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Use case – Insurance Industry | (NER-LLM) | Ruby

Insurance Industry

Ruby (NER+LLM)

Insurance companies can leverage NLP tools to automate many claim processes, speeds up decision-making, reduce costs and errors, and improve customer satisfaction

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Process documents to extract and analyze

We explain some examples of how LLM models can be used to enhance processes for customers.

  • Fraud detection
  • Document control


RUBY can automatically do this for you

Even more interestingly, it provides support for multiple languages.

With RUBY you can process your physical documents such as claims, invoices, contracts, medical reports, using AI and extract the relevant information intelligently and quickly.

Fraud detection

Unfortunately, some claims may be fraudulent and should be detected early

Manually reviewing emails, claims, documents and related forms for fraud is a complex task and can generate a high margin of error. Fortunately, this process can also be automated with NLP models.

These models are trained on applications that have been labeled as fraudulent or not. They then learn to identify applications with similar characteristics and assign scores to them based on the likelihood that they are fraud.
Applications deemed suspicious are reviewed by a final decision maker.









Document Control

Unfortunately, some claims may be fraudulent and should be detected early

Employees often have to analyze a large amount of data such as policies and other documents to assess the risks of insuring a client. In this case, speed and accuracy are highly important.

This process is often manual, slow and error-prone because of the large amount of data involved.

NLP models can extract key information such as names, dates, quantities, values, diagnoses and treatments from a claim management record or any required document.
Information that would otherwise have taken hours to find is now easily extracted.