Underwriting and Claims Text Analytics

Using Amenity, a global insurance and investment company was able to create automated alerts of real-time events that impact its underwriting and claims processes.


Revenue generation based on pricing decisions and loss mitigation from not underwriting a company who later is hit by a huge lawsuit.


At the start of the decision-making process, underwriters are generally provided with only two main data sources: the client-provided information in the application form and previous claims led against the applicant’s insurance policy. From there, they may use Google search, social media, and online news sites to locate any red flags that would impact the final evaluation.

Because the research methodology is manual and ad-hoc, there is a strong likelihood for unknown risks in the portfolio.


The insurance company can use NLP technology to automate the risk monitoring process.

A vendor with the ability to customize and quickly execute their technology can build a platform that captures news from a wide array of cloud-based documents, whether it is news on private or public companies, SEC filings, or earnings call transcripts.

The platform then scans the content for the insurance company’s specific view of risk. If there is a news story about the applicant in the context of that risk, the underwriter can factor that information into his or her decision.

The build-out process may involve several stages in order to reach an automation process that is an improvement on the existing process. After the vendor creates the initial model, the insurance company will assess the data results to determine which areas need fine tuning. The vendor then adjusts the model. There may be several iterations before finally arriving on a custom solution that automates risk detection according to the specific way the insurance company views risk.


The underwriter can now log onto the platform dashboard, select the relevant company, and see the number of mentions around the associated risk topic over a period of time.

  • For example, the underwriter may see that a pharmaceutical client had 35 mentions around the risk topic cannabis over the past year.
  • Based on that information, they can decide whether to not underwrite the policy or adjust the pricing because of the risks associated with the account.

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