comment below and let us know!

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